Global Highlights in Neuroengineering 2005-2018


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Global Highlights in Neuroengineering 2005-2018 – Logan Thrasher Collins

Optogenetic stimulation using ChR2

(Boyden, Zhang, Bamberg, Nagel, & Deisseroth, 2005)

  • Ed S. Boyden, Karl Deisseroth, and colleagues developed optogenetics, a revolutionary technique for stimulating neural activity.
  • Optogenetics involves engineering neurons to express light-gated ion channels. The first channel used for this purpose was ChR2 (a protein originally found in bacteria which responds to blue light). In this way, a neuron exposed to an appropriate wavelength of light will be stimulated.
  • Over time, optogenetics has gained a place as an essential experimental tool for neuroscientists across the world. It has been expanded upon and improved in numerous ways and has even allowed control of animal behavior via implanted fiber optics and other light sources. Optogenetics may eventually be used in the development of improved brain-computer interfaces.

Optogenetics

Blue Brain Project cortical column simulation

(Markram, 2006)

  • In the early stages of the Blue Brain Project, ~ 10,000 neurons were mapped in 2-week-old rat somatosensory neocortical columns with sufficient resolution to show rough spatial locations of the dendrites and synapses.
  • After constructing a virtual model, algorithmic adjustments refined the spatial connections between neurons to increase accuracy (over 10 million synapses).
  • The cortical column was emulated using the Blue Gene/L supercomputer and the emulation was highly accurate compared to experimental data.

Cortical Column

Optogenetic silencing using halorhodopsin

(Han & Boyden, 2007)

  • Ed Boyden continued developing optogenetic tools to manipulate neural activity. Along with Xue Han, he expressed a codon-optimized version of a bacterial halorhodopsin (along with the ChR2 protein) in neurons.
  • Upon exposure to yellow light, halorhodopsin pumps chloride ions into the cell, hyperpolarizing the membrane and inhibiting neural activity.
  • Using halorhodopsin and ChR2, neurons could be easily activated and inhibited using yellow and blue light respectively.

Halorhodopsin and ChR2 wavelengths

Brainbow

(Livet et al., 2007)

  • Lichtman and colleagues used Cre/Lox recombination tools to create genes which express a randomized set of three or more differently-colored fluorescent proteins (XFPs) in a given neuron, labeling the neuron with a unique combination of colors. About ninety distinct colors were emitted across a population of genetically modified neurons.
  • The detailed structures within neural tissue equipped with the Brainbow system can be imaged much more easily since neurons can be distinguished via color contrast.
  • As a proof-of-concept, hundreds of synaptic contacts and axonal processes were reconstructed in a selected volume of the cerebellum. Several other neural structures were also imaged using Brainbow.
  • The fluorescent proteins expressed by the Brainbow system are usable in vivo.

Brainbow

High temporal precision optogenetics

(Gunaydin et al., 2010)

  • Karl Deisseroth, Peter Hegemann, and colleagues used protein engineering to improve the temporal resolution of optogenetic stimulation.
  • Glutamic acid at position 123 in ChR2 was mutated to threonine, producing a new ion channel protein (dubbed ChETA).
  • The ChETA protein allows for induction of spike trains with frequencies up to 200 Hz and greatly decreases the incidence of unintended spikes. Furthermore, ChETA eliminates plateau potentials (a phenomenon which interferes with precise control of neural activity).

Ultrafast optogenetics

Hippocampal prosthesis in rats

(Berger et al., 2012)

  • Theodore Berger and his team developed an artificial replacement for neurons which transmit information from the CA3 region to the CA1 region of the hippocampus.
  • This cognitive prosthesis employs recording and stimulation electrodes along with a multi-input multi-output (MIMO) model to encode the information in CA3 and transfer it to CA1.
  • The hippocampal prosthesis was shown to restore and enhance memory in rats as evaluated by behavioral testing and brain imaging.

In vivo superresolution microscopy for neuroimaging

(Berning, Willig, Steffens, Dibaj, & Hell, 2012)

  • Stefan Hell (2014 Nobel laureate in chemistry) developed stimulated emission depletion microscopy (STED), a type of superresolution fluorescence microscopy which allows imaging of synapses and dendritic spines.
  • STED microscopy uses transgenic neurons that express fluorescent proteins. The neurons exhibit fluctuations in their fluorescence over time, providing temporal contrast enhancement to the resolution. Although light’s wavelength would ordinarily limit the resolution (the diffraction limit), STED’s temporal contrast overcomes this limitation.
  • Neurons in transgenic mice (equipped with glass-sealed holes in their skulls) were imaged using STED. Synapses and dendritic spines were observed up to fifteen nanometers below the surface of the brain tissue.

Superresolution microscopy in vivo

Eyewire: crowdsourcing method for retina mapping

(Marx, 2013)

  • The Eyewire project was created by Sebastian Seung’s research group. It is a crowdsourcing initiative for connectomic mapping within the retina towards uncovering neural circuits involved in visual processing.
  • Laboratories first collect data via serial electron microscopy as well as functional data from two-photon microscopy.
  • In the Eyewire game, images of tissue slices are provided to players who then help reconstruct neural morphologies and circuits by “coloring in” the parts of the images which correspond to cells and stacking many images on top of each other to generate 3D maps. Artificial intelligence tools help provide initial “best guesses” and guide the players, but the people ultimately perform the task of reconstruction.
  • By November 2013, around 82,000 participants had played the game. Its popularity continues to grow.

Eyewire

The BRAIN Initiative

(“Fact Sheet: BRAIN Initiative,” 2013)

  • The BRAIN Initiative (Brain Research through Advancing Innovative Technologies) provided neuroscientists with $110 million in governmental funding and $122 million in funding from private sources such as the Howard Hughes Medical Institute and the Allen Institute for Brain Science.
  • The BRAIN Initiative focused on funding research which develops and utilizes new technologies for functional connectomics. It helped to accelerate research on tools for decoding the mechanisms of neural circuits in order to understand and treat mental illness, neurodegenerative diseases, and traumatic brain injury.
  • The BRAIN Initiative emphasized collaboration between neuroscientists and physicists. It also pushed forward nanotechnology-based methods to image neural tissue, record from neurons, and otherwise collect neurobiological data.

The CLARITY method for making brains translucent

(Chung & Deisseroth, 2013)

  • Karl Deisseroth and colleagues developed a method called CLARITY to make samples of neural tissue optically translucent without damaging the fine cellular structures in the tissue. Using CLARITY, entire mouse brains have been turned transparent.
  • Mouse brains were infused with hydrogel monomers (acrylamide and bisacrylamide) as well as formaldehyde and some other compounds for facilitating crosslinking. Next, the hydrogel monomers were crosslinked by incubating the brains at 37°C. Lipids in the hydrogel-stabilized mouse brains were extracted using hydrophobic organic solvents and electrophoresis.
  • CLARITY allows antibody labeling, fluorescence microscopy, and other optically-dependent techniques to be used for imaging entire brains. In addition, it renders the tissue permeable to macromolecules, which broadens the types of experimental techniques that these samples can undergo (i.e. macromolecule-based stains, etc.)

CLARITY Imaging TechniqueTelepathic rats engineered using hippocampal prosthesis

(S. Deadwyler et al., 2013)

  • Berger’s hippocampal prosthesis was implanted in pairs of rats. When “donor” rats were trained to perform a task, they developed neural representations (memories) which were recorded by their hippocampal prostheses.
  • The donor rat memories were run through the MIMO model and transmitted to the stimulation electrodes of the hippocampal prostheses implanted in untrained “recipient” rats. After receiving the memories, the recipient rats showed significant improvements on the task that they had not been trained to perform.

Rat Telepathy

Integrated Information Theory 3.0

(Oizumi, Albantakis, & Tononi, 2014)

  • Integrated information theory (IIT) was originally proposed by Giulio Tononi in 2004. IIT is a quantitative theory of consciousness which may help explain the hard problem of consciousness.
  • IIT begins by assuming the following phenomenological axioms; each experience is characterized by how it differs from other experiences, an experience cannot be reduced to interdependent parts, and the boundaries which distinguish individual experiences are describable as having defined “spatiotemporal grains.”
  • From these phenomenological axioms and the assumption of causality, IIT identifies maximally irreducible conceptual structures (MICS) associated with individual experiences. MICS represent particular patterns of qualia that form unified percepts.
  • IIT also outlines a mathematical measure of an experience’s quantity. This measure is called integrated information or ϕ.

Expansion Microscopy

(F. Chen, Tillberg, & Boyden, 2015)

  • The Boyden group developed expansion microscopy, a method which enlarges neural tissue samples (including entire brains) with minimal structural distortions and so facilitates superior optical visualization of the scaled-up neural microanatomy. Furthermore, expansion microscopy greatly increases the optical translucency of treated samples.
  • Expansion microscopy operates by infusing a swellable polymer network into brain tissue samples along with several chemical treatments to facilitate polymerization and crosslinking and then triggering expansion via dialysis in water. With 4.5-fold enlargement, expansion microscopy only distorts the tissue by about 1% (computed using a comparison between control superresolution microscopy of easily-resolvable cellular features and the expanded version).
  • Before expansion, samples can express various fluorescent proteins to facilitate superresolution microscopy of the enlarged tissue once the process is complete. Furthermore, expanded tissue is highly amenable to fluorescent stains and antibody-based labels.

Expansion microscopy

Japan’s Brain/MINDS project

(Okano, Miyawaki, & Kasai, 2015)

  • In 2014, the Brain/MINDS (Brain Mapping by Integrated Neurotechnologies for Disease Studies) project was initiated to further neuroscientific understanding of the brain. This project received nearly $30 million in funding for its first year alone.
  • Brain/MINDS focuses on studying the brain of the common marmoset (a non-human primate abundant in Japan), developing new technologies for brain mapping, and understanding the human brain with the goal of finding new treatments for brain diseases.

Openworm

(Szigeti et al., 2014)

  • The anatomical elegans connectome was originally mapped in 1976 by Albertson and Thomson. More data has since been collected on neurotransmitters, electrophysiology, cell morphology, and other characteristics.
  • Szigeti, Larson, and their colleagues made an online platform for crowdsourcing research on elegans computational neuroscience, with the goal of completing an entire “simulated worm.”
  • The group also released software called Geppetto, a program that allows users to manipulate both multicompartmental Hodgkin-Huxley models and highly efficient soft-body physics simulations (for modeling the worm’s electrophysiology and anatomy).

C. elegans Connectome

The TrueNorth Chip from DARPA and IBM

(Akopyan et al., 2015)

  • The TrueNorth neuromorphic computing chip was constructed and validated by DARPA and IBM. TrueNorth uses circuit modules which mimic neurons. Inputs to these fundamental circuit modules must overcome a threshold in order to trigger “firing.”
  • The chip can emulate up to a million neurons with over 250 million synapses while requiring far less power than traditional computing devices.

Human Brain Project cortical mesocircuit reconstruction and simulation

(Markram et al., 2015)

  • The HBP digitally reconstructed a 0.29 mm3 region of rat cortical tissue (~ 31,000 neurons and 37 million synapses) based on morphological data, “connectivity rules,” and additional datasets. The cortical mesocircuit was emulated using the Blue Gene/Q supercomputer.
  • This emulation was sufficiently accurate to reproduce emergent neurological processes and yield insights on the mechanisms of their computations.

Cortical Mesocircuit

Neural lace

(Liu et al., 2015)

  • Charles Lieber’s group developed a syringe-injectable electronic mesh made of submicrometer-thick wiring for neural interfacing.
  • The meshes were constructed using novel soft electronics for biocompatibility. Upon injection, the neural lace expands to cover and record from centimeter-scale regions of tissue.
  • Neural lace may allow for “invasive” brain-computer interfaces to circumvent the need for surgical implantation. Lieber has continued to develop this technology towards clinical application.

Neural Lace

Expansion FISH

(F. Chen et al., 2016)

  • Boyden, Chen, Marblestone, Church, and colleagues combined fluorescent in situ hybridization (FISH) with expansion microscopy to image the spatial localization of RNA in neural tissue.
  • The group developed a chemical linker to covalently attach intracellular RNA to the infused polymer network used in expansion microscopy. This allowed for RNAs to maintain their relative spatial locations within each cell post-expansion.
  • After the tissue was enlarged, FISH was used to fluorescently label targeted RNA molecules. In this way, RNA localization was more effectively resolved.
  • As a proof-of-concept, expansion FISH was used to reveal the nanoscale distribution of long noncoding RNAs in nuclei as well as the locations of RNAs within dendritic spines.

Expansion FISH

Neural dust

(Seo et al., 2016)

  • Michel Maharbiz’s group invented implantable, ~ 1 mm biosensors for wireless neural recording and tested them in rats.
  • This neural dust could be miniaturized to less than 0.5 mm or even to microscale dimensions using customized electronic components.
  • Neural dust motes consist of two recording electrodes, a transistor, and a piezoelectric crystal.
  • The neural dust received external power from ultrasound. Neural signals were recorded by measuring disruptions to the piezoelectric crystal’s reflection of the ultrasound waves. Signal processing mathematics allowed precise detection of activity.

Neural Dust

The China Brain Project

(Poo et al., 2016)

  • The China Brain Project was launched to help understand the neural mechanisms of cognition, develop brain research technology platforms, develop preventative and diagnostic interventions for brain disorders, and to improve brain-inspired artificial intelligence technologies.
  • This project will be take place from 2016 until 2030 with the goal of completing mesoscopic brain circuit maps.
  • China’s population of non-human primates and preexisting non-human primate research facilities give the China Brain Project an advantage. The project will focus on studying rhesus macaques.

Somatosensory cortex stimulation for spinal cord injuries

(Flesher et al., 2016)

  • Gaunt, Flesher, and colleagues found that microstimulation of the primary somatosensory cortex (S1) partially restored tactile sensations to a patient with a spinal cord injury.
  • Electrode arrays were implanted into the S1 regions of a patient with a spinal cord injury. The array performed intracortical microstimulation over a period of six months.
  • The patient reported locations and perceptual qualities of the sensations elicited by microstimulation. The patient did not experience pain or “pins and needles” from any of the stimulus trains. Overall, 93% of the stimulus trains were reported as “possibly natural.”
  • Results from this study might be used to engineer upper-limb neuroprostheses which provide somatosensory feedback.

Somatosensory Stimulation

Hippocampal prosthesis in monkeys

(S. A. Deadwyler et al., 2017)

  • Theodore Berger continued developing his cognitive prosthesis and tested it in Rhesus Macaques.
  • As with the rats, monkeys with the implant showed substantially improved performance on memory tasks.

The $100 billion Softbank Vision Fund

(Lomas, 2017)

  • Masayoshi Son, the CEO of Softbank (a Japanese telecommunications corporation), announced a plan to raise $100 billion in venture capital to invest in artificial intelligence. This plan involved partnering with multiple large companies in order to raise this enormous amount of capital.
  • By the end of 2017, the Vision Fund successfully reached its $100 billion goal. Masayoshi Son has since announced further plans to continue raising money with a new goal of over $800 billion.
  • Masayoshi Son’s reason for these massive investments is the Technological Singularity. He agrees with Kurzweil that the Singularity will likely occur at around 2045 and he hopes to help bring the Singularity to fruition. Though Son is aware of the risks posed by artificial superintelligence, he feels that superintelligent AI’s potential to tackle some of humanity’s greatest challenges (such as climate change and the threat of nuclear war) outweighs those risks.

Bryan Johnson launches Kernel

(Regalado, 2017)

  • Entrepreneur Bryan Johnson invested $100 million to start Kernel, a neurotechnology company.
  • Kernel plans to develop implants that allow for recording and stimulation of large numbers of neurons at once. The company’s initial goal is to develop treatments for mental illnesses and neurodegenerative diseases. Its long-term goal is to enhance human intelligence.
  • Kernel originally partnered with Theodore Berger and intended to utilize his hippocampal prosthesis. Unfortunately, Berger and Kernel parted ways after about six months because Berger’s vision was reportedly too long-range to support a financially viable company (at least for now).
  • Kernel was originally a company called Kendall Research Systems. This company was started by a former member of the Boyden lab. In total, four members of Kernel’s team are former Boyden lab members.

Elon Musk launches NeuraLink

(Etherington, 2017)

  • Elon Musk (CEO of Tesla, SpaceX, and a number of other successful companies) initiated a neuroengineering venture called NeuraLink.
  • NeuraLink will begin by developing brain-computer interfaces (BCIs) for clinical applications, but the ultimate goal of the company is to enhance human cognitive abilities in order to keep up with artificial intelligence.
  • Though many of the details around NeuraLink’s research are not yet open to the public, it has been rumored that injectable electronics similar to Lieber’s neural lace might be involved.

Facebook announces effort to build brain-computer interfaces

(Constine, 2017)

  • Facebook revealed research on constructing non-invasive brain-computer interfaces (BCIs) at a company-run conference in 2017. The initiative is run by Regina Dugan, Facebook’s head of R&D at division building 8.
  • Facebook’s researchers are working on a non-invasive BCI which may eventually enable users to type one hundred words per minute with their thoughts alone. This effort builds on past investigations which have been used to help paralyzed patients.
  • The building 8 group is also developing a wearable device for “skin hearing.” Using just a series of vibrating actuators which mimic the cochlea, test subjects have so far been able to recognize up to nine words. Facebook intends to vastly expand this device’s capabilities.

DARPA funds research to develop improved brain-computer interfaces

(Hatmaker, 2017)

  • The U.S. government agency DARPA awarded $65 million in total funding to six research groups.
  • The recipients of this grant included five academic laboratories (headed by Arto Nurmikko, Ken Shepard, Jose-Alain Sahel and Serge Picaud, Vicent Pieribone, and Ehud Isacoff) and one small company called Paradromics Inc.
  • DARPA’s goal for this initiative is to develop a nickel-sized bidirectional brain-computer interface (BCI) which can record from and stimulate up to one million individual neurons at once.

Human Brain Project analyzes brain computations using algebraic topology

(Reimann et al., 2017)

  • Investigators at the Human Brain Project utilized algebraic topology to analyze the reconstructed ~ 31,000 neuron cortical microcircuit from their earlier work.
  • The analysis involved representing the cortical network as a digraph, finding directed cliques (complete directed subgraphs belonging to a digraph), and determining the net directionality of information flow (by computing the sum of the squares of the differences between in-degree and out-degree for all the neurons in a clique). In algebraic topology, directed cliques of n neurons are called directed simplices of dimension n-1.
  • Vast numbers of high-dimensional directed cliques were found in the cortical microcircuit (as compared to null models and other controls). Spike correlations between pairs of neurons within a clique were found to increase with the clique’s dimension and with the proximity of the neurons to the clique’s sink. Furthermore, topological metrics allowed insights into the flow of neural information among multiple cliques.
  • Experimental patch-clamp data supported the significance of the findings. In addition, similar patterns were found within the C. elegans connectome, suggesting that the results may generalize to nervous systems across species.

HBP algebraic topology

Early testing of hippocampal prosthesis algorithm in humans

(Song, She, Hampson, Deadwyler, & Berger, 2017)

  • Dong Song (who was working alongside Berger) tested the MIMO algorithm on human epilepsy patients using implanted recording and stimulation electrodes. The full hippocampal prosthesis was not implanted, but the electrodes acted similarly, though in a temporary capacity. Although only two patients were tested in this study, many trials were performed to compensate for the small sample size.
  • Hippocampal spike trains from individual cells in CA1 and CA3 were recorded from the patients during a delayed match-to-sample task. The patients were shown various images while neural activity data were recorded by the electrodes and processed by the MIMO model. The patients were then asked to recall which image they had been shown previously by picking it from a group of “distractor” images. Memories encoded by the MIMO model were used to stimulate hippocampal cells during the recall phase.
  • In comparison to controls in which the same two epilepsy patients were not assisted by the algorithm and stimulation, the experimental trials demonstrated a significant increase in successful pattern matching.

Brain imaging factory in China

(Cyranoski, 2017)

  • Qingming Luo started the HUST-Suzhou Institute for Brainsmatics, a brain imaging “factory.” Each of the numerous machines in Luo’s facility performs automated processing and imaging of tissue samples. The devices make ultrathin slices of brain tissue using diamond blades, treat the samples with fluorescent stains or other contrast-enhancing chemicals, and image then using fluorescence microscopy.
  • The institute has already demonstrated its potential by mapping the morphology of a previously unknown neuron which “wraps around” the entire mouse brain.

China Brain Mapping Image

Automated patch-clamp robot for in vivo neural recording

(Suk et al., 2017)

  • Ed S. Boyden and colleagues developed a robotic system to automate patch-clamp recordings from individual neurons. The robot was tested in vivo using mice and achieved a data collection yield similar to that of skilled human experimenters.
  • By continuously imaging neural tissue using two-photon microscopy, the robot can adapt to a target cell’s movement and shift the pipette to compensate. This adaptation is facilitated by a novel algorithm called an imagepatching algorithm. As the pipette approaches its target, the algorithm adjusts the pipette’s trajectory based on the real-time two-photon microscopy.
  • The robot can be used in vivo so long as the target cells express a fluorescent marker or otherwise fluoresce corresponding to their size and position.

Automated Patch Clamp System

Genome editing in the mammalian brain

(Nishiyama, Mikuni, & Yasuda, 2017)

  • Precise genome editing in the brain has historically been challenging because most neurons are postmitotic (non-dividing) and the postmitotic state prevents homology-directed repair (HDR) from occurring. HDR is a mechanism of DNA repair which allows for targeted insertions of DNA fragments with overhangs homologous to the region of interest (by contrast, non-homologous end-joining is highly unpredictable).
  • Nishiyama, Mikuni, and Yasuda developed a technique which allows genome editing in postmitotic mammalian neurons using adeno-associated viruses (AAVs) and CRISPR-Cas9.
  • The AAVs delivered ssDNA sequences encoding a single guide RNA (sgRNA) and an insert. Inserts encoding a hemagglutinin tag (HA) and inserts encoding EGFP were both tested. Cas9 was encoded endogenously by transgenic host cells and in transgenic host animals.
  • The technique achieved precise genome editing in vitro and in vivo with a low rate of off-target effects. Inserts did not cause deletion of nearby endogenous sequences for 98.1% of infected neurons.

Genome Editing Neurons

Near-infrared light and upconversion nanoparticles for optogenetic stimulation

(S. Chen et al., 2018)

  • Upconversion nanoparticles absorb two or more low-energy photons and emit a higher energy photon. For instance, multiple near-infrared photons can be converted into a single visible spectrum photon.
  • Shuo Chen and colleagues injected upconversion nanoparticles into the brains of mice and used them to convert externally applied near-infrared (NIR) light into visible light within the brain tissue. In this way, optogenetic stimulation was performed without the need for surgical implantation of fiber optics or similarly invasive procedures.
  • The authors demonstrated stimulation via upconversion of NIR to blue light (to activate ChR2) and inhibition via upconversion of NIR to green light (to activate a rhodopsin called Arch).
  • As a proof-of-concept, this technology was used to alter the behavior of the mice by activating hippocampally-encoded fear memories.

Upconversion nanoparticles and NIR

Map of all neuronal cell bodies within mouse brain

(Murakami et al., 2018)

  • Ueda, Murakami, and colleagues combined methods from expansion microscopy and CLARITY to develop a protocol called CUBIC-X which both expands and clears entire brains. Light-sheet fluorescence microscopy was used to image the treated brains and a novel algorithm was developed to detect individual nuclei.
  • Although expansion microscopy causes some increased tissue transparency on its own, CUBIC-X greatly improved this property in the enlarged tissues, facilitating more detailed whole-brain imaging.
  • Using CUBIC-X, the spatial locations of all the cell bodies (but not dendrites, axons, or synapses) within the mouse brain were mapped. This process was performed upon several adult mouse brains as well as several developing mouse brains to allow for comparative analysis.
  • The authors made the spatial atlas publicly available in order to facilitate global cooperation towards annotating connectivity among the neural cell bodies within the atlas.

CUBIC-X

Clinical testing of hippocampal prosthesis algorithm in humans

(Hampson et al., 2018)

  • Further clinical tests of Berger’s hippocampal prosthesis were performed. Twenty-one patients took part in the experiments. Seventeen patients underwent CA3 recording so as to facilitate training and optimization of the MIMO model. Eight patients received CA1 stimulation so as to improve their memories.
  • Electrodes with the ability to record from single neurons (10-24 single-neuron recording sites) and via EEG (4-6 EEG recording sites) were implanted such that recording and stimulation could occur at CA3 and CA1 respectively.
  • Patients performed behavioral memory tasks. Both short-term and long-term memory showed an average improvement of 35% across the patients who underwent stimulation.

Precise optogenetic manipulation of fifty neurons

(Mardinly et al., 2018)

  • Mardinly and colleagues engineered a novel excitatory optogenetic ion channel called ST-ChroME and a novel inhibitory optogenetic ion channel called IRES-ST-eGtACR1. The channels were localized to the somas of host neurons and generated stronger photocurrents over shorter timescales than previously existing opsins, allowing for powerful and precise optogenetic stimulation and inhibition.
  • 3D-SHOT is an optical technique in which light is tuned by a device called a spatial light modulator along with several other optical components. Using 3D-SHOT, light was precisely projected upon targeted neurons within a volume of 550×550×100 μm3.
  • By combining novel optogenetic ion channels and the 3D-SHOT technique, complex patterns of neural activity were created in vivo with high spatial and temporal precision.
  • Simultaneously, calcium imaging allowed measurement of the induced neural activity. More custom optoelectronic components helped avoid optical crosstalk of the fluorescent calcium markers with the photostimulating laser.

Optogenetic control of fifty neurons

Whole-brain Drosophila connectome data acquired via serial electron microscopy

(Zheng et al., 2018)

  • Zheng, Bock, and colleagues collected serial electron microscopy data on the entire adult Drosophila connectome, providing the data necessary to reconstruct a complete structural map of the fly’s brain at the resolution of individual synapses, dendritic spines, and axonal processes.
  • The data are in the form of 7050 transmission electron microscopy images (187500 x 87500 pixels and 16 GB per image), each representing a 40nm-thin slice of the fly’s brain. In total the dataset requires 106 TB of storage.
  • Although much of the the data still must be processed to reconstruct a 3-dimensional map of the Drosophila brain, the authors did create 3-dimensional reconstructions of selected areas in the olfactory pathway of the fly. In doing so, they discovered a new cell type as well as several other previously unrealized insights about the organization of Drosophila’s olfactory biology.

Drosophila connectome with SEM

 

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Notes on Digital Electronics


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PDF version: Notes on Digital Electronics – Logan Thrasher Collins

Logic gatesTable1

In digital electronics, circuit components (e.g. transistors, resistors, etc.) can be organized to form logic gates. Logic gates take input signals and determine output signals according to simple rules. Different logic gates exhibit distinct rules. Fundamental logic gates in digital electronics include the NOT gate (also called an inverter), AND gate, OR gate, NAND gate, NOR gate, XOR gate, and XNOR gate. The rules for these gates are given in the following table. 

It should be noted that there are also versions of these logic gates which take more than two inputs. For example, an AND gate with three inputs would require a signal value of 1 for all three inputs in order to give an output of 1.

NAND gates and NOR gates have alternative symbols (relative to the ones in the previous table) in certain situations. The following two paragraphs describe the reasons for these alternative symbols.

When a NAND gate is used to take one or more 0 inputs (i.e. 00, 01, 10 for a devicenegative-OR gate with two inputs), it acts as a “negative-OR” gate. In this case, any of the 0 inputs will give an output of 1, so the operation is similar to OR. The symbol for a negative-OR version of a NAND gate is given at right.

When a NOR gate is used to take all 0 inputs (i.e. 00 for a device with two inputs), it acts negative-AND gateas a “negative-AND” gate. In this case, all the 0 inputs together will give an output of 1, so the operation is similar to AND. The symbol for a negative-AND version of a NOR gate is given above.

Boolean algebraTable2

Boolean algebra provides a mathematical way of representing the behaviors of logic gates. Complementation, Boolean addition, and Boolean multiplication are important operations in Boolean algebra. These are shown in the table to the right. Combining these operations allows complex logic gate arrangements to be described. Note that any variable or its complement is referred to as a “literal” in the language of digital logic.

The distributive law, the commutative addition and multiplication laws, and the associative addition and multiplication laws are the same in Boolean algebra as in ordinary algebra. As a result, logic gate arrangements are subject to these laws.

There are twelve basic rules for Boolean algebra which are often useful for analyzing logic gates. They are listed in the box below.

Box1

DeMorgan’s theorems are useful tools for simplifying Boolean expressions. DeMorgan’s first theorem states that the complement of a Boolean product of variables equals the sum of the complements of the variables. DeMorgan’s second theorem states that the complement of a Boolean sum of variables equals the Boolean product of the complements of the variables. DeMorgan’s theorems also extend to expressions with more than two variables. DeMorgan’s first and second theorem are given below in the form with two variables and the form with n variables.

eq.1

SOP and POS forms

There are two forms into which Boolean expressions can be converted via simple algebraic methods; the sum-of-products (SOP) form and the product-of-sums (POS) form. These forms make it easier to use Boolean expressions for digital electronics. To understand these forms, note that the domain of a Boolean expression is the set of variables (either complemented or uncomplemented) which appear in the expression.Fig.1

The SOP form is exactly as its name suggests, a sum of terms which consist of products of literals (e.g. AB̅ + AC). The SOP form can also include terms which consist of a single literal. To implement the SOP form using logic gates, the following methods can be used. The first is to connect the output terminals of two or more AND gates to the input terminals of an OR gate. The second is to connect the output terminals of one or more NAND gates to the input terminals of another NAND gate (which operates in the negative-OR mode).

There is also a standard SOP form which is distinct from the regular SOP form. The standard SOP form is one in which all of the variables in the domain appear in each term of the expression. To convert a SOP expression into its standard form, multiply each nonstandard term by a sum of the missing variable and the missing variable’s complement. Repeat this until all of the terms contain all the variables in the domain either in complemented or uncomplemented form. As an example, consider the domain {A, B, C} and the SOP expression AB̅ + AC. Perform the operation AB̅(C + C̅) + AC(B + B̅) = AB̅C + AB̅C̅ + ABC. Now the expression is in standard SOP form (note that one of the AB̅C terms vanished as a result of the basic rules of Boolean algebra).

When looking at the binary representation of a standard SOP term, only a single combination of variable values will make the term equal to 1. Furthermore, a standard SOP expression will only equal 1 if at least one of its terms are equal to 1.Fig.2

The POS form is also exactly as its name suggests, a product of terms which consist of sums of literals. An example of this is (A̅ + B)(A + B̅ + C). The POS form can also include terms which consist of a single literal. However, a complementation overbar cannot extend to more than one term in a POS expression. To implement the POS form using logic gates, the following methods can be used. The first is to connect the output terminals of two or more OR gates to the input terminals of an AND gate. The second is to connect the output terminals of one or more NOR gates to the input terminals of another NOR gate (which operates in the negative-AND mode).

There is a standard POS form which is distinct from the regular POS form. The standard POS form is one in which all of the variables in the domain appear in each parentheses-enclosed sum term of the expression. To convert a POS expression into its standard form, add a product of the missing variable and its complement (e.g. AA̅) inside each parentheses-enclosed sum term. Next, apply the basic Boolean algebra rule that A + BC = (A + B)(A + C). Repeat this until the POS expression is in standard form. As an example, consider the domain {A, B, C} and the POS expression (A̅ + B)(A + B̅ + C). Perform the operation (A̅ + B + CC̅)(A + B̅ + C) = (A̅ + B + C)(A̅ + B + C̅)(A + B̅ + C). Now the expression is in standard POS form.

When looking at the binary representation of a standard POS sum term (parentheses-enclosed term), only a single combination of variable values will make the term equal to 0. Furthermore, a standard POS expression will only equal 0 if at least one of its parentheses-enclosed sum terms are equal to 0.

Truth tables

When working with Boolean expressions and digital electronics, a truth table provides a valuable way of representing the logical operation of a circuit. Furthermore, standard SOP and POS expressions can be determined using truth tables. A truth table is a listing of the possible combinations of input variable values and corresponding output values for a given Boolean expression.

To convert a standard SOP or POS expression into truth table format, first list all of the possible combinations of binary values (2n possibilities where n is the number of variables). Next, places 1s in the output column for all of the binary values that make the standard SOP or POS expression equal to 1 and place 0s in the output column for all of the binary values that make the standard SOP or POS expression equal to 0. Recall that a standard SOP expression will equal 1 if at least one of its terms are equal to 1 and that a standard POS expression will equal 0 if at least one of its parentheses-enclosed sum terms are equal to 0. The following tables are examples of truth tables for a SOP expression (left) and a POS expression (right).

Table3

To determine the standard SOP expression represented by a truth table, list the binary values for which the output is 1. Convert each binary value of 1 into its corresponding variable and each binary value of 0 into its corresponding complemented variable. This will produce the terms of the SOP expression which are then added together.

To determine the standard POS expression represented by a truth table, list the binary values for which the output is 0. Convert each binary value of 0 into its corresponding variable and each binary value of 1 into its corresponding complemented variable. This will produce the parentheses-enclosed sum terms of the POS expression which are then multiplied together.

Organization of Karnaugh maps

The Karnaugh map gives a systematic method for simplifying Boolean expressions. It can produce the simplest possible SOP or POS expression for a given problem. Karnaugh maps can be employed for expressions of two, three, four, or five variables. Note that there are also other algorithms, the Quine McCluskey method and the Espresso algorithm, which work on expressions with five or more variables. These more advanced algorithms are more readily automated by software as well. Nonetheless, Karnaugh maps represent a useful exercise for thinking about digital logic design.

The three-variable Karnaugh map is an array of eight cells (as shown below at left) with the possible binary values for A and B given along the rows and the possible binary values for C given along the columns. The four-variable Karnaugh map is an array of sixteen cells (as shown below at right) with the possible binary values for A and B given along the rows and the possible binary values for C and D given along the columns.

Fig.3

Karnaugh maps are arranged such that there is only a single change between any two adjacent cells. Two cells are physically adjacent if they are touching via the top, bottom, left side, or right side (diagonals do not count). Adjacency also extends to the cells at opposite edges of the tables. That is, the tables “wrap around” so that cells at the top are adjacent to corresponding cells at the bottom and cells at the left side are adjacent to corresponding cells at the right side.

Karnaugh maps with SOP and POS expressions

To map a standard SOP expression, place a 1 on the Karnaugh map for each term that is part of the expression. Make sure to place the 1s on the cells which match that term of the expression (e.g. A̅BC̅ goes in the 010 cell of a three-variable Karnaugh map). To map a nonstandard SOP expression, it must first be converted to standard SOP expression. This conversion is often carried out using a binary-based version of the method outlined earlier.

Karnaugh maps are often used to construct minimized SOP expressions (after starting with standard SOP expressions). By contrast to standard SOP expressions, minimized SOP expressions contain the fewest possible terms and the fewest possible variables per term. Minimized SOP expressions are usually implementable with fewer logic gates than standard SOP expressions.

To minimize a standard SOP expression, first perform the process of grouping the 1s. The goal of grouping the 1s is to maximize the size of the groups and minimize the number of groups. For Karnaugh maps of three or four variables, any individual group must consist of 1, 2, 4, 8, or 16 cells. Each cell in a group must be adjacent to one or more cells in the Fig.4same group (but not all cells in a group need to be adjacent). Each group must contain the largest possible number of 1s. Finally, each 1 on the map must be included in at least one group. Note that a 1 can be included in overlapping groups so long as each of the groups involved also have noncommon 1s. At right, an example of grouping the 1s is displayed.

The next step in minimizing a standard SOP expression is to analyze the groups of 1s. Each group of cells with 1s creates a product term composed of all of the variables in that group which occur in only the complemented or only the uncomplemented form. Any variables which occur in both forms within the group are eliminated. The remaining variables in a given group comprise a term within the minimized SOP expression. All of the terms from the groups are summed to find the full minimized SOP expression.

To use Karnaugh maps with truth tables for standard SOP expressions, place the 1s on the map according to the binary input values and the binary output values described by the truth table (e.g. if the truth table has a value of 101 for the SOP expression AB̅C, put a 1 on the corresponding 101 cell of the Karnaugh map).

Certain binary values are not used in certain applications. For instance, when encoding integers through binary, there are sixteen possible combinations of binary digits but only the integers 0 through 9 need to be represented (ten values). The other six combinations of binary digits are invalid. Since these binary values will never actually occur, their outputs can be treated as either 0 or 1. On Karnaugh maps, these are called “don’t care” terms. “Don’t care” terms are placed on the Karnaugh map as an X and treated as 1 if they help to make a larger group. If a “don’t care” term does not help to make a larger group, it is treated as a 0 output. By helping to make larger groups of 1s, “don’t care” terms can aid in further simplifying SOP expressions and allowing less logic gates to be used.

To map a standard POS expression, place a 0 on the Karnaugh map for each parentheses-enclosed sum term that is part of the expression. Make sure to place the 0s on the cells which match that term of the expression (e.g. (A̅ + B + C̅) goes in the 101 cell of a three-variable Karnaugh map). To map a nonstandard POS expression, it must first be converted to standard POS expression. This conversion is often carried out using a binary-based version of the method outlined earlier.

Karnaugh maps are often used to construct minimized POS expressions (after starting with standard POS expressions). By contrast to standard POS expressions, minimized POS expressions contain the fewest possible terms and the fewest possible variables per term. Minimized POS expressions are usually implementable with fewer logic gates than standard POS expressions.

To minimize a standard POS expression, first perform the process of grouping the 0s. The rules for doing so are identical to the rules for grouping the 1s as outlined in the previous section, except that 0s are used. The next step in minimizing a standard POS expression is to analyze the groups of 0s. This is also carried out in the same way as that described for the SOP version of the process. After doing this, the remaining variables in a given Fig.5group comprise a parentheses-enclosed sum term within the minimized POS expression. All of the parentheses-enclosed sum terms from the groups are multiplied to find the full minimized POS expression. “Don’t care” terms are also applied the same way for standard POS expressions as they are for standard SOP expressions.

To use Karnaugh maps with truth tables for standard POS expressions, place the 0s on the map according to the binary input values and the binary output values described by the truth table (e.g. if the truth table has a value of 101 for the POS expression AB̅C, put a 0 on the corresponding 101 cell of the Karnaugh map).

Karnaugh maps can be used to convert between standard SOP and standard POS expressions. This is useful to help compare the minimized versions of the expressions and to see if one of the two can be implemented using fewer logic gates than the other. For going from SOP to POS, all of the cells which do not contain 1s must instead contain 0s. These 0 cells encode the equivalent POS expression. For going from POS to SOP, all of the cells which do not contain 0s must instead contain 1s. These 1 cells encode the equivalent SOP expression.

Basic combinational logic

As described earlier, SOP expressions can be implemented via using AND logic gates as inputs to an OR logic gate output. This is called AND-OR logic. Another important type of logic is using AND logic gates as inputs to an OR logic gate and subsequently inverting the output of the OR gate. This is called AND-OR-Invert logic. To build a XOR gate (referred to as exclusive-OR logic), two AND gates, one OR gate, and two inverters are organized as shown in the figure below. To build a XNOR gate (referred to as exclusive-NOR logic), AND gates, OR gates, and inverters can be organized in multiple ways as shown in the figure below.

Fig.6

Implementing combinational logic from a Boolean expression requires recalling that Boolean multiplication is equivalent to an AND gate, Boolean addition is equivalent to an OR gate, and complementation is equivalent to a NOT gate (inverter). To implement combinational logic from a truth table, first convert the truth table to a Boolean expression via the method described in the truth table section. If a minimized Boolean expression is required, use a Karnaugh map as described in the section on Karnaugh maps.

Universal properties of NAND and NOR gates

The NAND gate is a universal gate because it can be used to make NOT, AND, OR, and NOR functions. The configurations of NAND gates needed to construct these functions are displayed below at left. The NOR gate is a universal gate because it can be used to make NOT, AND, OR, and NAND functions. The configurations of NOR gates needed to construct these functions are displayed below at right.

Fig.7

Combinational logic with NAND and NOR gates

NAND gates can be used to construct AND-OR logic systems (which implement SOP expressions). To do this, connect the output terminals of one or more NAND gates to the input terminals of another NAND gate. Note that the latter NAND gate is acting as a negative-OR gate.

Recall that, when a NAND gate is used to take one or more 0 inputs (i.e. 00, 01, 10 for a device with two inputs), it acts as a “negative-OR” gate. In this case, any of the 0 inputsnegative-OR gate will give an output of 1, so the operation is similar to OR. Any NAND gate carrying out a negative-OR operation is represented by the alternative symbol at right.

When drawing a combinational logic circuit with both NAND gates and negative-OR Fig.8gates, the symbols should be drawn with NAND gate bubbles facing negative-OR gate bubbles to help make it easier to visualize how the inversion properties of the gates are cancelling each other out (a bubble represents that a gate carries out inversion as at least part of its operation).

NOR gates can be used to construct logic systems which implement POS expressions. To do this, connect the output terminals of one or more NOR gates to the input terminals of another NOR gate. Note that the latter NOR gate is acting as a negative-AND gate.

Recall that, when a NOR gate is used to take all 0 inputs (i.e. 00 for a device with two inputs), it acts as a “negative-AND” gate. In this case, all the 0 inputs together will give an negative-AND gateoutput of 1, so the operation is similar to AND. Any NOR gate carrying out a negative-AND operation is represented by the alternative symbol at right.

When drawing a combinational logic circuit with both NOR gates and negative-ANDFig.9 gates, the symbols should be drawn with NOR gate bubbles facing negative-AND gate bubbles to help make it easier to visualize how the inversion properties of the gates are cancelling each other out. (As mentioned, a bubble represents that a gate carries out inversion as at least part of its operation).

Pulse waveform operation

The voltage or current signals associated with electronic circuits are often represented using trains of pulsed square waves. When a square wave is at its “HIGH” level of voltage or current, it represents a 1. When a square wave is at its “LOW” level of voltage or current, it represents a 0. Although these square waves are ideal forms of messier pulse trains, the binary qualities of digital electronic systems filter out the noise, allowing many analyses to be carried out with ideal square wave pulse trains.

In digital electronics, all waveforms are synchronized to a periodic waveform called the clock which keeps time for the system. All signals within the system are measured against the rate of periodic pulses from the clock. The clock sets the unit of time which denotes a single HIGH or LOW pulse state. This makes it so that the signals can pass through circuit elements (e.g. logic gates) at the appropriate time points and therefore perform appropriate operations.

Timing diagrams compare the signal trains within a digital system. Within each of these units of time, the waveforms for signal trains within the system might take on any combination of HIGH and LOW states.

Fig.10

In the context of logic gates, timing diagrams help to establish what inputs are arriving at which logic gates at what times. To review, the output of an AND gate at a given time is only HIGH if all of its inputs at the given time are HIGH, the output of an OR gate at a given time is only HIGH if at least one of its inputs at the given time are HIGH, the output of a NAND gate at a given time is LOW only when all of its inputs at the given time are HIGH, and the output of a NOR gate at a given time is LOW only when at least one of its inputs at the given time are HIGH.

Binary arithmetic

In digital systems, binary arithmetic serves as an essential mathematical language. It is especially vital for constructing complex combinational logic circuits which perform useful functions. As a starting point, the first fifteen binary numbers are 0, 1, 10, 11, 100, 101, 110, 111, 1000, 1001, 1010, 1011, 1100, 1101, 1110, 1111. These correspond to 1-15 in the base-10 system. Binary uses a base-2 system rather than a base-10 system. As will be explained in the following paragraphs, there are tricks which help to interconvert between binary and base-10 numbers.Fig.11

The columns of a binary number can be thought of as representing the of base-10 values which sum up to the binary number’s base-10 equivalent where the nth column from right to left equals 2n starting with n = 0. To explain this, consider the following example. The binary value of 1011 is equal to eleven in the base-10 system. From right to left, 20•1 + 21•1 + 22•0 + 23•1 = eleven. This technique can be employed to convert any binary number to a base-10 number. Going the other way, one can convert a base-10 number to a binary number by finding the binary weights wi that cause the sum of 2n•wi to add up to the desired binary value.

To convert a base-10 decimal number to a binary fraction, a similar method is used, but negative binary weights are used for the part of the decimal number which is less than one. For instance, 2-1 = 0.5 and 2-2 = 0.25. In addition, a decimal point is inserted into the final binary number and the n within 2-n grows from left to right. As an example, consider that the base-10 decimal number 0.75 = 2-1•1 + 2-2•1, so its binary fractional equivalent is 0.11. Likewise, a binary fraction can be converted to a base-10 decimal number by finding the binary weights wi that cause the sum of 2n•wi + 2-n•wi to add up to the desired base-10 decimal number.

Binary addition is summarized in the box below. The top part describes the basic Fig.12rules for addition of two bits (a bit is a single binary value) and the bottom part describes the rules for the addition of two bits plus a carry bit, which is a value that is carried in the same way as in base-10 addition. The carry bits are highlighted in gray. To illustrate this concept, an example of binary addition with carrying is also shown at right.

Box2

Binary subtraction is summarized in the box below. When performing binaryFig.13 subtraction, a borrow is only needed if subtracting a 1 from a 0 digit (e.g. 110 – 1). To borrow, take a 1 from the column to the left, create a 10 in the column undergoing subtraction, and apply the rule 10 – 1 = 1. To illustrate this concept, an example of binary subtraction with borrowing is also shown at right.

Box3Fig.14

Basic binary multiplication rules are summarized in the box below. To multiply two binary numbers, the top number is multiplied by each digit of the bottom number from right to left. The first of these partial products is not shifted left, the second is shifted left by place, the third is shifted left by two places and so on. After shifting, 0 values are put in the empty slots. The partial products are then summed to obtain the result. To illustrate this concept, an example of binary multiplication with shifting is shown at right.

Box4

To perform binary division, one must (1) set up long division. The quotient goes on the top, the dividend goes under the quotient, and the divisor goes to the side of the dividend. (2) Place a copy of the divisor below the dividend but align it to the leftmost digits of the dividend. (3) If the part of the dividend above the divisor is greater than or equal to the divisor, then subtract the divisor from that part of the dividend, place the Fig.15result of the subtraction below, and concatenate a 1 to the rightmost end of the quotient. If the dividend above the divisor is less than the divisor, concatenate a 0 to the rightmost end of the quotient. (4) Place another copy of the divisor at the bottom but shift it one column to the right. (5) Repeat steps 3 and 4 until the part of the dividend is less than the divisor. The result is the quotient with the dividend as a remainder. To illustrate this concept, an example of binary division is shown at right.

Half-adders and full-adders

Half-adders take in two input bits and subsequently generate a sum bit and a carry bit, performing the first step in binary addition. Half-adders implement the basic rules of binary addition: 0 + 0 = 0, 0 + 1 = 1, 1 + 0 = 1, and 1 + 1 = 10. Because XOR gates onlyTable4 produce outputs of 1 when the inputs are not equal, a XOR gate is used to generate the sum bit. Because AND gates only produce outputs of 1 when the inputs are both 1, an AND gate is used to generate the carry bit. The truth table for a half-adder is displayed at right. The logic gate diagram for a half-adder is shown below at left and the equivalent logic symbol is shown below at right.

Fig.16

Full-adders take in two input bits and an input carry and subsequently generate a sum bit and an output carry before performing the OR operation on them. In other words, full-adders can implement the addition of two 1-bit numbers and an input carry. To see how this corresponds to the rules for binary addition, refer to the box in the Table8section on binary addition. Since the sum of the two input bits is A⊕B, the sum of the two input bits and the input carry is (A⊕B)⊕Cin. (Note that the circled plus indicates the XOR operation). The equation for the output carry is Cout = AB + (A⊕B)Cin. Full-adders are composed of two half-adders and an OR gate. The truth table for a full-adder is displayed at right. The logic gate diagram for a full-adder is shown below at left and the equivalent logic symbol is shown below at right.

Fig.17

Parallel binary adders

Parallel binary adders are composed of two or more full adders and are used to add binary numbers of more than one bit. To add two binary numbers, a full-adder is needed for each bit in the numbers (e.g. for a 2-bit number, two full-adders are necessary). The least significant bit of the output is often grounded to make it zero since there is no carry input to the least significant bit (LSB) position. Note that there is also a most significant bit position (MSB) at the other end of the system. The block diagram for a 2-bit parallel adder alongside the addition operation it performs are shown below.

Fig.18

Extension of these concepts to 4-bit parallel adders and beyond is straightforward. The output carry terminal of each full-adder is linked to the input carry of the next full-adder in the lineup (these links are known as internal carries). In this way, larger binary numbers can undergo the sum operation. As an example, the block diagram for a 4-bit parallel adder and its equivalent logic symbol are shown below.

Fig.19

Table8

The truth table for a the 4-bit adder example is shown at right. The subscript n represents the adder bits. Cn – 1 represents the carry from the previous adder. Carries 1, 2, and 3 are internal carries while carry 4 is an output carry and carry 0 is an input carry.

Ripple carry adders are a category of parallel binary adder which have the output carry of each full-adder connected to the input carry of the next full-adder in the sequence. The sum and the output carry of any stage (a single full-adder is one stage) cannot be produced until the input carry occurs, causing time delays associated with each successive stage. The input carry to the LSB stage must move through all of the stages before a final sum is obtained. Because of this, the cumulative delay is assumed as the number of stages multiplied by the maximum amount of time for the signal to pass through each stage. For an individual ripple carry adder, this cumulative delay is often on the order of tens of nanoseconds.

Look-ahead carry adders are a category of parallel binary adder which eliminate the ripple carry delay and so operate more efficiently than ripple carry adders. To eliminate the ripple carry delay, look-ahead carry adders generate a carry only when both of the input bits are 1s. The input carry then propagates within its full-adder to the output carry. The generated carries are denoted Cg and the propagated carries are denoted Cp. The output carry of a single full-adder can be expressed in Boolean algebraic terms as Cout = Cg + CpCin. In parallel binary adders, the Cin of each successive stage equals the Cout of the previous stage. Since each Cg and Cp is expressible in terms of the A and B input bits (to the full-adders), all of the output carries are available almost immediately and it is not necessary to wait for the carries to ripple through all of the stages. The logic gate implementation of a 4-bit look-ahead carry adder is displayed below.

Fig.20

Comparators

Comparators compare two binary numbers and determine relationships between those quantities. The simplest type of comparator decides if two binary numbers are equal. An XNOR gate can act as a comparator to decide if the input bits are equal, giving an output of 1 if the inputs are equal and giving an output of 0 if the inputs are unequal. To expand this to numbers consisting of more bits, the output terminals of multiple XNOR gates are linked to the input terminals of an AND gate. In this way, all of the bits comprising the two numbers must be equal in order for the two numbers themselves to be equal. As an example, a 4-bit comparator and is shown below.

Fig.21

There are also magnitude comparators which can determine whether a binary number is greater than or less than another binary number. To do this, the magnitude comparator must examine if the MSB of number A is greater than, less than, or equal to the MSB of number B. If AMSB > BMSB, then A > B, if AMSB < BMSB, then A < B, and if AMSB = BMSB, then the same process must be performed upon the next most significant bit in the number. The steps are repeated until the relationship between the two numbers is determined. The output which correctly describes the relationship between the two numbers is 1 and the other two outputs are 0. As an example, a logic gate implementation of a 2-bit magnitude comparator and its equivalent logic symbol are shown below.

Fig.22

Decoders

Decoders give an output of 1 when a certain binary number is used as an input and they give an output of 0 for any other binary number input. For example, a decoder might detect the binary number 1001 and output 1 only when the inputs are 1001.

To perform their function, decoders must convert all the 0s of the targeted number Fig.23into 1s via NOT gates and then feed every input to an AND gate. Since the AND gate will only output 1 when all inputs are 1, the targeted number will be detected through the NOT gates converting all 0s in the targeted number to 1s. As an example, the logic gate implementation of a decoder which detects 1001 is shown at right.

One can also construct a decoder using a NAND gate in place of the AND gate. In the NAND gate version, an output of 0 indicates that the specified binary number has been detected.

One common type of decoder configuration is a decoder which takes in n bits and decodes every one of the 2n possible combinations of those bits. These decoders typically operate by the same principle as described above, though they must send their n inputs to 2n AND gates or NAND gates (depending on whether the active output needs to be a 1 or a 0). For example, a 4-line-to-16-line decoder receives a 4-bit input and outputs a 1 from a different terminal when it detects each of the 16 distinct combinations of 4 bits. To illustrate this, the truth table of a 4-line-to-16-line decoder which uses AND gates is displayed below.

Table5

Because this type of decoder outputs a 1 (or a 0 if NAND gates are used) corresponding to each of the possible 4-bit binary inputs, it can be used to convert 4-bit binary numbers to base-10 numbers. For instance, the binary value 1110 is equivalent to the base-10 value of 14 and 1110 activates the 14th output terminal. The logic symbol for a 4-line-to-16-line decoder (with 1s as active outputs) is displayed below.

Fig.24

There are other similar decoders that act as standard parts for binary to base-10 conversion. In particular, the BCD-to-decimal converter (“decimal” is another name for base-10 numbers and “BCD” stands for Binary Coded Decimal) or 4-line-10-line decoder uses the same setup as the 4-line-to-16-line decoder, but it only has ten outputs. These ten outputs correspond to the base-10 values of zero through nine. Once again, the active output is 1 if AND gates are used and is 0 if NAND gates are used. Another common decoder is the BCD-to-7-segment decoder, which has a 4-bit input and gives one of seven outputs depending on the input. This decoder also operates on the same principle as the 4-line-to-16-line decoder. However, the BCD-7-segment decoder is used to control the images formed on a seven-segment display device such as a handheld calculator.

Encoders

An encoder first receives an active input on one of its input terminals. The input often represents a digit (such as a base-10 digit), an alphabetic character, or another symbol. Next, the encoder translates this input into a coded output such as a binary value.

One common type of encoder is the decimal-to-BCD encoder. As alluded to in the previous section, BCD code is a way of representing base-10 numbers using 4-bit binary values. The BCD code uses the first ten binary numbers (0000 to 1010) to represent base-10 values of 0 to 9.Table6

The decimal-to-BCD encoder has inputs of 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9 and outputs of A3, A2, A1, and A0. Decimal-to-BCD encoders use OR gates to convert each input to its appropriate output. The logic of this (assuming 1 as the active value) is as follows. Bit A3 only outputs a 1 for base-10 digits 8 or 9, bit A2 only outputs a 1 for base-10 digits 4 or 5 or 6 or 7, bit A1 only outputs a 1 for base-10 digits 2 or 3 or 6 or 7, and bit A0 only outputs a 1 for digits 1 or 3 or 5 or 7 or 9. To better understand this, it is helpful to examine the decimal-to-BCD truth table above. The logic gate implementation for a decimal-to-BCD encoder is displayed below alongside its corresponding logic symbol. Note that an input for the base-10 zero digit is not needed since, when all of the binary inputs are 0, the binary outputs are 0000.

Fig.25

Another similar type of encoder is the decimal-to-BCD priority encoder. This type of encoder is configured to include a priority function. As a result, if multiple inputs are given to the decimal-to-BCD priority encoder, the binary output will represent only the largest base-10 value that was used as an input. By comparison, the decimal-to-BCD encoder without the priority function must have only one active input to work properly.

Code converters

It is often necessary in electronic systems to convert between different digital codes. Though there are others, one important type of code converter is the BCD-to-binary converter. This type of code converter can help to illustrate the general idea of code conversion in digital electronics.

From a mathematical standpoint, to perform BCD-to-binary conversion, one must first weight the BCD digits depending on their position within the BCD number. For instance, a decimal value of 27 is represented in BCD as 00100111. In BCD code, the 0010 corresponds to the 2 in the tens place and the 0111 corresponds to the 7 in the ones place. The weights of the tens place are 80, 40, 20, 10 and the weights of the ones place are 8, 4, 2, 1.

In BCD-to-binary circuits, the binary representations of the weights of the BCD bits are added to obtain the corresponding binary number. For the case of 00100111, this is 1•20 + 1•4 + 1•2 + 1•1 = 27 in decimal form and 1•10100 + 1•100 + 1•10 + 1•1 in binary form (the parts that are multiplied by 0 have been excluded here since they equal 0). The BCD-to-binary conversion can be implemented using adder circuits to sum the weighted binary numbers.

Multiplexers

Multiplexers (or data selectors) are devices which take in data from multiple sources and route those data into a single transmission line to send them to a common destination. The way that a multiplexer works is that it receives both data-select lines and data-input lines. Bits sent to the data-select lines control which of the data-input lines is transmitted to the single data-output line.

As an example, consider a 4-bit multiplexer. The 4-bit multiplexer receives two data-Table7select lines which control four data-input lines. The four possible inputs to the data-select lines (00, 01, 10, and 11) control which of the four data-input lines undergoes transmission to the data-output line. The truth table for this device is displayed at right where S1 and S0 are the data-select lines.

To understand how to implement a 4-bit multiplexer using logic gates, it is helpful to see that the Boolean expression below describes the multiplexer’s operation. Y represents the data output. Recall that OR gates implement Boolean addition, AND gates implement Boolean multiplication, and NOT gates implement Boolean complementation. The logic gate implementation and corresponding logic symbol of a 4-bit multiplexer are given along with the Boolean expression.

Fig.26

The principles of 4-bit multiplexers can readily be extended to any n-bit multiplexers. For a given n-bit multiplexer with n data-input lines, log2(n) data-select lines would be necessary. The logic gate implementation would depend on a Boolean expression analogous to the one above, but with more input variables and more of their corresponding data-select bit combinations.

Demultiplexers

Demultiplexers take information from a single input line and send the information to a given number of output lines. As with multiplexers, demultiplexers also take data-select inputs to determine to which output line the data are sent.

To illustrate how demultiplexers work, consider a 1-line-to-4-line demultiplexer. The logic gate implementation for a 1-line-to-4-line demultiplexer is displayed below. This demultiplexer receives a single data-input line which goes to all of the AND gates. It also receives two data-select lines which control which of the AND gates transmits the data-input line. Since all inputs to an AND gate must be 1 in order for the AND gate to transmit a 1, the data-select lines make it possible to choose which AND gate receives all 1 inputs. Using the data-select lines, only one of the data-output lines will transmit the information at a time.

Fig.27

The principles of demultiplexers in general can readily be found by extending the concept of the above demultiplexer. For a demultiplexer with n data-output lines, log2(n) data-select lines are needed to control where the data-input line signal is sent.

Modified decoders are also usable as demultiplexers. When a decoder is used as a demultiplexer, its input lines are used as data-select lines since they determine which of the output lines sends an active signal. The modification needed is an enable gate. If the enable gate is not active on both inputs, the decoder cannot transmit any active outputs. By fixing one of the enable gate’s inputs as permanently active, the other input to the enable gate can behave as the data-input line. In this way, the decoder acts as a demultiplexer.

Parity checkers

Parity is a method of error detection. For a given system, any set of bits contains either an even or an odd number of 1s. Depending on the system, the parity bit is attached to a set of bits to make it so that the total number of 1s in the given system is always even or so that the total number of 1s in the given system is always odd. If a system operates with even parity, a parity check is made on each set of bits to make sure that the total number of 1s is even. If a system operates with odd parity, a parity check is made on each set of bits to make sure that the total number of 1s is odd. In the case that these conditions are not met, the system in question reports an error.

To determine if a code has even parity or odd parity, all of the bits in that code are added together. Two bits can be summed using a single XOR gate, four bits can be summed using a pair of XOR gates with their output terminals linked to the input terminals of a third XOR gate, and so on. It is important to note that the sum is a modulo-2 sum. This is a binary sum where a 0 results whenever a carry would otherwise occur (0 + 0 = 0, 0 + 1 = 1, 1 + 0 = 1, 1 + 1 = 0). If a set of bits contains an even number of bits, the XOR gate system will produce a 1. If a set of bits contains an odd number of bits, the XOR gate system will produce a 0. The XOR gate implementations of parity checkers for sets of two, four, and eight bits are given below.

Fig.28

Reference: Floyd, T. (2015). Digital Fundamentals, Global Edition. Pearson Education Limited.

Cover image source: SciTechDaily.com

Notes on Optics and Microscopy


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Wave optics

The wave equation

Because light exhibits wave-particle duality, wave-based descriptions of light are often appropriate in optical physics, allowing the establishment of an electromagnetic theory of light.

As electric fields can be generated by time-varying magnetic fields and magnetic fields can be generated time-varying electric fields, electromagnetic waves are perpendicular oscillating waves of electric and magnetic fields that propagate through space. For lossless media, the E and B field waves are in phase.

By manipulating Maxwell’s equations of electromagnetism, two relatively concise vector expressions that describe the propagation of electric and magnetic fields in free space are found. Recall that the constants ε0 and μ0 are the permittivity and permeability of free space respectively.

eq1

Since an electromagnetic wave consists of perpendicular electric and magnetic waves that are in phase, light can be described using the wave equation (which is equivalent to the expressions above). Note that the speed of light c = (ε0μ0)-1/2. Electromagnetic waves represent solutions to the wave equation.

eq2

Either the electric or the magnetic field can used to represent the electromagnetic wave since they propagate with the same phase and direction. With the exception of the wave equation above, the electric field E will instead be used to represent both waves. Note that either the electric or magnetic field can be employed to compute amplitudes.

Solutions to the wave equation

Plane waves represent an important class of solutions to the wave equation. The parameter k is the wavevector (which points in the direction of the wave’s propagation) with a magnitude equal to the wavenumber 2π/λ. In a 1-dimensional system, the dot product k•r is replaced by kx. The parameter ω is the angular frequency 2πf and φ is a phase shift.

eq3

To simplify calculations, Euler’s formula can be used to convert the equation above into complex exponential form. Only the real part describes the wave as the real part corresponds to the cosine term.

eq4

Spherical waves are another useful solution to the wave equation (though they are an approximation and truly spherical waves cannot exist). Because of their geometry, the electric field of a spherical wave is only dependent on distance from the origin. As such, the equation for a spherical wave can be written as seen below with origin r0.

eq5

Gaussian beams are a solution to the wave equation that can be used to model light from lasers or light propagating through lenses. If a Gaussian beam propagates in the z direction, then from the perspective of the xy plane, it shows a Gaussian intensity distribution. For a Gaussian beam, the amplitude decays over the direction of propagation according to some function A(z), R(z) represents the radius of curvature of the wavefront, and w(z) is the radius of the wave on the xy plane at distance z from the emitter. Often these functions can be approximated as constants.

eq6

Fig. 1

Intensity and energy of electromagnetic waves

The Poynting vector S is oriented in the direction of a wave’s propagation (assuming that the wave’s energy flows in the direction of its propagation).

eq7

The magnitude of the Poynting vector represents the power per unit area (W/m2) or intensity crossing a surface with a normal parallel to S. Note that this is an approximation since, according to a quantum mechanical description of electromagnetic waves, the energy should be quantized.

eq8

Power per unit area (intensity or irradiance) of plane waves, spherical waves, and Gaussian beams can also be calculated using the equations below. The formula for the Gaussian beam’s power represents the power at a plane perpendicular to the direction of light propagation z.

eq9

For electromagnetic waves, instantaneous energy per unit area is difficult to measure, so the average energy per unit area over a period of time Δt is often worked with instead. Since waves are continuous functions, taking their time-average requires an integral.

eq10

When using the above integral on the function eiωt, it is useful to think of finding the real and imaginary parts, cos(ωt) and sin(ωt). Then the time-averages of the cosine and sine functions can be computed using the following equations.

eq11

Polarization of light

The waves comprising linearly polarized light are all oriented at the same angle which is defined by the direction of the electric field of the light waves. For linearly polarized plane waves with electric fields oriented along the x or y axes that propagate in the z direction, the following equations describe their electric fields.

eq15

The superposition of two linearly polarized plane waves that are orthogonal to each other (and out of phase) is the vector sum of each electric field.

eq16

The superposition of two linearly polarized plane waves that are orthogonal to each other (and in phase) is computed via the following equation and has a tilt angle θ determined by the ratio of amplitudes of the original waves. This process can also be performed in reverse with a superposed polarized wave undergoing decomposition into two orthogonal waves.

eq18

When two constituent waves possess equal amplitudes and a phase shift of nπ/2, the superposed wave is circularly polarized (as it can be expressed using a sine and a cosine term). Equations for the constituent waves and the superposed wave are given below.

eq19Fig. 2

When circularly polarized light propagates, it takes a helical path and so rotates. As such, a full rotation occurs after one wavelength. If a circularly polarized wave rotates clockwise, it is called right-circularly polarized and has a positive sine term. If a circularly polarized wave rotates counterclockwise, it is called left-circularly polarized and has a negative sine term.

eq20

If a right-circularly polarized light wave and a left-circularly polarized light wave of equal amplitude are superposed, then they create a linearly polarized light wave with twice the amplitude of the individual waves.

eq21

Linearly polarized and circularly polarized light are special cases of elliptically polarized light. For elliptically polarized light, the amplitudes of the superposed waves may differ and the relative phase shift does not need to be nπ/2. As such, the electric field traces an elliptical helix as it propagates along the z direction.

eq22

For elliptically polarized light with a positive phase shift φ, it is called right-elliptically polarized if E0x > E0y and left-elliptically polarized if E0x < E0y.

Most light is unpolarized (or more appropriately, a mixture of randomly polarized waves). To obtain polarized light, polarizing filters are often used.

Superposition of waves with same frequency and direction

Let two waves E1 and E2 of the same frequency traveling in the same direction undergo superposition. E1 and E2 may or may not possess the same amplitude or phase. The substitution α = –(kx+φ) will be carried out.

eq12

If the phases of the waves are different, some special equations are necessary to find the amplitude E0 and the phase α of the resulting wave.

eq13

For the superposition of any number of these waves, the equations above can be extended.

eq14

It is often useful to employ complex exponentials when calculating the superposition of waves. For a sum of waves traveling in the same direction which all exhibit the same frequency, the following equation can be used. The summation in parentheses is called the complex amplitude of the resulting wave.

eq25

Superposition of waves with different frequencies

 In another special case, consider calculation of the superposition of two waves with the same direction and amplitude E01 but different frequencies ω1 and ω2. The resulting wave can be expressed using the equation below. Since this wave takes on a beat pattern (see figure), the total disturbance of this wave has an average angular frequency (also called a temporal frequency) of ϖ = 0.5(ω1 + ω2) and an average propagation number (also called a spatial frequency) of k̅ = 0.5(k1 + k2). The quantity ωm is called the modulation frequency and the quantity km is called the modulation propagation number.

eq23

Fig. 3

When waves of different frequencies undergo superposition, they create waves which themselves oscillate. The beat pattern is an example of this phenomenon. The smooth curves which outline the extremes of an oscillating signal are called the envelope of that signal.

Fig. 4

Often when adding waves of different frequencies, the higher-frequency wave is referred to as the carrier wave and the lower-frequency wave is called the modulating wave. Since the modulating wave determines the envelope of the resulting waveform, this envelope is called the modulation envelope.

Furthermore, when waves of different frequencies undergo superposition, the modulation envelope travels at a different velocity than the constituent waves. The constituent waves travel at their phase velocities, given by v = ϖ/k̅, while the envelope travels at a group velocity. Note that in a nondispersive medium (vacuum), the phase and group velocities are the same since the speed of light is a constant. However, in a dispersive medium (non-vacuum), the phase velocity and group velocities differ. To find the group velocity, the equation below is used. If the frequencies of the two waves undergoing superposition are very similar, then the value of this formula approaches the derivative vg = dω/dk.

eq24

Superposition of waves using Fourier series

Fourier series can be utilized to represent periodic waves. To construct a Fourier series representation of a periodic function of a wave, the following equations are employed. As is consistent, λ is the wavelength and k is the wavenumber 2π/λ. More terms in a Fourier series representation leads to a more accurate approximation of the given wave.

eq44

It is often useful to create a Fourier series representation using complex exponentials. To do this, the following equations are used rather than those above.

eq45

Interference

Recall that the intensity (or irradiance) of an electromagnetic wave is the power received by a surface per unit area. Due to the high frequencies of many electromagnetic fields, it is often most useful to measure intensity. As such, equations for working with interference and intensity are valuable tools for optics.

When considering the interference of two waves with the same frequency E1(r,t) and E2(r,t), the average intensity Iavg of the resulting disturbance over a time period Δt is computed using the following equations. (This treatment assumes that the sources of the waves are separated by a distance much greater than their wavelength). To find Iavg at any given point in space, simply evaluate the equation for Iavg using the coordinates for that point. The angled brackets describe time averages over Δt (recall the integral from a previous section). Here, squaring a vector is equivalent to taking its dot product with itself.

eq26

In the case described above, the maximum intensity (total constructive interference) occurs where δ = 2nπ and the minimum intensity (total destructive interference) occurs where δ = (2n+1)π. The light and dark zones that result from constructive and destructive interference are called interference fringes.

For two waves to interfere, they must have the same or very close to the same frequency. In the case of a large frequency difference, a rapidly varying and time-dependent phase difference would occur, causing the interference term (E01•E02)cos(δ) to average to zero. However, when two sources emit white light, some interference takes place. This is because similar wavelengths within the white light will interfere with each other (e.g. blues will interfere with blues). For white light, the interference is not as sharp as in the case of monochromatic light.

Interference patterns will occur when any phase difference between the waves is constant. (Note that two waves with the same frequency and a constant phase difference are coherent waves). In addition, interference patterns are more clearly observable when the interfering waves have very similar amplitudes since this means that the maxima and minima of the interference fringes correspond to total constructive and total destructive interference.

Polarization of light can influence interference. Recall that the polarization state of a wave is defined by the orientation of its electric field. Even if coherent, two waves with orthogonal polarization states cannot interfere. By contrast, coherent waves with parallel polarization states will interfere.

DiffractionFig. 5

Diffraction occurs when light encounters an obstruction and a multitude of scattered waves result, causing interference patterns to emerge. The vector theory of diffraction is very complicated. But in many applications, one can use approximate scalar treatments of diffraction which are based on Huygens’s principle. When dealing with a viewing position that is close to the obstruction, Fresnel diffraction is used. When dealing with a viewing position that is far from the obstruction, Fraunhofer diffraction is employed.

According to Huygens’s principle, each point on a wavefront can be treated as a source of a secondary spherical wavelet and the envelope of these wavelets describes the new position of the wavefront. One illustrative result of this is that a plane wave passing through an infinitesimal pinhole in a barrier will produce a spherical wave on the other side of the wall. For larger apertures in a barrier, the emerging wave will be the sum of an infinite number of spherical waves originating at each point on the area of the aperture (see figure). It should be noted that the backward propagating part of the secondary wavelet envelope is not physically accurate and is largely ignored in this scalar approximation of diffraction.

For light propagating through an aperture of any shape, the Huygens-Fresnel integral describes the electric field at a point in space r located past the barrier. This integral is an infinite sum of spherical waves that emerge from each infinitesimal point coveringFig. 6 the barrier’s aperture. The constant i/λ arises as an approximation of the influence of the angle of the wave arriving at the aperture relative to the aperture’s normal vector. The domain Σ over which integration takes place is the aperture’s area and r0 represents an infinitesimal surface element of the aperture dx0dy0 (with the flat barrier and its aperture described as an xy plane). Ei(x0,y0) is the complex amplitude of the incident wave at the point x0,y0 on the flat barrier’s xy coordinate system. Here, θ represents the angle of the vector r – r0 relative to the aperture’s normal vector. A transmission function f(x0,y0) can be included inside the integral to describe the effect of a partially translucent surface. For the case of a fully opaque barrier and a fully translucent aperture, f(x0,y0) is zero at all barrier points and one at all aperture points. For a system with a partially translucent barrier or aperture, f(x0,y0) is a complex quantity with magnitudes falling between zero and one. Recall that the magnitude of a complex quantity a + bi is defined as (a2 + b2)1/2. Though it is an approximation, the Huygens-Fresnel integral is still complicated enough that it is often computed numerically.

eq27

The Fraunhofer approximation is employed when the aperture’s size is small relative to the distance to the observation point (such that the light is approximately a plane wave at this observation point). It should be noted that converging lenses can decrease the necessary distance for using the Fraunhofer approximation. Fraunhofer diffraction also assumes that the light source is far enough from the aperture that the incident light can be considered a plane wave. A useful property of the Fraunhofer diffraction integral is that it is mathematically equivalent to a 2D Fourier transform of the aperture’s transmission function f(x0,y0). The Fraunhofer diffraction integral equation is given as follows. Here, x0 and y0 are points on the plane of the aperture or barrier while r or x, y, and z represent points anywhere in space. 

eq28

Ray optics

Refraction and total internal reflection

When light moves between materials with different refractive indices, the refracted ray’s angle changes relative to the incident ray’s angle, a process called refraction. The refractive index n of a material describes the rate of propagation of light within that material relative to vacuum (which has a refractive index of one).

For most media, the refractive index of a given material varies with the wavelength of the light as n(λ). The property of birefringence also occurs in some materials (and is especially common in crystals). Birefringence is when a material’s refractive index varies depending on the angle of light propagation or on the polarization state of the light.

Refraction is described by Snell’s law. Here, θ1 is the incident ray’s angle relative to the normal of the interface, θ2 is the refracted ray’s angle relative to the normal of Fig. 7the interface, n1 is the index of refraction of the material in which the incident ray propagates, and n2 is the index of refraction of the material in which the refracted ray propagates. If n1 < n2, the refracted ray bends towards the normal of the interface. If n1 > n2, the refracted ray bends away from the normal of the interface. It should be noted that refraction (and reflection) can also be studied in the context of wave optics.

eq29Fig. 8

In total internal reflection, a ray of light is reflected from an interface between two media at the same angle as the angle it approached the interface from. Total internal reflection occurs when the incident ray is traveling from a medium with a higher refractive index to a medium with a lower refractive index and approaches from an angle relative to the normal of the interface greater than a value called the critical angle θc. The critical angle is computed using the following equation.

eq30

Basic ray tracing and lenses

Though ray optics itself represents an approximation and is not as accurate as wave optics, a further simplification called the paraxial approximation is often still useful in practice. For the paraxial approximation, the angles made by all rays with respect the system’s optical axis (e.g. the line perpendicular to a lens) are assumed to be small enough that θ ≈ sin(θ) ≈ tan(θ) and that cos(θ) ≈ 1. This means that Snell’s law simplifies to n1θ1 = n2θ2.

When working with thin lenses and the paraxial approximation, three rules for ray tracing can be applied. (1) All rays which pass through a focal point on one side of the lens bend as they pass through the lens so that they are parallel to the optical axis when they continue on the other side of the lens. (2) Any ray which passes through the center of a lens continues in the same direction and does not bend. (3) All rays which are parallel to each other (though not necessarily to the optical axis) on one side of a lens will bend as they move through the lens to focus upon a single point on the other side. The location of this point is at the intersection of the optical axis with a ray that both passes through the center of the lens and is part of the group of parallel rays.

The distance dobj from an object to the thin lens, the distance dimg from the thin lens to where object’s associated image forms, the focal length f of the thin lens, and the magnification M of the thin lens are related using the following equations. The negative value of magnification indicates that the orientation of the image is inverted relative to the object.

eq31

The focal length of a thin lens is computed using a simplified form of the lens maker’s equation. R1 is the radius of curvature for the side of the lens closest to the lightFig. 9 source, R2 is the radius of curvature for the side of the lens furthest from the light source, and n is the refractive index of the material of the lens. Sign conventions for R1 and R2 are usually as follows. If the lens is convex, R1 is positive and R2 is negative. If the lens is concave, R1 is negative and R2 is positive.

eq32

For thicker lenses, the full lens maker’s equation is used and takes the following form. Here, d represents the thickness of the lens.

eq33

Ray transfer matrix analysis

To aid in the analysis of complicated optical systems, a matrix-based formalism is often employed. Ray transfer matrix analysis requires the paraxial approximation; small enough angles of incident rays relative to the optical axis that θ ≈ sin(θ) ≈ tan(θ) and that cos(θ) ≈ 1). As a result of the paraxial approximation, the equations involved in the propagation of light rays are linear and therefore can be described using matrices. As usual, the propagation direction of rays is conventionally represented as going from left to right.

In ray transfer matrix analysis, a given ray starts out at an angle θ1 with respect to the optical axis and at a height y1 relative to the optical axis. After passing through an optical element or a series of optical elements (an optical system), the ray has an angle θ2 with respect to the optical axis and a height y2 relative to the optical axis. The ray transfer matrix M (also called the ABCD matrix) helps to compute an output ray from an input ray using an equation of the following form.

eq46

The entries of the ray transfer matrix depend upon the components of the given optical system. To describe combinations of several successive optical components, several 2×2 ray transfer matrices are multiplied in the order of the components to give an overall ray transfer matrix. Below is a table of ray transfer matrices for some common optical components.

Table1

Fundamentals of Microscopy

Typical organization of a basic light microscope

Modern light microscopes typically employ an objective lens and a tube lens. These are called infinity-corrected microscopes. The objective lens collects light rays and refracts them so that they are parallel to each other. Next, the tube lens focuses these parallel rays onto a detector. The space where the rays are parallel between the objective lens and the tube lens is called infinity space. The magnification of this system is computed by the ratio of the focal lengths of the tube lens and objective lens.

eq34

Fig. 12

If the microscope allows for direct observation by eye, an extra lens (the ocular lens) refracts the light from the tube lens so that the rays are once again parallel to each other. These parallel rays are subsequently focused onto the retina by the human eye’s lens. The magnification produced by the ocular lens and human eye is the focal length of the ocular lens divided by distance from the eye that the brain perceives the image is located (about 25 cm). Ocular lenses are usually designed to give an additional magnification factor of 10x.

Many microscopes use a detector to carry out imaging rather than (or in addition to) direct observation by eye. Since detectors convert light into an array of pixels using a photodetector array, the size of the individual photodetector devices determines the maximum level of detail that the microscope can acquire. Smaller photodetectors allow for more detail, though this only helps if the microscope’s optics can achieve sufficient resolution to provide this level of detail in the first place. 

Microscopes also require a light source to illuminate the sample. Light sources often pass light through the sample from beneath, though sometimes inverted configurations where the light source is at the top and the objective lens is underneath are used. To focus the light from a source onto a specimen, a condenser lens is employed. In addition, a component called the condenser diaphragm acts as an iris that can adjust the numerical aperture (a concept described in the next section) of the condenser lens by limiting the size of the light cone.

Resolution in light microscopy

The maximum angle at which rays from the specimen can be collected by the objective lens is called the angular aperture. Using the angular aperture, the numerical aperture NA of the objective lens is computed. Numerical aperture is important for calculating the resolution of a light microscope. Here, n is the refractive index of the medium between the lens and the specimen and α is half of the angular aperture.

eq35

The resolution of light microscopy is diffraction limited (except in super-resolution microscopy), meaning that diffractive effects result in a maximum achievable resolutionFig. 13 which depends on wavelength and numerical aperture. To understand this, consider diffraction from a point object. (Assume small angles with respect to the optical axis and neglect vector analysis). According to Huygens’s principle, the point object diffracts incoming light into a spherical wave. Part of this spherical wave is converted into a plane wave by the objective lens. Next, the tube lens focuses this light. At the focus on the image plane, the waves constructively interfere since they exhibit the same optical pathlength. But around this point, the optical pathlengths are different and a series of concentric circles of destructive and constructive interference occur. This phenomenon is called the Airy pattern.

The intensity of light from the Airy pattern is a type of point spread function (PSF). Any microscope image is made up of a tapestry of PSFs. It should be noted that the AiryFig. 14 pattern represents an ideal PSF for a perfect optical system and that many other kinds of PSF exist. By using the Fraunhofer diffraction integral and integrating over a circular aperture (the objective lens) with radius a, the Airy pattern’s field is calculated as a function of the angle θ between the optical axis and the line from the center of the aperture to the observation point (see the figure in the diffraction section for a visual depiction of θ). The function for the Airy pattern’s intensity can be computed by squaring the equation of the field. Here, J1 represents a type of function called a Bessel function of the first kind of order one. I0 is the maximum intensity at the Airy pattern’s center.

eq36

The reason the Fraunhofer integral can be used is that the refraction of the spherical wave into a plane wave makes the component waves parallel such that the image plane is effectively at the far field. Also note that the radial symmetry of the Airy pattern allows for I(θ) to describe the pattern in 3D despite only depending on a single variable.

Since microscope images are made up of a tapestry of PSFs as mentioned above, PSFs are useful in helping to describe resolution. As a type of PSF, the Airy pattern is often used to help calculate resolution. The circular region of the Airy pattern with a radius defined by the distance between the intensity distribution’s central maximum and its first zero is called the Airy disk.

If two point objects are farther from each other than the radius of the Airy disk dR (which depends on wavelength and numerical aperture, they are perceivable asFig. 15 separate objects. This principle is used as a measure of lateral resolution (x and y directions) and is called the Rayleigh criterion. Note that there are also other measures of lateral resolution such as the Sparrow limit.

When the light source illuminating the object uses coherent light (where waves exhibit the same frequency and a constant phase difference), the Rayleigh criterion is given by the following equation. Laser-based light sources and situations in which the condenser aperture is closed to produce a pointlike light source are the most common type of coherent light sources used in microscopy. In the case of the narrow condenser aperture, the numerical aperture of the condenser is sometimes approximated as zero.

eq37

In the cases where incoherent light is involved, the situation is different since the light between two adjacent points does not interfere. This happens when the sample itself emits light (as in fluorescence microscopy) or when the numerical aperture of the condenser lens is greater than or equal to the numerical aperture of the objective lens, the Rayleigh criterion is given by the following equation.

eq38

Measuring axial resolution in a manner that is consistent with the Rayleigh criterion requires using the intensity distribution of light along the optical axis (z direction). This axial interference pattern is typically hourglass shaped. However, it still exhibits a central maximum as well as regions of minimum intensity along the z axis. The distance between the central maximum and the first minimum is used as an approximate measure of axial resolution. This distance can be computed using the following equation. Here, n is the refractive index of the immersion medium between the specimen and the objective lens.

eq39

As mentioned earlier, the size of the individual photodetector elements in a microscope’s detector array determines the maximum possible level of image detail. To collect all of the available information from an image, the size dd of each photodetector element must be at most half the resolution or radius dR of the Airy disk in the image plane (that is, dd ≤ dR/2). Because the microscope’s magnification makes the specimen appear larger, it also multiplies the apparent radius of each Airy disk. This means that a higher magnification factor will cause each Airy disk to cover a larger number of photodetector elements (and pixels). As such, the minimum magnification factor necessary to make it so that dd ≤ dR/2 is given by the following equation.

eq40

Fig. 16

Lens aberrations

Because the refractive index of a lens is wavelength dependent, different colors of light passing through the lens refract at different angles and create distinct focal planes. ThisFig. 10 causes lens aberrations that are known as chromatic aberrations.

Most lenses have spherical curvature since lenses with other types of curves are much more difficult to manufacture. Note that this does not necessarily mean these lenses are entirely spherical, just that the curved surfaces on the lenses could act as parts of a sphere. Unfortunately, light refracts at different angles towards the edges of these kinds of lenses. This is a result of the angle of incidence for parallel rays changing with the increasing curvature near the edges. With different angles of refraction, distinct focal planes occur, producing lens aberrations called spherical aberrations.

Any curved lens does not produce a flat focal plane, but rather a bowl-shaped focal region. Since microscopy often involves imaging samples with mostly flat geometry (e.g. samples on glass slides), this bowl-shaped focal region leads to a type of aberration known as a field curvature aberration.

When there is a disparity between the focal distances of rays passing through the vertical and axis of a lens and the horizontal axis of the lens, it leads to poorer image quality, focuses point objects as streaks, and is called astigmatism. This aberration sometimes comes from differences in vertical curvature and horizontal curvature of a lens due to imperfections in manufacturing. But even in a lens with perfect symmetry, when the object under observation is located away from the optical axis, a form of astigmatism called oblique astigmatism can occur (which has the same effect of creating distinct focal planes for the horizontal and vertical axes of the lens). The further the object is from the optical axis, the more severe the oblique astigmatism.

Slightly tilted lenses can result in an aberration known as coma. With coma, a cometlike blur oriented either away from the optical axis (positive coma) or towards the optical axis (negative coma) occurs.

Types of objective lenses

To correct for chromatic aberrations which arise between two wavelengths of light (usually blue and red), compound lenses made from materials with different dispersion properties are used. These lenses are called achromatic lenses (or achromats)Fig. 11. Dispersion refers to the wavelength dependence of refractive index. Achromatic objective lenses typically employ convex lenses made from crown glass fused to concave lenses made from flint glass, though the entire objective often involves more than just this lens doublet. The differences in dispersion (and therefore refraction) resulting from the crown glass and flint glass cancel each other out, correcting for chromatic aberrations in two wavelengths and creating a single focal plane for both of these wavelengths of light. In addition, achromatic objectives correct for spherical aberrations in a single wavelength which lies between two chromatically corrected wavelengths.

When correcting for chromatic aberrations between blue, red, and green wavelengths, a fluorite lens is often employed. Fluorite objectives are typically similar to achromatic objectives (in that they include fused convex and concave lenses), but they use convex lenses made from fluorite glass rather than flint glass. Though usually more expensive than achromats, the dispersion properties of the convex fluorite glass is better able to complement the material of the concave lens, leading to a single focal plane for red, blue, and green light.

Apochromatic objectives are very expensive and combine many individual lenses so as to correct for chromatic aberrations in four or even five wavelengths such as red, green, blue, near-UV, and near-infrared. They are also typically corrected for spherical aberration in green and blue wavelengths. Some apochromatic objectives are constructed to exhibit minimal field curvature and are called plan-apochromats.

There are also special objective lens designs for various applications. Some examples include long working distance objective lenses, immersion objectives (immersion of an objective in liquid often creates a closer refractive index match to the lens material and improves image quality), and UV-transparent objectives to support uses that involve ultraviolet light.

Mirrors

Many optical microscopy setups are complicated and require mirrors to direct light to the necessary locations. Mirrors are also used for many other purposes in microscopy (e.g. curved mirrors can spread light out, etc.) Reflectance, the ratio of reflected light to incident light, is used to quantitatively evaluate the performance of mirrors. The angle at which a ray of light undergoes reflection is equal to the incident ray’s angle relative to the normal of the mirror’s surface. The reflected ray travels away from the mirror on the opposite side of the normal.

Metallic mirrors are made by coating a substrate with a thin layer of metal. The electrons in metallic substances are not bound to any one atom, so they are free to move through a metal’s volume. As such, metals electromagnetic fields induce oscillations in these free electrons. The oscillations cause reemission of the wave with a 180° phase shift relative to the incident wave, leading to destructive interference along the direction of the incident wave’s propagation. Due to the destructive interference, the reflected wave does not transmit into the metal, only out from the surface.

Many metallic mirrors can achieve 90-95% reflectance depending on what material they are made from. However, there are tradeoffs since a metallic mirror’s reflectance can rapidly decrease outside of a certain wavelength range. For instance, silver shows around 95% reflectance across visible and infrared spectra but drastically lower reflectivity in the ultraviolet region. The reflectance R of a metallic mirror can be computed (as a proportion) using the equation below where n is the metallic coating’s refractive index and ε is the metallic coating’s molar extinction coefficient. The molar extinction coefficient is a measure of how strongly a material attenuates light at a given wavelength.

eq47

It should also be noted that the molar extinction coefficient can be found experimentally by using absorption spectroscopy and applying the Beer-Lambert law. The Beer-Lambert law is given below where L is the optical pathlength (the distance that light travels from one side of the sample to the other side), c is the molar concentration of the substance, and A is the measured absorbance.

eq48

Dielectric mirrors take advantage of the fact that nonmetallic materials exhibit some reflectivity. To make a dielectric mirror, alternating layers of materials with low and high refractive indices are deposited on top of a substrate. Partial reflection occurs at the interfaces between the layers. Because the many layers each contribute some reflection, the total reflectance of a dielectric mirror can exceed 99%. Dielectric mirrors are oftenFig. 17 used with laser sources.

For a dielectric mirror, the thicknesses and refractive indices of its layers allow tuning of the mirror’s properties. Some designs can reflect only a narrow range of wavelengths while others can reflect a broad range of wavelengths.

Dielectric mirrors that reflect a specific wavelength (or range of wavelengths) are often designed such that the thickness times the refractive index of each layer equals one quarter of the target wavelength. Layers with alternating low and high refractive indices are still used, so the thicknesses are varied accordingly. As a result, constructive interference occurs among the partially reflected waves at the interfaces between each layer, adding up to achieve a high level of reflectance. It should be noted that dielectric mirrors are sensitive to the angle of incidence and therefore require correct positioning to function properly.

One useful type of dielectric mirror is a dichroic mirror. These dichroic mirrors reflect a designated range of wavelengths and transmit colors (through the mirror) which fall outside of this range. To do this, the thicknesses and refractive indices of the layers are adjusted so as to cause constructive interference which reinforces wavelengths that fall within the desired range.

Though mirrors are often flat, curved mirrors are also common in microscopy. They are used to bend the paths taken by light, for focusing light, to magnify or reduce images, and other applications. Curved mirrors come in a variety of shapes (e.g. with spherical, parabolic, or hyperbolic curvature) and can have concave or convex geometry.

Curved mirrors can behave similarly to lenses, reflecting light rays to converge at a certain focal distance f. As a result, the mirror equations are identical to the thin lens equations (see below). Recall that this assumes the paraxial approximation. When the object or image is in front of the mirror the value of dobj or dimg is positive. When the object or image is behind the mirror the value of dobj or dimg is negative. Using these sign conventions along with ray tracing methods, the mirror equation can be applied to both concave and convex mirrors.

eq41

There are several useful concepts to note with regards to the properties of particular types of curved mirrors. Spherical mirrors have a focal length equal to half the radius of curvature. Concave cylindrical mirrors reflect light into a linear focal plane. Concave paraboloidal mirrors focus plane waves into a point source and can also convert a point source into a plane wave.

Sources

For fluorescence microscopes, there are usually two light sources. One is a standard halogen lamp that allows a user to view the specimen using light transmission rather than fluorescence. The other is a more specialized and much brighter light source used for exciting fluorophores, sometimes a mercury arc lamp, a xenon arc lamp, a metal halide lamp, or a laser source.

Mercury arc lamps, xenon arc lamps, and metal halide lamps emit light across a wide range of spectra, though they each exhibit some specific differences in their emission ranges and peaks as seen in the following figure. It should be noted that metal halide lamps have a longer lifetime than mercury arc lamps and xenon arc lamps. Unfortunately, these types of light source do not emit light with constant intensity in space and time. Photons undergo emission in bunches rather than in a uniform series, fluctuations in the lamp’s temperature and in the lamp’s electric current can cause emission inhomogeneities, and external electric fields can interfere with the uniformity of emission. These issues can sometimes cause problems in quantitative microscopy.

Fig. 18

Lasers provide more stable illumination. Lasers that emit single wavelengths as well as lasers that emit a broad spectrum of wavelengths are available. Lasers use a process called stimulated emission to produce light. Stimulated emission involves external electromagnetic radiation causing excited electrons to transition to their ground states more frequently than they would otherwise, causing emission of photons. An important property of stimulated emission is that the emitted photons exhibit the same direction, frequency, phase, and polarization as the incident electromagnetic radiation.

 

References

Boudoux, C. (2017). Fundamentals of Biomedical Optics. Blurb, Incorporated.

Degiorgio, V., & Cristiani, I. (2015). Photonics: A Short Course. Springer International Publishing.

Guenther, R. D. (2019). Modern Optics Simplified. Oxford University Press.

Hecht, E. (2017). Optics. Pearson Education, Incorporated.

Kubitscheck, U. (2017). Fluorescence Microscopy: From Principles to Biological Applications. Wiley.

Murphy, D. B., & Davidson, M. W. (2012). Fundamentals of Light Microscopy and Electronic Imaging. Wiley.

Cover image source: Nature

Primer on the Biology of the Cerebral Cortex


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PDF version: Primer on the Biology of the Cerebral Cortex

Cortex cytoarchitecture

On the axial dimension, the cerebral cortex is partitioned into six layers of cells. Layer 1 of the cerebral cortex is the most superficial and layer 6 is the deepest relative to the surface of the brain. Layers 2 and 3 are often categorized together as layer 2/3. Laterally, the cerebral cortex is tiled by distinct regions called cortical columns which act as repeating units of similar function.

Layer 1 and layer 2/3 are called the supragranular layers and mostly perform intracortical computations. Layer 4 is called the granular layer and functions primarily to receive thalamic input. Layers 5 and 6 are called the infragranular layers and mainly send outputs to other parts of the brain.

Though the majority of cortical cells are excitatory pyramidal neurons, there are also many inhibitory interneurons which are crucial to cortical function. There are many morphologically distinct classes of inhibitory interneurons. Pyramidal cells also exhibit structural diversity, though their morphological differences are less often discussed in an explicit fashion.

Canonical cortical microcircuit 

The canonical cortical microcircuit is a model for some of the most frequently occurring patterns of cortical connectivity and function. Some neuroscientists hypothesize that most cortical circuits are variations of a canonical microcircuit. Though different authors have suggested somewhat different versions of the canonical cortical microcircuit model, there are some common patterns as follows.

According to the canonical cortical microcircuit model, strong thalamic inputs first arrive at layer 4 and stimulate both the layer’s inhibitory interneurons and its excitatory pyramidal neurons. Next, these layer 4 neurons transmit information to layer 2/3 pyramidal cells which in turn send excitatory projections to layers 5 and 6 (as well as to layer 1). Layers 5 and 6 send both excitatory and inhibitory signals to other parts of the brain (e.g. back to the thalamus, to the brainstem, etc.) Layers 5 and 6 also send both excitatory and inhibitory feedback to layer 4.

Fig.1

Cortical hierarchy and extrinsic connections

Links among neurons within a cortical column are referred to as intrinsic connections while links between neurons in different cortical columns are called extrinsic connections. Though the majority of cortical connections are intrinsic, the extrinsic connections often stimulate (via activation and inhibition) their targets more strongly.

There are three types of extrinsic connections; feedforward, feedback, and lateral connections. Feedforward connections are from supragranular pyramidal cells to layer 4, feedback connections are from infragranular pyramidal cells to any layer except layer 4 (usually the supragranular layers), and lateral connections are between cells in the same layer. By using this laminar system, a cortical hierarchy emerges with lower cortical areas such as V1 sending information to higher cortical areas such as the dorsal and ventral visual streams via feedforward connections.

Fig.2

Feedback connections are usually inhibitory and can be either modulatory or driving. Though only excitatory lateral connections are shown in the figure above, lateral connections vary in whether they are excitatory, inhibitory, modulatory, or driving. Modulatory connections produce weak metabotropic and ionotropic responses while driving connections produce strong ionotropic responses.

Extrinsic connections are mostly from excitatory glutamatergic neurons, yet feedback connections are usually inhibitory. As such, it is thought that feedback connections typically involve excitatory neurons stimulating layer 1 cells which subsequently send inhibitory projections to layer 2/3 pyramidal cells.

Feedforward connections are excitatory and driving. Feedforward connections might operate as either corticocortical links or as transthalamic links. Transthalamic refers to when a cortical projection goes to the thalamus and then the postsynaptic thalamic neuron sends an axon back to some other part of the cortex.

Reference: Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012). Canonical Microcircuits for Predictive Coding. Neuron, 76(4), 695–711. https://doi.org/https://doi.org/10.1016/j.neuron.2012.10.038

Cover image: modified from Innovations Report article on Motta et al.’s paper

Notes on x-ray physics


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PDF version: Notes on x-ray physics – Logan Thrasher Collins

Thomson scattering and Compton scattering

  • Electrons are the main type of particle that can scatter x-rays. Elastic or Thomson scattering occurs when a non-relativistic electron is accelerated by the electrical component of an incoming electromagnetic field from an x-ray. The accelerated electron then reradiates light at the same frequency. Since the frequency of the input light and output light are the same, this is an elastic process.
  • The intensity of the re-emitted radiation at an observer’s location depends on the angle Χ between the incident light and the observer. Because of the sinusoidal wave character of light, the scattered intensity at the observer’s location is given by the proportionality equation below.

Eq.1

  • Light that encounters the electron is scattered if it is incident on the region defined by the electron’s classical radius. This region is called the Thomson scattering length r0. For a free electron, r0 = 2.82×10-5 Å.

Fig.1

  • Compton scattering occurs when an electron scatters a photon and the scattered photon has a lower energy than the incident photon (an inelastic process). For Compton scattering, a fraction of the incident photon’s energy is transferred to the electron.

Fig.2

  • The amount of energy lost via Compton scattering where the incident photon has energy E0 = hc/λ0 and the scattered photon has energy E1 = hc/λ1 is described by the following equation. Here, ψ represents the angle between the paths of the incident photon and the scattered photon.

Eq.2

Scattering from atoms

  • X-rays are scattered throughout the volumes of atomic electron clouds. For x-rays that scattered in the same direction as the incident x-rays, the strength of scattering is proportional to the atom’s Z-number. In the case of an ionic atom, this value is adjusted to equal the atom’s number of electrons. Note that this assumes free electron movement within the cloud.
  • By contrast, x-rays that are scattered at some angle 2θ relative to the incident x-rays exhibit lower scattering magnitudes. Each of the x-rays scattered at angle 2θ will possess different magnitudes and phases depending on where they were scattered from within the atomic cloud. As a result, the scattering amplitude for the x-rays at angle 2θ will be a vector sum of these waves with distinct magnitudes and phases.

Fig.3

  • A wavevector k is a vector with magnitude 2π/λ that points in the direction of a wave’s propagation. The difference between the wavevector of the incident wave k0 and the wavevector of the scattered wave k1 is equal to a scattering vector Q (that is, Q = k0k1). The magnitude of Q is given by the following equation.

Eq.3

  • The atomic scattering factor f describes the total scattering amplitude for an atom as a function of sin(θ)/λ. By assuming that the atom is spherically symmetric, f will depend only on the magnitude of Q and not on its orientation relative to the atom. Values for f can be found in the International Tables for Crystallography or computed using nine known coefficients a1,2,3,4, b1,2,3,4, and c (which can also be looked up) and the following expression. The coefficients vary depending on the atom and ionic state. The units of f are the scattering amplitude that would be produced by a single electron.

Eq.4

  • If the incident x-ray has an energy that is much less than that of an atom’s bound electrons, the response of the electrons will be damped due to their association with the atom. (This no longer assumes free electron movement within the cloud). As a result, f will be decreased by some value fa. The value fa increases when the incoming x-ray’s energy is close to the energy level of the electron and decreases when the incoming x-ray’s energy is far above the energy levels of the electrons.
  • When the incident x-ray’s energy is close to an electron’s energy level (called an absorption edge), the x-ray is partially absorbed. With this process of partial absorption, some of the radiation is still directly scattered and another part of the radiation is re-emitted after a delay. This re-emitted radiation interferes with the directly scattered radiation. To mathematically describe the effect of the re-emitted radiation’s phase shift and interference, f is adjusted by a second term fb (which is an imaginary value). Far from absorption edges, fb has a much weaker effect (it decays by E-2). The total atomic scattering factor is then given by the following complex-valued equation.

Eq.5

Refraction, reflection, and absorption

  • A material’s index of refraction can be expressed as a complex quantity nc = nRe + inIm. The real part represents the rate at which the wave propagates through the material and the imaginary part describes the degree of attenuation that the wave experiences as it passes through the material.
  • The reason that a material can possess a complex refractive index involves the complex plane wave equation. The wavenumber k = 2π/λ0 is the spatial frequency in wavelengths per unit distance and it is a constant within the complex plane wave equation (λ0 is the wave’s vacuum wavelength). The complex wavenumber kc = knc is the wavenumber multiplied by the complex refractive index. As such, the complex refractive index can be related to the complex wavenumber via kc = 2πnc0 where λ0 is the vacuum wavelength of the wave. After inserting 2π(nRe + inIm)/λ0 into the complex plane wave equation, a decaying exponential can be simplified out as a coefficient for the rest of the equation. The decaying exponential represents the attenuation of the wave in the material. Once this simplification is performed, the equation’s complex wavenumber is converted to a real-valued wavenumber.

Eq.6

  • For x-rays, a material’s complex refractive index for wavelength λ is related to the atomic scattering factors of atoms in the material using the following equation. Ni represents the number of atoms of type j per unit volume and fj(0) is the atomic scattering factor in the forward direction (angle of zero) for atoms of type j. Recall that r0 is the Thomson scattering length.

Eq.7

  • The refractive index is a function of the wavelength. For most optical situations, as the absorption maximum of a material is approached from lower frequencies, the refractive index increases. But when the radiation’s frequency is high enough that it passes the absorption maximum, the refractive index decreases to a value of less than one.
  • The refractive index is defined by n = c/v, where v is the wave’s phase velocity. Phase velocity is the rate at which a wave’s phase propagates (i.e. how rapidly one of the wave’s peaks moves through space). Rearranging the equation, v = c/n is obtained. When the refractive index is less than one, the phase velocity is greater than the speed of light. However, this does not violate relativity because the group velocity (not the phase velocity) carries the wave’s energy and information. For comparison, group velocity is the rate at which a change in amplitude of an oscillation propagates.
  • Anomalous dispersion occurs when the radiation’s frequency is high enough that the refractive index of a material is less than one. As a result, x-rays entering a material from vacuum are refracted away from the normal of the refracting surface. This is in contrast to the typical case where the radiation would be refracted toward the normal of the refracting surface. In addition, the refracted wave is phase shifted by π radians.
  • The complex refractive index is often expressed using the equation below. Here, δ is called the refractive index decrement and β is called the absorption index. Note that nRe = 1 – δ and nIm = β (as a comparison to the previously used notation). Recall that nIm = β describes the degree of a wave’s attenuation as it moves through a material.

Eq.8

  • The refractive index decrement can be approximately computed using the average density of electrons ρ, the Thomson scattering length r0, and the wavenumber k = 2π/λ0. Note that this approximation is better for x-rays that are far from an absorption edge.

Eq.10

  • With most materials, the resulting real part of the index of refraction is only slightly less than one when dealing with x-rays. For example, a typical electron density of one electron per cubic Angstrom yields a δ value of about 5×10-6.
  • Snell’s law applies to the index of refraction for x-rays and is given as follows.

Eq.11

  • Because the index of refraction for x-rays is slightly less than one, total external reflection can occur when x-rays are incident on a surface at angles less than the critical angle θcritical. This stands in contrast with the total internal reflection that commonly occurs with visible light.

Eq.12

  • The critical angle can be approximated with a high level of accuracy using the following equation (derived from the Taylor expansion of the cosine function). With typical values of δ on the order of 10-5, θcritical is often equal to just a few milliradians (or a few tenths of a degree). These small angles relative to the surface are called grazing angles.

Eq.13

  • Because grazing incident angles facilitate x-ray reflection, special curved mirrors can be used to focus x-rays. The curvature of these mirrors must be small enough that the steepest incident angle is less than θcritical. It should be noted that, even when undergoing total external reflection, x-rays do penetrate the reflecting material to a depth of a few nanometers via an evanescent wave.

Fig.4

  • The absorption index β is related to the value fb using the following equation where r0 is the Thomson scattering length. Recall that fb represents the effects of scattering from absorption and remission of x-rays with energies that are close to the absorption edges of a material.

Eq.14

  • Using the process explained earlier for computing the decaying exponential exp(-2πnImx/λ0) that represents the attenuation of a wave’s amplitude as it travels through a material, the decay of a wave’s intensity as it travels through a material can also be found. Recall that λ0 is the wavelength in a vacuum. Because intensity is proportional to the square of the amplitude, the equation below describes the exponential decay of a wave’s intensity in a material. (This decaying exponential function is multiplied by the equation of the wave). Here, μ is called the absorption coefficient and is defined as the reciprocal of the thickness of a material required to decrease a wave’s intensity by a factor of 1/e. The absorption coefficient is a rough indication of a material’s electron density and electron binding energy.

Eq.15

  • The correspondences between the atomic configurations associated with an x-ray absorption edge and the commonly used name for said absorption edge are given in the following table. The subscripts used with the configurations represent the total angular momenta.

Table1

X-ray fluorescence and Auger emission

  • Materials fluoresce after bombardment with x-rays or high-energy electrons. If electrons are used, the emitted light consists of Bremsstrahlung radiation (which comes from the deacceleration of the electrons) and fluorescence lines. The Bremsstrahlung radiation includes a broad spectrum of wavelengths and has low intensity while the fluorescence lines are sharp peaks and exhibit high intensity. If x-rays are used to bombard a material, there is no Bremsstrahlung radiation, but fluorescence lines occur.
  • Different materials exhibit different characteristic fluorescence lines. These x-ray fluorescence lines are caused by outer-shell electrons relaxing to fill the holes left after the ejection of photoelectrons. However, not all electronic transitions are allowed, only those which follow the selection rules for electric dipoles. These selection rules are given below. J is the total angular momentum and can be computed from the sum of the Azimuthal quantum number L (which determines the type of atomic orbital) and the spin quantum number S (which determines the direction of an electron’s spin).

Eq.16

  • The nomenclature for x-ray fluorescence lines is based on the shell to which an electron relaxes. If an excited electron relaxes to the 1s shell state, then the fluorescence line is part of the K series. For an excited electron that relaxes to the 2s or 2p state, the fluorescence line is part of the L series. The M series includes relaxations to 3s, 3p, and 3d. The N series includes relaxations to 5s, 5p, 5d, and 5f. As such, the Azimuthal quantum number determines if the fluorescence line falls into the K, L, M, or N series (there are some series beyond these as well which follow the same pattern). The transition within each series that exhibits the smallest energy difference is labeled with α (i.e. Kα), the transition with the next smallest energy difference is labeled with β, and so on. It should be noted that the fluorescence lines are further split by the effects of electron spin and angular momentum and so are labeled with suffixes of 1, 2, etc.
  • Auger emission is the process where a photoelectron is ejected, an outer shell electron relaxes to fill the hole, and the released energy causes ejection another electron instead of emitting a photon. The energies of emitted Auger electrons are independent of the energies of the incident photons.
  • The excess energy released by the relaxation of the outer shell electron is equal to |Ecore – Eouter|. In order for the last electron ejection to occur, the electron must have a binding energy that is less than the excess released energy from the relaxation. The kinetic energy of the ejected Auger electron is |Ecore – Eouter – Ebinding|. Note that Ebinding is the binding energy of the Auger electron in the ionized atom (which is different from the binding energy in the neutral form of the atom).
  • Auger emission and x-ray fluorescence are competitive with each other. Fluorescence is stronger for heavier atoms (higher Z-number) since they exhibit larger energy differences between adjacent shells as well as binding electrons more tightly. For the same reasons, Auger emission is stronger from atoms with lower Z-numbers.

Fig.5

 

Reference: Willmott, P. (2011). An Introduction to Synchrotron Radiation: Techniques and Applications. Wiley.

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