Author: logancollins

Notes on Honeybee Sensory Neurobiology

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PDF version: Notes on Honeybee Sensory Neurobiology – Logan Thrasher Collins


Antennal lobes

  • Honeybee antennal lobes (ALs) are composed of about 160 regions called glomeruli in which olfactory receptor neurons from the antennae make synapses on projection neuron cell bodies as well as inhibitory local neurons.
  • The projection neurons send cholinergic axons to the mushroom bodies and to the lateral horn (LH) while the GABAergic local neurons facilitate olfactory computations within the antennal lobes.

Mushroom bodies

  • The mushroom bodies are paired structures located on either side of the central brain (CB). They are known to facilitate higher sensory integration as well as associative learning processes.Fig. 1
  • In honeybees, the mushroom bodies use cup-shaped medial calyces (MCAs) and lateral calyces (LCAs) as their major sensory input regions while using the pedunculi (PEDs) as their major sensory output regions.
  • The calyces contain Kenyon cells which receive cholinergic axons from the projection neurons of the antennal lobes and the pedunculi contain the efferent axons of the Kenyon cells.

Associative olfactory learning

  • Honeybee associative olfactory learning can occur where the olfactory pathway converges with other pathways.Fig. 2
  • Specific odors can serve as conditioned stimuli when they are associated with unconditioned stimuli of appetitive or aversive character.
  • Experimental evidence shows that the VUMmx1 neuron is sufficient for olfactory reward learning in bees. Its cell body is located within a region called the subesophageal ganglion and it synapses upon cells in the calyces, the lateral horn, and the antennal lobe.


Honeybee eyes

  • Honeybees possess two frontal compound eyes and three ocelli (simple eyes) located on the top of the head.
  • The retinas of honeybee compound eyes are composed of ommatidia, each with nine photoreceptor cells. The types of bee photoreceptor cells include S, M, and L photoreceptors corresponding to UV, blue, and green wavelengths respectively.
  • Ocellar retinas are composed of rod cells (note that they do not have ommatidia) and are covered by a lens. However, the focal plane of this lens is behind the actual retina, leading to much lower resolving power than that of the compound eyes. Although the function of ocelli is not entirely understood, they may operate as widefield detectors of illumination changes. In addition, ocellar retinas can be subdivided into dorsal and ventral regions which view the horizon and the sky respectively. Distinct neuronal pathways are associated with these subdivisions.

Optic lobe

  • Honeybee vision (associated with the compound eyes) starts with the optic lobe’s three regions; the lamina (La), medulla (Me), and lobula (Lo).
  • The lamina is positioned directly under the compound eye’s photoreceptors. It receives inputs mainly from the L photoreceptors, which are involved in the achromatic pathway and exhibit fast response times. However, some very rough color processing may still occur in the lamina. Fig. 3
  • In the medulla, neurons are organized in a columnar retinotopic fashion with eight layers. The columns also possess horizontal connections (unlike the lamina) which likely facilitate color opponency. The medulla’s outer layers contain neurons that respond to specific wavelengths and neurons that respond to a broad range of wavelengths while the medulla’s inner layers contain color-opponent neurons that compare colors at center and surround regions of receptive fields.
  • The lobula consists of six layers. Its outer layers (1-4) are part of the achromatic pathway and exhibit motion sensitivity. Its inner layers (5-6) continue the color processing pathway. Some projections from the inner layers go to the mushroom bodies, possibly facilitating sensory crosstalk and learning.
  • Beyond the optic lobe, further visual processing of the achromatic and color pathways occurs in the protocerebrum and central brain.

Audition and antennal somatosensation

Johnston’s organ

  • Honeybees use Johnston’s organ as their sensory organ for audition. In bees, audition also acts as a form of somatosensation. Johnston’s organ is located on the antennae. It detects vibrations during the waggle dance and air currents during flight. Fig. 4
  • Johnston’s organ contains about 240 scolopidia, mechanosensory complexes which include bristles that deform and trigger action potentials along efferent axons.
  • The soma of neurons within Johnston’s organ are divided into dorsal (dJO), ventral (vJO), and anterior groups (aJO).

Projections from Johnston’s organ

  • The main axons from the soma within Johnston’s organ trifurcate into the fascicles called T6I, T6II, and T6III. The T6I axons terminate at the ventro-medial superior posterior slope (vmSPS), the T6II axons terminate at the antennal mechanosensory and motor center (AMMC), and the T6III axons terminate at the ventro-central superior posterior slope (vcSPS). Fig. 5
  • In the vmSPS, the axons show some degree of somatotopy arising from the dorsal, ventral, and anterior Johnston’s organ regions. Somatotopy is not observed in the AMMC or vcSPS.
  • All the sensory axons from Johnston’s organ also send small collateral branches to the bee’s dorsal lobe (DL).


  • The AMMC contains two classes of interneuron, AMMC-Int-1 and AMMC-Int-2. AMMC-Int-1 neurons have somas located in the honeybee’s primary auditory center, which is near the central brain. They densely arborize at the AMMC and thinly arborize in the ventral protocerebrum (the protocerebrum is a region of the insect brain that includes the mushroom bodies and central brain as well as several other structures). Their dense arborization in the AMMC runs close to the T6 collaterals at the dorsal lobe.
  • AMMC-Int-1 neurons demonstrate spontaneous spiking without sensory input.Fig. 6 During exposure to a vibratory stimulus, the spike rate slows slightly. After the stimulus is removed, the spike rate increases to a higher rate than that of the spontaneous spiking, but eventually returns to the basal rate. However, it should be noted that olfactory stimuli and other modulating factors can drastically alter the response properties of AMMC-Int-1 neurons.
  • AMMC-Int-2 neurons have somas located in the dorsal lobe. Their dendrites split into three main branches called x, y, and z. Branch y is the axon while branches x and z are dendritic. It sends a long process to the lateral protocerebrum (LP) and makes synapses. The x arborization represents the densest of the three branches and is located in the AMMC. Branch z passes through the dorsal lobe and into the lateral superior posterior slope (lateral SPS). Fig. 7
  • AMMC-Int-2 neurons respond to relatively high vibratory amplitudes, especially those which cause 30 μm (or greater) shifts in antennal position. Their sensitivity reaches a maximum at 265 Hz (a frequency that occurs during the waggle dance), though they also respond to other frequencies.


  • The SPS contains an interneuron known as SPS-D-1 which projects to the ipsilateral and contralateral SPS.
  • SPS-D-1 does not respond to 265 Hz alone. However, it responds to long-lasting 265 Hz vibratory stimulation with simultaneous olfactory stimulation at the contralateral antenna.


Gustatory sensilla

  • Gustatory receptor cells are found in sensilla, structures which resemble hairs or pegs. Sensilla are located on the glossa, antennae, labial palps, and several other parts of the bee’s body. Fig. 8
  • Each sensillum contains 3-5 gustatory receptor neurons that send dendrites up the shaft towards a pore at the sensillum’s tip. The somas of the receptor cells (along with a mechanoreceptor cell) are encapsulated by auxiliary cells and bathed in a receptor hemolymph. The gustatory receptor neurons likely use GPCRs to detect various food molecules while the mechanoreceptor facilitates evaluation of the food’s position and density.
  • Antennal sensilla respond in a dose-dependent and highly sensitive manner to sucrose solutions. In addition, antennal sensilla respond to aqueous NaCl. As the antennal sensilla do not respond to very low concentrations of KCl, they probably do not contain a receptor that responds to water alone (unlike in many other insects). Sensilla on the mouthparts respond to aqueous sucrose, glucose, fructose, LiCl, KCl, and NaCl. They do not respond to CaCl2 or MgCl2. Foreleg sensilla respond to sucrose as well as very low concentrations of KCl, suggesting that these sensilla may contain a receptor that responds to water alone (unlike the bee’s other sensilla).

Honeybee central gustatory processing

  • Honeybee central gustatory processing takes place primarily in their subesophageal ganglion (SEG). Axons of gustatory neurons and the mechanosensory neurons found in the sensilla project to the SEG’s mandibular, maxillary, and labial neuromeres via the mandibular, maxillary, and labial nerves respectively.
  • As mentioned, the SEG contains the VUMmx1 neuron, which facilitates pairing of olfactory and gustatory stimuli for reward learning. Other VUM neurons have been identified in the SEG, but their function remains unclear.
  • Beyond the SEG, other neurons might be involved in the honeybee’s gustatory processing. In the mushroom bodies, the PE1 neuron exhibits increased spiking in response to sucrose gustation. However, PE1 also responds to mechanical and olfactory inputs. Also located in the mushroom bodies are cells dubbed as “feedback neurons” which respond to odors and sucrose as well. In these cases, multisensory integration likely occurs.

With the exception of image created for the section “projections from Johnston’s organ,” images were modified from: (Steijven, Spaethe, Steffan-Dewenter, & Härtel, 2017), (R. Menzel, 2012), (Kiya & Kubo, 2011),  and (Galizia, Eisenhardt, & Giurfa, 2011).


Dyer, A. G., Paulk, A. C., & Reser, D. H. (2011). Colour processing in complex environments: insights from the visual system of bees. Proceedings of the Royal Society B: Biological Sciences, 278(1707), 952 LP-959. Retrieved from

Galizia, C. G., Eisenhardt, D., & Giurfa, M. (2011). Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel. Springer Netherlands.

Heisenberg, M. (2003). Mushroom body memoir: from maps to models. Nature Reviews Neuroscience, 4, 266. Retrieved from

Hung, Y.-S., & Ibbotson, M. (2014). Ocellar structure and neural innervation in the honeybee. Frontiers in Neuroanatomy. Retrieved from

Kiya, T., & Kubo, T. (2011). Dance Type and Flight Parameters Are Associated with Different Mushroom Body Neural Activities in Worker Honeybee Brains. PLOS ONE, 6(4), e19301. Retrieved from

Menzel, R. (2012). The honeybee as a model for understanding the basis of cognition. Nature Reviews Neuroscience, 13, 758. Retrieved from

Mota, T., Yamagata, N., Giurfa, M., Gronenberg, W., & Sandoz, J.-C. (2011). Neural Organization and Visual Processing in the Anterior Optic Tubercle of the Honeybee Brain. The Journal of Neuroscience, 31(32), 11443 LP-11456. Retrieved from

Sandoz, J.-C. (2013). Chapter 30 – Neural Correlates of Olfactory Learning in the Primary Olfactory Center of the Honeybee Brain: The Antennal Lobe. In R. Menzel & P. R. B. T.-H. of B. N. Benjamin (Eds.), Invertebrate Learning and Memory (Vol. 22, pp. 416–432). Elsevier.

Steijven, K., Spaethe, J., Steffan-Dewenter, I., & Härtel, S. (2017). Learning performance and brain structure of artificially-reared honey bees fed with different quantities of food. PeerJ, 5, e3858.

Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System (journal article)

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My first scientific journal article (I am the lead author)! I came up with the idea for this project during middle school. In high school, I started working at a university laboratory. Over the course of this research, I competed in the International Science and Engineering Fair three times, gave a TEDx presentation, fought through countless obstacles, ignored the naysayers, witnessed the Nobel Prize ceremonies firsthand, and brought my idea to fruition.

ACS Biochemistry: Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System

Local copy (full text): Design of a De Novo Aggregating Antimicrobial Peptide and a Bacterial Conjugation-Based Delivery System

Notes on computer architecture

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PDF version: Notes on computer architecture – Logan Thrasher Collins

Main memory

  • Some computers store data using flip-flop circuits. Each flip-flop circuit possesses a configuration of logic gates (including AND, OR, and NOT gates) that allows Fig.1switching between “on” and “off” states corresponding to 1 and 0.
  • More modern machines often use conceptually similar ways of storing data that involve using tiny electric charges to represent 1 and 0 states.
  • Each memory cell contains eight flip-flop circuits (or similar storage devices) that correspond to eight bits of memory. Together, eight bits are equal to one byte.
  • The memory cell’s eight bits are depicted as arranged in a line. The leftmost end is called the high-order end and the rightmost end is called the low-order end. The leftmost bit is called the most significant bit and the rightmost bit is called the least significant bit.
  • In order for the computer to find specific memory cells within main memory, every cell is assigned a unique numeric address. This can be visualized as a series of memory cells lined up and numbered starting with zero. In this way, individual cells are not only identifiable, but they are also ordered relative to other cells.
  • Since the computer can independently access any cell that is needed for a Fig.2computation (despite the cells possessing an ordered configuration), the main memory is called random access memory (RAM).
  • For computers that use tiny charges (rather than flip-flop circuits) to store data, the main memory is called dynamic RAM or DRAM because the charges are volatile, dissipate quickly, and must be restored many times per second using a refresh circuit.

Central processing unit

  • The central processing unit (CPU) includes an arithmetic unit that performs operations on data, a control unit that coordinates the machine’s activities, and a register unit that temporarily stores results from the arithmetic unit (and other data) in registers. Fig.3
  • The CPU is connected to the main memory (which is more permanent than the registers) via a collection of wires called a bus. To perform an operation on data from the main memory, the CPU uses an electronic address to find the desired data cell and send it to a set of registers. To write data into the proper location within main memory, the CPU uses a similar address system.

The stored program

  • Instructions for the CPU’s data manipulation can be stored in a computer’s main memory because programs and data are not fundamentally distinct entities.
  • The following steps summarize how stored programs operate.
    1. Retrieve a set of values from main memory and place each value within a register.
    2. Activate the circuitry that performs some operation upon the values (i.e. two values might be added together) and then store the result in another register.
    3. Transfer the result from its register to main memory for long-term storage. After this, stop the program.
  • CPUs also store cache memory in order to increase their speed. The cache memory is a temporary copy of the portion of the main memory that is undergoing processing at a given time. Using cache memory, the CPU can rapidly retrieve relevant data without needing to go all the way to the main memory as often.

Machine language

  • Data transfer group: instructions to “transfer” data from a memory cell to a register (or some similar process) are more accurately described as “copying” the data. Requests to copy data from are memory cell to a register are called LOAD instructions. Requests to copy data from a register and write it to a memory cell are called STORE instructions. Requests that control interaction of the CPU and main memory with external devices like printers and keyboards are referred to as I/O instructions.
  • Arithmetic/logic group: the arithmetic/logic unit can carry out instructions that run data through basic arithmetic operations and Boolean logic gate operations (AND, NOT, OR, XOR, etc.) The arithmetic/logic unit also uses the SHIFT and ROTATE instructions. SHIFT moves bits to the left or right within a register. ROTATE is another version of SHIFT which moves bits to the slots at the other end of the register (rather than allowing them to “fall off” as would happen if SHIFT were used).
  • Control group: contains instructions that direct program execution and termination. JUMP (also called BRANCH) commands cause a program to change the next action that it performs. JUMP commands can be unconditional or conditional (when conditional, they work like “if” statements). The STOP command also falls into this category.

Machine cycle

  • The machine cycle involves two special purpose registers, the instruction register and the program counter.
  • The instruction register contains the instruction that is undergoing execution.
  • The program counter contains the address of the next instruction that will be executed and so keeps track of the machine’s place within the program.
  • Using three steps, the CPU performs the machine cycle.
    1. Fetch: the CPU retrieves an instruction from the main memory at the address specified by its program counter. The program counter then increments to specify the next instruction.
    2. Decode: the CPU breaks the instruction into appropriate components based on its operational code.
    3. Execute: the CPU activates the necessary circuitry to perform the command that was requested.
  • The computer’s clock is a circuit that generates oscillating pulses which control the machine cycle’s rate. A faster clock speed results in a faster machine cycle. Clock speed is measured in Hertz. Typical laptop computers (as of 2018) run at clock speeds of several GHz.
  • To increase a computer’s performance, pipelining is often used. Pipelining involves allowing the steps of the machine cycle to overlap. Using pipelining, an instruction can be fetched while the previous operation is still underway, multiple instructions can be fetched simultaneously, and multiple operations can be executed simultaneously so long as they are independent of each other.

Multiprocessor machines

  • Some computers possess multiple CPUs that are linked to the same main memory. This is called a multiple-instruction stream multiple-data stream (MIMD) architecture. The CPUs operate independently while coordinating their efforts by writing instructions to each other on their shared memory cells. In this way, a CPU can request another CPU to perform a specified part of a large processing task.
  • Some computers use multiple CPUs that are linked together so as to perform the same sequence of instructions simultaneously upon distinct datasets. This is called a single-instruction stream multiple-data stream (SIMD) architecture. SIMD machines are useful when the application requires the same task to be performed upon a large amount of data.
  • Parallel processing can also be carried out using large computers that are composed of multiple smaller computers, each with its own CPU and main memory. In these cases, the smaller computers coordinate the partitioning of resources to handle a given task.


Reference and image source: Brookshear, J. G., Smith, D. T., & Brylow, D. (2012). Computer Science: An Overview. Addison-Wesley.

Topics of Interest

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Architecture: antebellum architecture, architecture that incorporates extensive electronic components, architecture that merges seemingly disparate styles, atomic age architectural design, Baroque architecture, bionic architecture, Burj Khalifa, civic engineering, contemporary architecture, contemporary gothic architecture, Googie architecture, gothic architecture, green architecture, industrial architecture, lighting design in architecture, skyscraper design, supertall skyscrapers, urban planning

Art and writing: algorithmic art, antebellum fiction and poetry, aquapunk writing, art that incorporates industrial motifs, atomic age design, Baroque art, bioart, biopunk writing, body modification art, code poetry, contemporary American poetry, contemporary fairy tales, contemporary installation art, cyberart, cyberpoetry, cyberpunk writing, dieselpunk writing, futuristic fashion, Googie art, goth fashion, gothic art, industrial photography, Ken Rinaldo, literary science fiction, literary tales of vampirism, literary tales of witchcraft, Lovecraftian writing, magical realism, middle eastern literature, modern and contemporary American literature, multimedia art, nanopunk writing, Natasha Vita More, Neil Harbisson, Neri Oxman, paleoart, performance art, posthuman art, posthuman fiction and poetry, radical fashion design, retro fashion, retrofuturism, retro science fiction, robots as art, sexuality in literature, Simon Stålenhag, speculative poetry, synthpop and electronic dance music, tattoo art, the femme fatale in literature, the gothic, the literature of monsters, the weird, utopian science fiction, writing inspired by atomic age design, writing that explores machine consciousness, writing that explores the interplay between danger and desire, writing that merges seemingly disparate genres, Yayoi Kusama

Chemistry: chemical kinetics, computational chemistry, computational protein folding, conductive polymers, flux balance analysis, industrial chemical manufacturing, inorganic chemistry, medicinal chemistry, molecular orbital theory, organic supramolecular chemistry, organic synthesis, organometallic chemistry, PEDOT, quantum chemistry, solid state chemistry, statistical thermodynamics

Earth and space sciences: astrobiology, biogeochemistry, black holes, caves, desert ecology, dinosaurs, evolution of early mammals, exotic atmospheric phenomena, galactic superclusters, large-scale structure of the universe, marine biology, Mars, Neptune, ocean chemistry, paraceratherium, physical processes in stars, prehistoric sea creatures, telescopes, troglodytic organisms, tropical ecology, tundra ecology, underwater caves, urban ecology, velociraptors

Economics: American economic system, economics as ecology, economics of healthcare, entrepreneurship, Japanese economics, macroeconomics, mathematical finance, microeconomics, Scandinavian economics

Electrical engineering and computer science: algorithm design, applied probability, computational approaches to solving ordinary and partial differential equations, computational geometry, computer architecture, curve fitting and optimization, embedded systems, flexible electronics, graph algorithms, high-performance computing, k-means clustering algorithms, manifold learning, MATLAB, memristors, microelectronics, nanoelectronics, neuromorphic engineering, nonlinear dimensionality reduction, optoelectronics and photonics, Python, reinforcement learning, RF circuit design, semiconductor devices, supercomputer architecture, supervised learning, unsupervised learning, VLSI design

History: Abrahamic mythology and its ties to historical trends, F. Scott Fitzgerald, historical demonology, historical perspectives on romantic love, historical perspectives on the concept of monsters, historical perspectives on witchcraft, history of American literature, history of American poetry, history of architecture, history of biomedicine, history of chemistry, history of computers and electronics, history of feminism, history of Halloween, history of mathematics, history of middle eastern literature, history of modern and contemporary art, history of science fiction, history of skyscrapers, the Cold War

Humanities: academic perspectives on popular culture, academic perspectives on sanity and insanity, academic perspectives on synthpop and electronic dance music as well as their associated cultures, American politics, culture of the American South, demonology, digital humanities, future studies, gender studies, Japanese politics, perspectives on the relationship between genius and insanity as a psychosocial and cultural phenomenon, posthuman studies, romantic love as a psychosocial and cultural phenomenon, sexuality studies, structure and culture of criminal organizations, studies on contemporary Japanese culture, study of robots as a sociocultural phenomenon, study of vampires as a sociocultural phenomenon, study of witchcraft as a sociocultural phenomenon, queer studies, the femme fatale

Mathematics: abstract algebra, algebraic geometry, algebraic topology, calculus of variations, category theory, chaos theory, combinatorics, complex analysis, complex and hypercomplex geometry, convexity, differentiable and smooth manifolds, differential forms, differential geometry, dynamical systems, exotic spheres, Fourier analysis, fractal geometry, fractional calculus, functional analysis, geometric analysis, graph theory, group theory, harmonic analysis, high-dimensional sphere packing, hypercomplex numbers, independent component analysis, integral equations, knot theory, linear programming, measure theory, number theory, numerical analysis, ordinary and partial differential equations, principal component analysis, probability theory, random graphs, real analysis, set theory, stochastic geometry, surgery theory, tensors, tools from linear algebra, topological data analysis, topological manifolds, topology, tropical geometry, very large numbers

Molecular and synthetic biology: Adeno-associated viruses, bioactive natural products, bioinformatics, cardiac developmental biology, cardiac molecular biology, catalytic and regulatory RNAs, cell signaling, cell-based therapeutics, cytoskeleton, directed evolution, engineering logic gates using synthetic gene regulatory pathways, epigenetics and gene regulation, gastrointestinal physiology, gene therapy, glycobiology and glycomics, HIV biology, inorganic biochemistry, limb development, lipidomics, membrane biology, metabolic engineering, metabolomics, molecular biology of aquatic organisms, molecular biology of fungi, molecular biology of muscles, molecular biology of vesicles and vesicular trafficking, molecular biology of yeast, molecular biology techniques, molecular endocrinology, molecular entomology, molecular genetics of bacteria, molecular immunology, molecular mechanobiology, nanopore sequencing, next-generation sequencing, organ-on-a-chip technologies, protein engineering, protein folding, proteomics, regenerative biology, specialized PCR variants, specialized forms of gel electrophoresis, structural biology, subnuclear organelles, the Golgi apparatus, the microbiome, tissue engineering, transcriptomics, virology

Nanotechnology: bioconjugation, bionanotechnology, carbon nanotubes, crystalline lattices for nanotechnology, DNA origami and other DNA nanotechnology, gold nanoparticles for biotechnology, graphene, mass production of nanotechnology, nanoelectronics, nanomechanics, nanorobotics, nanoscale membranes, nanotechnology-based drug delivery systems, optical interfacing with nanoscale systems, polymer engineering, quantum dots, quantum tunneling in nanotechnology, rotaxanes and catenanes, self-assembly, self-replicating machines, supramolecular chemistry, upconversion nanoparticles, using proteins as components within nanotechnological systems

Neuroscience: affective neuroscience, amygdala, application of X-ray microscopy to neuroscience, applications of higher mathematics to neuroscience (i.e. algebraic geometry, algebraic topology, category theory, etc.), applications of nanotechnology to neuroscience, applications of signal processing to neuroscience, auditory cortex, bioinspired artificial intelligence and robotics, biophysical theory of dendritic voltage propagation, brain-machine interfaces, cephalopod neurobiology, cerebellar circuitry, cerebral organoids, computations in the primary motor cortex, connectomics, contrast agent design, developmental neurobiology, engineering new types of electrodes and optrodes, enteric nervous system, expansion microscopy, graph theoretic models of neuronal connectivity, Hodgkin-Huxley models, honeybee cognition, image processing for 3D reconstruction of neuronal microanatomy, information processing in the peripheral nervous system, injectable electronics, insect sensory neurobiology, interactions of materials with brain tissue, light-sheet microscopy for neuroscience, mathematical models of synaptic potentiation, mechanisms of information coding in neural systems, microinsect neurophysiology (i.e. parasitoid wasps), molecular biology of the synapse, morphological diversity of neurons, MRI and fMRI, multielectrode arrays, neural circuits in the spinal cord, neural dust, neural mass models, neuroacarology, neurobiology of exotic arthropods, neuroendocrinology, neuroimmunology, neuroinformatics, neuromodulators, neuromuscular interactions, neuromyrmecology, NEURON, neurophysics, neuroscience of glial cells, neuroscience of language, neuroscience of reptiles, neurotechnology, noncoding RNAs in the brain, nucleus accumbens, optogenetics, phenomenological models of single neurons, retinal computations, reward system, RNA-seq for characterizing neurons, simulating large populations of multicompartmental Hodgkin-Huxley-type neurons, spider neurobiology, synthetic neurobiology, thalamus, tool development for connectomics, two-photon microscopy for neuroscience

Philosophy: affective philosophy, bioethics, epistemology, ethics of animal suffering, ethics of cerebral organoids, ethics surrounding machine consciousness, existentialism, extropianism, feminist philosophy, monism, ontology, panpsychism, philosophical definitions of the body, philosophy of art, philosophy of gender and sexuality, philosophy of mathematics, philosophy of science, philosophy of technology, philosophy regarding the fundamentals of good and evil, posthumanism, rational romanticism, romanticism, transhumanism, utilitarianism and deontology

Physics: computational physics, differential geometry of spacetime, diffusion, electrodynamics, general relativity, high energy physics, long-term fate of the universe, M-theory, metallurgy, optics, physical theory of protein folding, polymer physics, quantum computing, quantum field theory, quantum mechanics, radio frequency physics, solid state physics, solid state quantum mechanics, solid state thermodynamics, string theory, theory of classical mechanics, thermodynamics, topological matter, X-ray physics