Author: logancollins

     Neuroecologies, cybernetic systems which combine biological and technological components, may streamline human interactions with their environment and counteract issues like pollution and climate change. Today, humans occupy too many ecological niches for long-term sustainability. If we continue to outcompete other species, we will observe deadly systemic bifurcations that lead to desertification, coastal submergence, and even more widespread extinctions. However, the interdependence of existing socioeconomic structures makes them difficult to uproot in order to avert catastrophe. To overcome this challenge, I propose that humans must integrate into their environments without sacrificing existing structures, instead metamorphosing those structures into more sustainable forms through improved technologies. In particular, the worlds of electronics, nanoscience, and synthetic biology must merge into the meadows, forests, and oceans. As written by Richard Brautigan in his 1967 poem, All Watched Over by Machines of Loving Grace, the “mammals and computers [will] live together in mutually programming harmony.”

     Traditionally, environmentalist viewpoints have divorced technology and nature, framing human influence as corrupting and impure. However, this represents an incorrect and ultimately counterproductive approach. Humans and their creations are no more separate from nature than termites and their mounds, beavers and their dams, or bacteria and their metabolic byproducts. While human impact on the environment has occurred at a rapid pace, this does not mean that humans are inherently less ethical than other organisms. Framing the technological humans as evil is a mistake since this ideology “drives underground” many possible solutions to environmental challenges.

     Many naturalists have supported anti-interference arguments with the fact that some technology has contributed to environmental damages. However, framing any particular technology as representative of all technology is misguided in an analogous way to cultural prejudice. Few would advocate judging the entire population of China based on an interaction with a single Chinese person. Likewise, it should be clear that the technologies which have contributed to climate change should not represent the philosophical concept of technology as a whole.

     Unlike most other organisms, humans possess a potent ability to simulate possible future scenarios. This ability arises from internal biological cognition, social exchange of knowledge, and technological augmentations. On its own, the human brain can generate plans for behavior many years into the future. In societies, networks of human brains act as a form of distributed superintelligence, agglomerating and refining individual predictions (Lenartowicz, 2017). The outsourcing of cognitive tasks to computers, pencils, and other technologies provides an immense expansion to the human noosphere. This combined global cortex has the power to analyze, predict, and act to tackle today’s greatest challenges.

     Biological and technological systems often share characteristics. From a network perspective, both usually possess a small-world structure (Sporns, 2009) (Newman, 2003). Small-world networks (supplementary Fig. S1C-D) have small mean path lengths and high clustering. For reference, mean path length is the average number of jumps taken to traverse between any pair of nodes in a network and clustering measures the connectivity among a given node’s nearest-neighbors (supplementary Fig. S1A-B). In addition, both biological and technological networks tend to exhibit resilience against random removal of nodes and vulnerability to targeted removal of highly interconnected nodes (Newman, 2003). Such network properties show remarkable universality across technology and biology.

     From a control theory perspective, biological and technological systems share modules and protocols (Csete, 2002). Modules are subsystems that utilize interfaces to other modules and work with some degree of independence. Biological modules can be seen in macromolecular complexes, membrane-bound compartments, cells, oscillating neural circuits, organs, as well as in ecosocial structures like packs of wolves and coral reefs. Technological modules arise in circuit components like flip-flops, in microprocessors, in network-connected computational devices, stations on assembly lines, and in components of infrastructure like hospitals and schools.

     Protocols are rules which manage the flow of ordered operations within systems (Csete, 2002). Biological protocols arise from information encoded in nucleic acids, epigenetic regulatory networks, neural ensembles, and ecological competition structures. Within such biological and technological protocols, feedback loops are universal. Feedback facilitates stable oscillations, system-scale or subsystem-scale adaptation to external stimuli, and discourse among modules.

     Since biology and technology operate by common principles, the engineering techniques applied in technological systems can be adapted to engineering biological systems and engineering interfaces between systems. Of course, doing this will also require recognizing differences and working to optimize the characteristics of all the systems involved in order to more seamlessly merge our technology and environment.

     For illustrative purposes, I will briefly outline a potential urban implementation of neuroecologies, a fictional city called Las Futuras. The skyscrapers of Las Futuras are wrapped in a silky, biocompatible polymer (Fig. 1B). Embedded in the polymeric matrix are nanomachines which recycle chemical waste products into nutrients, antioxidants, water, and molecules that chelate toxins and facilitate their traffic to more intensive treatment nodules. Cybernetic fireflies and bioluminescent trees illuminate the metropolis at night. There are no roads, the ground is overgrown with sensory flowers that monitor conditions and transmit data through conductive root networks, glistening metabolic blobs which assemble swarms of nanorobotic gardeners on command (Fig. 1C), and gossamer membranes for collecting solar energy.

     Enhanced herbivores and insects wander freely, instinctively consuming the fruitlike globules growing out of the buildings. There is no predation as all predators have either been transformed into herbivores or their populations have been phased out by reproductive methods. Because of this and the fact that the animals may live for hundreds or even thousands of years, their fertility rates are modulated by AI savants who continuously oversee the organismic network and prevent catastrophic bifurcations from triggering issues like overpopulation and starvation.

     The reason that few humanoid creatures are visible is that they exist elsewhere. Inside the skyscrapers, dense cores of computronium (Fig. 1A) simulate vast and beautiful worlds, kaleidoscopic utopias in which humanity’s billions and their AGI children may reside without overconsumption of resources. These people do have the option of entering android bodies and exploring realspace, but this exploration usually involves visiting locations other than Earth. As such, the neuroecologies remain sparsely populated by androids and their resource demands are minimal.

Figure 1 (A) Top view of the speculative urban neuroecology dubbed Las Futuras. Some skyscrapers (hexagonal structures) contain computronium for simulating uploaded minds and their virtual worlds, others manufacture materials and machines as needed by the system, still others recycle waste products. (B) Mesoscale view of a region within Las Futuras. Engineered trees exhibit bioluminescence. Nanomachines which recycle atmospheric pollutants are found in the walls of the skyscrapers. (C) Ground-level view of a region within Las Futuras. Fruitlike globules provide nutrients for cybernetic wildlife, including modified microorganisms and nanorobots. Solar membranes gather energy for local use, though more extensive solar farms exist outside the metropolitan area. Wriggling wormlike creatures tend to various macroscale tasks. Sensory flowers (not shown) monitor ambient conditions and route information into the conductive root network. This extensive system of roots provides a method of rapidly transporting data into the noosphere. Most organisms and devices are compatible with the roots and can easily link their nervous systems to the network.

     Although this kind of world might seem distant, there is a real possibility that it will be achievable before the end of the 21^st century. As a result of exponential trends in computer science and bioinformatics, many futurists argue that we are approaching a technological singularity. If this event occurs, it will be marked by an intelligence explosion which radically transforms life on Earth and beyond. The technological singularity may come from artificial superintelligence, self-aware computer networks, biological enhancement, or from the merging of humans and machines (Vinge, 1993). National Medal of Technology recipient Ray Kurzweil proposes that the technological singularity may occur as early as 2045, providing cognitive abilities up to a billion-fold more powerful than the entirety of today’s human race combined (122). This prediction may initially sound outrageous, but Kurzweil has shown a remarkable ability to accurately envision future events as evidenced by his numerous successful predictions; for instance, former world chess champion Garry Kasparov losing his games against IBM’s Deep Blue chess computer in 1997. If the singularity comes to pass, it will provide the intellectual and robotic resources necessary to merge technology and the environment.

     For some, my vision of the future might initially sound unsettling as it is a very different world than the world to which we are accustomed. But throughout evolutionary history, the ecological scene has continuously metamorphosed. In the Precambrian, aquatic metazoans (multicellular organisms) were only just beginning to emerge and the land was populated only by cyanobacterial mats (Horodyski and Knauth, 1994). Today, a rich and beautiful biosphere thrives across the air, land, and sea. The prospect of transformation need not be frightening. Technology is a part of nature, facilitating the metamorphosis. We have the ability to carry out this transformation, guiding unstable human and nonhuman ecologies into unified, sustainable neuroecologies.

References

Brautigan. “All Watched Over by Machines of Loving Grace.” All Watched Over by Machines of Loving Grace, Communication Company, 1967.

Bullmore, Ed, and Olaf Sporns. “Complex brain networks: graph theoretical analysis of structural and functional systems.” Nature Reviews Neuroscience, vol. 10, no. 3, Apr. 2009, 186–198., doi:10.1038/nrn2575.

Csete, M. E. “Reverse Engineering of Biological Complexity.” Science, vol. 295, no. 5560, Jan. 2002, pp. 1664–1669., doi:10.1126/science.1069981.

Horodyski, R. J., and L. P. Knauth. “Life on Land in the Precambrian.” Science, vol. 263, no. 5146, 1994, pp. 494–498., doi:10.1126/science.263.5146.494.

Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking.

Lenartowicz, Marta. “Creatures of the semiosphere: A problematic third party in the ‘humans plus technology’ cognitive architecture of the future global superintelligence.” Technological Forecasting and Social Change, vol. 114, 2017, pp. 35–42., doi:10.1016/j.techfore.2016.07.006.

Newman, Mark. “The Structure and Function of Complex Networks.” SIAM Review, vol. 45, no. 2, Mar. 2003, pp. 167–256., doi:10.1137/S003614450342480.

Vinge, Vernor. “The Coming Technological Singularity: How to Survive in the Post-human Era.” Interdisciplinary Science and Engineering in the Era of Cyberspace, Dec. 1993.

Supplementary Figure S1 (A) The clustering coefficient for a node is the number of links among its nearest neighbors divided by the maximum number of links among those neighbors. (B) The path length between two nodes is the minimum number of edges which must be traversed in order to reach one node from the other. (C) Small-world networks are extremely common in biological and technological systems. Here, a randomly generated small-world network is depicted. (D) Small-worldness is proportional to the mean clustering coefficient over the mean path length. Note that the N^-1 terms cancel since they are the same in the numerator and the denominator for a given network with N nodes.

Nanowire arrays restore vision in blind mice

• Nanowire arrays were developed and implanted in the retinas of blind mice to replace photoreceptor cells (A).
• The nanowires consisted of titanium dioxide wires decorated with gold nanoparticles.
  • TiO₂ nanowires are known to generate photocurrents upon exposure to UV light.
  • Decorating the wires with gold nanoparticles enhances this effect and allows for blue and green visible light to also trigger photocurrents.
  • The photocurrents for blue and green light were much lower than the photocurrents for near-UV light, but still high enough to be useful.
• Patch-clamp recording was used to measure the responses of retinal ganglion cells (RGCs) in controls and in mice equipped with nanowires to near-UV, blue, and green light.
  • The RGCs in wild-type mice were responsive to near-UV, blue, and green light.
  • RGCs with nanowires were responsive to near-UV light (B).
  • RGCs with nanowires were responsive to blue and green light (C), though the latencies were longer compared to near-UV case, likely because of their smaller photocurrents.
• The responses of nanowire-equipped RGCs to varying light spot diameters were also tested (D).
  • For near-UV light, RGCs could be reliably activated with spots of about 50-100 µm (and larger spots).
  • For blue and green light, RGCs could be reliably activated with spots of about 200-300 µm (and larger spots).
• Pupillary reflex improvements in the nanowire-carrying mice supported the conclusion that the nanowire implants had partially restored vision.

Reference: Tang, J., Qin, N., Chong, Y., Diao, Y., Y., Wang, Z., … Zheng, G. (2018). Nanowire arrays restore vision in blind mice. Nature Communications, 9(1). doi:10.1038/s41467-018-03212-0

Surface chemistry governs cellular tropism of nanoparticles in the brain

• The interactions of polylactic acid (PLA) nanoparticles with the brain microenvironment were studied. It should be noted that this paper also investigated PLA nanoparticle interactions with glioblastoma tumors, but that topic will not be discussed in this summary.
• PLA nanoparticles were infused into rat brains. Four types of PLA nanoparticle formulation were tested, each bearing a different functional group.
  • PLA nanoparticles coated with polyethylene glycol (PEG), hyperbranched glycerol (HPG), HPG-CHO (aldehydes instead of vicinal diols), and unmodified PLA nanoparticles were examined.
  • PLA-PEG and PLA-HPG nanoparticles have decreased immunogenicity. PLA-HPG-CHO nanoparticles are less immunogenic as well as exhibiting bioadhesivity.
  • The nanoparticles were engineered to be brain-penetrating. They possessed neutral or negative surface charge, diameters of under 100 nm, hydrodynamic diameters (a measure which incorporates the movement of polymer chains in the nanoparticles) of under 150 nm, and did not aggregate in cerebrospinal fluid. In addition, they were fluorescently labeled with the chemical dye DiA.
  • All nanoparticle formulations diffused over volumes of about 40 mm³. The diffusion volumes were also fairly homogenous, though some minor variation occurred (A).
• Interactions with neurons, astrocytes, and microglia were tested. Nanoparticles were infused into the brain and allowed to diffuse. Relative abundances of nanoparticles in the three cell types were analyzed using fluorescence-activated cell sorting to measure mean florescence intensity (MFI) within individual cells (B).
  • PLA-PEG and PLA-HGP nanoparticle uptake was distributed fairly evenly among the cell types. They also exhibited lower total uptake than PLA-HPG-CHO nanoparticles.
  • In addition to showing higher total uptake, PLA-HPG-CHO nanoparticles exhibited preferential uptake by microglial cells and decreased uptake by neurons.
• Confocal fluorescence microscopy was used to image tissue samples with infused nanoparticles.
  • Neuron morphology was not significantly affected by any of the nanoparticle types.
  • PLA, PLA-PEG, and PLA-HPG-CHO (but not PLA-HPG) nanoparticles caused astrocytes to display upregulated GFAP protein, indicating some level of immunogenicity (C).
  • PLA and PLA-HPG-CHO nanoparticles caused amoeboid morphology in microglia, indicating that they had entered an immunologically active state. PLA-PEG and PLA-HGP nanoparticles allowed microglia to stay in a ramified (branched) state, indicating lack of immunological response (D).
  • In the displayed confocal microscopy images (taken after 4 hours of diffusion), nanoparticles are stained red, nuclei blue, and GFAP white.

Reference: Song, E., Gaudin, A., King, A. R., Seo, Y., Suh, H., Deng, Y., … Saltzman, W. M. (2017). Surface chemistry governs cellular tropism of nanoparticles in the brain. Nature Communications, 8, 15322. doi:10.1038/ncomms15322

Andromeda Spaceways Magazine #70 can be accessed at the link below. Visit pages 89-90 to read my poem, The Sonata Machine.

ASM70

 

Manifolds

• Although differential geometry usually involves smooth manifolds, topological manifolds provide the foundation for understanding smooth manifolds.
• Topological manifolds are a type of topological space which must satisfy the conditions of Hausdorffness, second-countability, and paracompactness (see Notes on Topology).
• In addition, topological manifolds must be locally homeomorphic to Euclidean space, ℝⁿ.
• In order to be locally homeomorphic to Euclidean space, the formalisms below must hold.

• This means that, for every point x in a locally Euclidean topological space X, there exists an open set U such that x belongs to U and there exists a homeomorphism h from ℝⁿ to U (and its inverse). These homeomorphisms are called charts. The combination of charts which covers X is called an atlas.
• For an atlas, two charts can overlap on a manifold. The intersection of these two charts is an open set which maps to the same region of Euclidean space. Transition maps are composition functions f(g^-1) = f∘g^-1 and g(f^-1) = g∘f^-1 which map the open sets in ℝ²to the manifold (by the inverse function) and then map back to Euclidean space (by the function which describes the other homeomorphism). This is also called a coordinate transformation.

• If a manifold’s transition maps are all at least once differentiable (C¹), then the manifold is called a differentiable manifold. If the transition maps are all infinitely differentiable (C^∞), then the manifold is called a smooth manifold.

Dot products on smooth manifolds

• The metric tensor allows generalization of the dot product for vectors on manifolds and is given by the equation below. Here, u and v are input vectors. The superscript and subscript indicate that uⁱ is represented as a column vector and v_j is represented as a row vector (covector to uⁱ) inside the summation. The matrix g_ij is a coefficient matrix which modifies the initial coordinate system for a given manifold. The summation is over matrix indices i and j where i=j.

• To see how this equation works, consider using metric tensor to describe the dot product of two vectors in ℝ². In this case, g_ij is simply the 2×2 identity matrix.

• For manifolds in general, the coefficient matrix g_ij is usually not the identity matrix. Instead, the entries of g_ij are given by the equation below involving the Jacobian matrix J.

• To use this equation, new coordinates must be defined in terms of Euclidean coordinates. In order to understand this, consider the example of polar coordinates. The polar coordinate formulas are operated on by the Jacobian. When inputted into the formula for the coefficient matrix, they simplify to the result below.

• Any set of alternative coordinates written in terms of Euclidean coordinates can be used to generate a g_ij matrix by this process.
• An interesting application of the metric tensor is to compute the generalized dot product of two vectors on a surface (2-manifold). The vectors “start out” in ℝ², but then are mapped onto the surface, which is embedded in ℝ³. To accomplish this, the ℝ² coordinates are represented in ℝ³ by using zero for the z-components. Note that the plane is assumed to be perpendicular to the surface in this scenario.

• Given a pair of vectors u and v tangent to a point on a parametric surface (2-manifold), the lengths of the vectors and the angle between them can be computed using the metric tensor. The lengths are given in the top equations and the angle is given in the bottom equations (below).

Arc lengths on smooth manifolds

• The metric tensor can be used to determine the distance between the points γ(t₁) and γ(t₂) on a manifold. The vector-valued function γ(t) defines a parametric curve on the manifold. Here, g_ij is generated using the Jacobian of the parametric functions in γ(t). The components γⁱ and γ^j are equivalent when i=j since they represent the same components. The absolute value is included to keep the term under the square root positive.

• Consider the example of a parametric curve in ℝ² which is mapped onto a 2-manifold embedded in ℝ³. Here, the arc length of such a curve is computed.

Integration on smooth manifolds

• Integration can be performed on surfaces (2-manifolds) using surface integrals. Furthermore, this method can be generalized to volumes and hypervolumes on n-dimensional smooth manifolds.
• The surface integral of a region on a 2-manifold embedded in ℝ³ can be computed by the equation below. The double-struck bars indicate magnitude. Here, the surface is given as a function z(x,y).

• It should be noted that r may represent any parameterized parametrized surface (not just a surface given as z(x,y)). The more general case of a surface embedded in ℝ³ is given below.

• Using some algebraic manipulations, the surface integral can be rewritten as the equation below. The matrix g represents the metric tensor (generated by applying the Jacobian to the parameterized function r).

• In this form, the surface integral can easily be generalized to higher dimensions so as to integrate volumes and hypervolumes on smooth manifolds. Below, a volume integral on a smooth 3-manifold is given.

• For integrating a hypervolume on an n-dimensional smooth manifold, a generalized version of the surface and volume integrals can be used.

by Logan Thrasher Collins