VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality

2. Systems Neuroscience: Multiplex Networks and Critical Function

Buzsaki, Gyorgy. Rhythms of the Brain. Oxford: Oxford University Press, 2006. A Hungarian-American professor of Molecular and Behavioral Neuroscience at Rutgers University informs and enriches this endeavor by the principles of nonlinear science, via theory and experiment, to articulate an innately self-organizing cerebral formation and activity. By this vista, our brains, distinguished by a universal pattern and process across nested scales, can be appreciated to embody nature’s independent while emergent dynamics. This intricate volume, graced not by chapters but “cycles,” joins human and universe, and goes on to suggest a rudimentary global brain may be likewise generating itself. But a disclaimer is added on page 4 that Nature, of course, has no laws, desires, goals, or drives. (Is this necessary for publication or membership?) But Buzsaki and colleagues make a major contribution to a genesis vision, which is worth extended excerpts.

Oftentimes, not only does complexity characterize the system as a whole, but also its constituents (e.g. neurons) are complex adaptive systems themselves, forming hierarchies at multiple levels. All these features are present in the brain’s dynamics because the brain is also a complex system. (11) The scale invariance of fractals implies that knowledge of the properties of a model system at any scale can be used to predict the structure of the real system at larger or smaller scales. Applying this knowledge to neuroscience, knowing the fundamental properties of the organization of the cerebral cortex in any mammalian species and the rules of network growth, the principal structural organization of smaller and larger brains can be predicted. (30)

In essence, the claim is that a collective pattern recorded from a small portion of the cortex looks like the pattern recorded from the whole. This “scale invariance” or “self-similarity” is a decisive characteristic of fractals. Fractal structures – such as river beds, snow flakes, fern leaves, tree arbors, and arteries – and fractal dynamic processes – such as pink noise, cloud formation, earthquakes, snow and sand avalanches, heart rhythms, and stock market price fluctuations – are self-similar in that any piece of the fractal design contains a miniature of the entire design. Regarding the collective behavior of neuronal signals as fractals with self-similar fluctuations on multiple time and geometry scales has potentially profound theoretical and practical implications for understanding brain physiology. (126-127) The concept that physical systems, made up of a large number of interacting subunits, obey universal laws that are independent of the microscopic details is a relative recent breakthrough in statistical physics. Neuroscience is in serious need of a similar systematic approach that can derive mesoscale laws at the level of neuronal systems. (127)

Buzsaki, Gyorgy, et al. Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms. Neuron. 80/3, 2013. In a “Neuroscience Retrospective” issue, Buzsaki, NYU, Nikos Logothetis, MPI Biological Cybernetics, and Wolf Singer, MPI Brain Research, contribute a current summary and bibliography for this reconception, as many other fields, by way of nonlinear complexities. By these theories, a mature synthesis and verification has been reached of a unified cerebral architecture and activity graced by nested, self-organized critical dynamics. Please search Buzsaki and Singer for prior papers.

Despite the several-thousand-fold increase of brain volume during the course of mammalian evolution, the hierarchy of brain oscillations remains remarkably preserved, allowing for multiple-time-scale communication within and across neuronal networks at approximately the same speed, irrespective of brain size. Deployment of large-diameter axons of long-range neurons could be a key factor in the preserved time management in growing brains. We discuss the consequences of such preserved network constellation in mental disease, drug discovery, and interventional therapies. (Abstract)

We hypothesize below that the aforementioned essential features of brain organization, the activity-information retention and the local-global integration, are maintained by a hierarchical system of brain oscillations, and we demonstrate that despite a 17,000-fold variability in brain volume across mammalian species, the temporal dynamics within and across brain networks remain remarkably similar. It follows that, irrespective of brain size, the management of multiple time-scales is supported by the same fundamental mechanisms, despite potential adaptive changes in network connectivity. (751)

Cabessa, Jeremie and Hava Siegelmann. The Computation Power of Interactive Recurrent Neural Networks. Neural Computation. 24/4, 2012. University of Massachusetts, Amherst, computational neuroscientists take these cerebral complexities to exemplify how nature evolves, develops and learns. We are then invited to realize that the same dynamical trial and error, feedback to move forward, iterative process is in effect everywhere. See also Turing on Super-Turing and Adaptivity by Hava Siegelmann in Progress in Biophysics and Molecular Biology (113/117, 2013), and search Richard Watson 2014 herein.

In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities. (Abstract)

This analog information processing model turns out to be capable of capturing the nonlinear dynamical properties that are most relevant to brain dynamics. (997) Indeed, in the brain (or in organic life in general), information is processed in an interactive way, where previous experience must affect the perception of future inputs and older memories themselves may change with response to new inputs. Hence, neural networks should be conceived as performing sequential interactions or communications with their environments and be provided with memory that remains active throughout the whole computational process. Accordingly, we propose to study the computational power of recurrent neural networks from the rising perspective of interactive computation. (997)

Carruthers, Peter. Practical Reasoning in a Modular Mind. Mind & Language. 19/3, 2004. (As an initial note, it appears that “modular” schools also exist in cognitive science with various persuasions and viewpoints.) Philosopher Carruthers makes a case for domain-specific modules in the brain which arose in evolution in response to changing environments. In support of an evolutionary psychology, they are now seen to influence the mores of human behavior.

Carruthers, Peter. The Architecture of the Mind. Oxford: Clarendon Press, 2006. The University of Maryland philosopher makes a strong case for a massively modular brain, with certain evolutionary roots, whose remnants are with us today. In this regard, a broadly conceived evolutionary psychology is endorsed. But this academic endeavor in so many books and journals seems to labor within an assumed mechanical paradigm tacitly devoid of any extant identity or purpose. That minds are modular because they spring from and exemplify a universal tendency of self-organizing systems from genes to galaxies to form modules is not appreciated.

Cepelewicz, Jordana. To Make Sense of the Present, Brains May Predict the Future. Quanta. Online July, 2018. A science writer widely surveys the rising neuroscience school which goes by a broad “prediction coding hypothesis” umbrella. In so doing it is a popular entry to the contributions of its main founder and articulator, the British neuroscientist Karl Friston (search), along with many colleagues. The view then entails a “Bayesian brain” of better probabilistic inferences, and personal “enactive” aspects as they may flow from working memory to goal-directed behaviors. Advocates and doubters are given voice, but the general approach seems to be gaining much interest and avail. See also a Special Issue on Predictive Brains and Embodied, Enactive Cognition in Synthese (195/6, 2018) for much more. We note herein some papers by Michael Kirchhoff, Micah Allen and Karl Friston.

A controversial theory suggests that perception, motor control, memory and other brain functions all depend on comparisons between ongoing actual experiences and the brain’s modeled expectations.

Chang, Le and Doris Tsao. The Code for Facial Identity in the Primate Brain. Cell. 169/6, 2017. A main technical paper from Tsao’s CalTech lab about her collegial breakthrough decipherment of how pixelated neuronal architectures and mosaic areas are dynamically able to recognize whole faces. See also a commentary How Do We Recognize a Face? by Rodrigo Quiroga in this issue.

Primates recognize complex objects such as faces with remarkable speed and reliability. Here, we reveal the brain’s code for facial identity. Experiments in macaques demonstrate an extraordinarily simple transformation between faces and responses of cells in face patches. By formatting faces as points in a high-dimensional linear space, we discovered that each face cell’s firing rate is proportional to the projection of an incoming face stimulus onto a single axis in this space, allowing a face cell ensemble to encode the location of any face in the space. Using this code, we could precisely decode faces from neural population responses and predict neural firing rates to faces. Our work suggests that other objects could be encoded by analogous metric coordinate systems. (Abstract excerpt)

How individual faces are encoded by neurons in high-level visual areas has been a subject of active debate. An influential model is that neurons encode specific faces. However, Chang and Tsao conclusively show that, instead, these neurons encode features along specific axes, which explains why they were previously found to respond to apparently different faces. (R. Quiroga summary)

Changeux, Jean-Pierre. Climbing Brain Levels of Organisation from Genes to Consciousness. Trends in Cognitive Sciences. 21/3, 2017. The College de France, Institute Pasteur, Paris senior neuroscientist, now 80 years young, continues to advance the expansive understandings of life’s long Darwinian evolution as it lately becomes known as a neural cognitive development. In regard, a dynamic nesting of brain levels of organization is cast from genomes to gene-brain networks to synaptic epigenesis and long-range cerebral connectivities. Human aware sociality is then seen to be facilitated by and arise from this emergent scale.

The College de France, Institute Pasteur, Paris senior neuroscientist, now 80 years young, continues to advance the expansive understandings of life’s long Darwinian evolution as it lately becomes known as a neural cognitive development. In regard, a dynamic nesting of brain levels of organization is cast from genomes to gene-brain networks to synaptic epigenesis and long-range cerebral connectivities. Human aware sociality is then seen to be facilitated by and arise from this emergent scale.

Charvet, Christine, et al. Variation in Human Brains may Facilitate Evolutionary Change Toward a Limited Range of Phenotypes. Brain, Behavior and Evolution. 81/2, 2013. With coauthors Richard Darlington and Barbara Finlay, Cornell University neuropsychologists first cite recent studies that rejoin evolution and development, aka evo-devo, as a “conservation of programs specifying the initial body plan and fundamental physiological control processes in vertebrates and invertebrates” that serves to restrict somatic forms. They then proceed to show that similar constraints apply to “the domain of basic architecture in neural computation.”

Individual variation is the foundation for evolutionary change, but little is known about the nature of normal variation between brains. Phylogenetic variation across mammalian brains is characterized by high intercorrelations in brain region volumes, distinct allometric scaling for each brain region and the relative independence of olfactory and limbic structure volumes from the rest of the brain. Previous work examining brain variation in individuals of some domesticated species showed that these three features of phylogenetic variation were mirrored in individual variation. We extend this analysis to the human brain and 10 of its subdivisions (e.g., isocortex and hippocampus) by using magnetic resonance imaging scans of 90 human brains ranging between 16 and 25 years of age. Human brain variation resembles both the individual variation seen in other species and variation observed across mammalian species, i.e., the relative differences in the slopes of each brain region compared to medulla size within humans and between mammals are concordant, and limbic structures scale with relative independence from other brain regions. This nonrandom pattern of variation suggests that developmental programs channel the variation available for selection. (Abstract)

Chialvo, Dante. The Brain Near the Edge. www.arxiv.org/pdf/q-bio.NC/0610041. A Northwestern University Medical School neuroscientist proposes that neural processes, via their scale-free, functional networks, are critically poised in metastable states between order and disorder. (2006) Google the author’s name to access his copious writings.

Chialvo, Dante, et al. The Brain: What is Critical About It? Ricciardi, Luigi, et al, eds. Collective Dynamics: Topics on Competition and Cooperation in the Biosciences. American Institute of Physics Conference Proceedings, 2008. Researchers from Northwestern University, Universidad de Buenos Aires, and Universidad de San Andrés, similar to Levina 2007 and Kelso 2009, find nature’s tendency toward self-organized criticality, as lately girded by statistical physics, to be likewise present in collaborative neural activities. See also Chialvo's update survey "Emergent Complex Neural Dynamics" in Nature Physics (6/10, 2010). What great discovery might we all be closing on, if only we could allow and imagine it?

The brain is a complex adaptive nonlinear system that can be studied along with other problems in nonlinear physics from a dynamical standpoint. With this perspective here we discuss a proposal claiming that the brain is spontaneously posed at the border of a second order phase transition. The claim is that the most fascinating properties of the brain are simply generic properties found at this dynamical state, suggesting a different angle to study how the brain works. From this viewpoint, all human behaviors, including thoughts, undirected or goal oriented actions, or simply any state of mind, are the outcome of a dynamical system - the brain - at or near a critical state.

Christianson, Nicolas, et al. Architecture and Evolution of Semantic Networks in Mathematics Texts. arXiv:1908.04911. University of Pennsylvania bioneuroengineers NC, Ann Blevins, and Danielle Bassett, with many colleagues, continue to parse the presence of node/link multiplex geometries as they become evident in every natural and social milieu. In this instance, even textual script and its educational content is found to be distinguished. In August 2019, we can report an increasing realization that such a singular, iconic physiology and anatomy is vitally present everywhere. A graphic core-periphery array is depicted with dense inner and sparse outer areas, while another figure cites the same Betti (search) mathematics used to analyze clusters of galaxies. See also The Network Architecture of the Human Brain is Modularly Encoded in the Genome by this team (Bertolero, May 2019). From school books to cerebral faculties and onto to quantome and cosmome phases, a natural genesis is graced by the one, same, ultimately bigender icon.

Knowledge is a network of interconnected concepts. Yet, how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development of learning enhancement strategies. Here we study topological semantic networks reflecting mathematical concepts and their relations in college- linear algebra texts. We find that the networks exhibit strong core-periphery architecture, where a dense core of concepts presented early is complemented with a sparse periphery evenly throughout the exposition. Using tools from applied topology, we find that the expositional evolution of the semantic networks produces and fills knowledge gaps. Broadly, our study lays the groundwork for optimal design principles for textbook teaching in a classroom setting. (Abstract excerpt)

Knowledge has been distilled into formal representations for millennia. Such efforts have sought to explain human reasoning and support artificial reasoning. Semantic networks organize information by detailing concepts (nodes) and their relations (edges), which can be defined by inclusion in the same thesaurus entry, free word association data, or co-occurrence within a corpus of text. Concept maps reflect information in a similar manner, and therefore can be used to evaluate comprehension and identify topics that are most difficult to connect to other concepts. With the capacity to construct semantic networks, and similar formal representations of knowledge comes the challenge of distilling rules and mechanisms of knowledge formalization and acquisition. (1)

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