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

2. Systems Neuroscience: Multiplex Networks and Critical Function

Bryant, Katherine, et al. Connectivity profile and function of uniquely human cortical areas.. Journal of Neuroscience.. March 17, 2025. Eight neuroscientists posted at Oxford University, Université Aix-Marseille, Brain and Behavior Research Centre, Jülich, Germany, University of Nottingham, Radboud University, and Heinrich Heine University, Düsseldorf provide a sophisticated imaging analysis to discern just what cerebral qualities separate and distinguish our homo sapience. A main difference seems to concern the locales and degrees of neural network connectivities.

Determining the brain specializations unique to humans requires comparative anatomical information from other primates. Human (Homo sapiens) (m/f), chimpanzee (Pan troglodytes) (f), and rhesus macaque (Macaca mulatta) (m/f) white matter atlases were used to describe the cortical grey matter in terms of connectivity with white matter tracts. We identified human-unique profiles in temporal and parietal cortices, and hominid-unique organization in prefrontal cortex. Functional decoding revealed human-unique hotspots correlated with language processing and social cognition. These findings emphasize the importance of temporal and parietal cortical evolution in shaping our sapient abilities.

While current theories on human brain uniqueness focus on changes to prefrontal areas, our findings support a two-step evolutionary process, in which changes in prefrontal cortex organization emerge prior to changes in temporal areas. Unlike global connectivity or gross anatomical approaches, an informed comparative connectivity makes it possible to reveal major changes in fiber systems underlying a variety of cognitive functions that have changed in a stepwise manner in the great ape and human lineages. (17)

Buckner, Randy and Fenna Krienen. The Evolution of Distributed Association Networks in the Human Brain. Trends in Cognitive Sciences. 17/12, 2013. In work noted by the New York Times as “In the Human Brain, Size Really Isn’t Everything” (December 26, 2013), Harvard University and Massachusetts General Hospital neuroscientists propose that as earlier, rudimentary neural architectures became “untethered,” this allowed more complex connective topologies to form in primate and homo sapiens brains. A volumetric increase is not enough, an increasingly intricate “connectome” makes the difference. And of further importance, as per the quote, neural evolution is seen in an embryonic way as a expansive deployment from an original archetypal geometry.

The inherited constraints of development and the general plan, or Bauplan, of the brain are powerful limiters on how neural circuits can evolve across generations. Here we raise the possibility that critical features of the association cortex, linked to size scaling, may contribute to the human brain’s extraordinary capabilities. The central idea is that a distributed form of circuit may have become increasingly prominent when ancient rules of development were expressed in an expanding cortical mantle. The possibility that simple mechanisms play a major role in recent brain evolution is comforting because it demystifies the gap between our brain’s capabilities and those of our ancestors. (661-662)

Bullmore, Edward, et al. Generic Aspects of Complexity in Brain Imaging Data and other Biological Systems. Neuroimage. 47/3, 2009. University of Cambridge, NIH, and University of Melbourne neuroscientists, as the extended quotes aver, contend that after a decade of intensifying progress in endeavors to view neural anatomy and activity as an iterative, emergent self-organization, as every other domain of life and society has done also, that a universality of such similar kind can be admitted and appreciated. Upon reflection, this is huge for we are now invited to realize in our midst an epochal worldwide discovery of an untangled, comprehensible nature, arising from and manifestly representing a true universe to human genesis.

A key challenge for systems neuroscience is the question of how to understand the complex network organization of the brain on the basis of neuroimaging data. Similar challenges exist in other specialist areas of systems biology because complex networks emerging from the interactions between multiple non-trivially interacting agents are found quite ubiquitously in nature, from protein interactomes to ecosystems. We suggest that one way forward for analysis of brain networks will be to quantify aspects of their organization which are likely to be generic properties of a broader class of biological systems. In this introductory review article we will highlight four important aspects of complex systems in general: fractality or scale-invariance; criticality; small-world and related topological attributes; and modularity. (Abstract, 1125)

However, it is becoming increasingly clear that complexity may be a characteristic of biological systems in general; and that some of the same mathematical tools and concepts can appropriately be used to quantify and compare aspects of complexity in substantively very diverse systems. To take a single, illustrative example in brief; many complex systems have been shown to have a modular or nearly-decomposable organization, including systems as different as the human brain transcriptome, the global air transportation network, and ecological or economic networks. (1126)

The important generalization is that one way forward in dealing with the formidable complexity of the human brain may be to recognize that certain key principles of its organization are shared in common with other complex systems in biology and elsewhere. This idea that both brain and biological systems may have generic properties in common is one implication of the more general universality hypothesis: that certain network organizing principles are highly conserved and more-or-less universally instantiated in real-life networks. Studies addressing network organization have proliferated recently in an interdisciplinary research area, which is driven largely by technical developments in statistical physics and has begun to demonstrate a startling degree of commonality in the organization of substantively distinct complex systems. (1126)

Buzsaki, Gyorgy. Neural Syntax: Cell Assemblies, Synapsembles, and Readers. Neuron. 68/3, 2011. Natural realms from quanta and genomes to cooperative groups are presently being reinterpreted and better understood not only by complex systems science, but by way of novel appreciations of their linguistic, semiotic essence. As the quotes note, the Rutgers University behavioral neuroscientist (search) proceeds with a similar approach to parse cerebral structure and dynamics in such grammatical terms. See also Buzsaki's 2006 book Rythmns of the Brain for more. And what might we take from this – is a greater creation being found that is, as our traditions know, deeply textual, poetic, even scriptural, in kind?

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as ‘‘cell assemblies,’’ underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by ‘‘reader-actuator’’ mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights (‘‘synapsembles’’). (Abstract, 362)

Neural Syntax: Rules that Integrate and Parse Fundamental Assemblies. In general, syntax (grammar) is a set of principles that govern the transformation and temporal progression of discrete elements (e.g., letters or musical notes) into ordered and hierarchical relations (e.g., words, phrases, sentences or chords, chord progression, and keys) that allow for a congruous interpretation of the meaning of language or music by the brain. (365) Neural Words and Sentences. The second hypothesis of this review is that temporal sequencing of discrete assemblies by neural syntax can generate neural words and sentences. Although strings of assemblies can be regarded simply as a larger assembly, and indeed assemblies of different length and size refer to many things in neuroscience, I chose the term ‘‘neural word’’ to emphasize that words consist of multiples of the fundamental assemblies. (365)

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.

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