VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
1. The Evolution of Brain Anatomy and Cognizance
Kaiser, Marcus and Sreedevi Varier. Evolution and Development of Brain Networks: From Caenorhabditis elegans to Homo sapiens. Network: Computation in Neural Systems. 22/1-4, 2011. Within our humankind cerebral and cognitive vista, Newcastle University and Seoul National University neuroscientists fill in and report upon this common, concerted and mosaic, evolutionary development that these collective faculties have arisen from. May one then wonder what kind of a reality seeks to create and accomplish, some billions of years on, its own consciously perceived reconstruction and witness? Whom is learning this and for what great discovery and purpose?
Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. ‘Small-world’ topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints. (143)
Kandel, Eric, et al. Principles of Neural Science. New York: Plenum, 2000. An encyclopedic text on every aspect of brain and nervous system structure and function.
Karten, Harvey. Vertebrate Brains and Evolutionary Connectomics: On the Origins of the Mammalian Neocortex. Philosophical Transactions of the Royal Society B. 370/0060.2015, 2015. The veteran UC San Diego neurophysician continues his flow of findings that while animal classes differ, persistent commonalities can be discerned by the latest sophisticated analysis, aka cerebral connectomics over time. See Morphological Evolution of the Vertebrate Forebrain: From Mechanical to Cellular Processes by Francisco Aboitiz and Juan Montiel (herein) in Evolution & Development (21/6, 2019) for similar confirmations.
Kazemian, Majid, et al. Evidence for Deep Regulatory Similarities in Early Developmental Programs across Highly Diverged Insects. Genome Biology and Evolution. 6/9, 2014. In this online Oxford journal, University of Illinois and SUNY Buffalo researchers provide another witness from this arthropod phylum of anatomical features in place from their very onset.
Our results argue strongly that despite extensive binding site turnover and overall sequence divergence, similar regulatory mechanisms govern developmental gene expression even over distances of >350 Myr, and suggest that gene regulatory networks have been directly conserved. (2317)
Kennedy, Henry and Colette Dehay. Self-Organization and Interareal Networks in the Primate Cortex. Hofman, Michel and Dean Falk, eds. Evolution of the Primate Brain: From Neuron to Behavior. Amsterdam: Elsevier Science, 2012. In this chapter, University of Lyon, Stem Cell and Brain Research Institute, neuroscientists open another window on how the brain manages and proceeds to organize its cognitive anatomy. Search Hofman for the whole volume.
Variability of gene expression of cortical precursors may partially reflect the operation of the gene regulatory network and determines the boundaries of the state space within which self-organization of the cortex can unfold. In primates, including humans, the outer subventricular zone (OSVZ), a primate-specific germinal zone, generates a large contingent of the projection neurons participating in the interareal network. The number of projection neurons in individual pathways largely determines the network properties as well as the hierarchical organization of the cortex. Mathematical modeling of cell-cycle kinetics of cortical precursors in the germinal zones reveals how multiple control loops ensure the generation of precise numbers of different categories of projection neurons and allow partial simulation of cortical self-organization. We show that molecular manipulation of the cell-cycle of cortical precursors shifts the trajectory of the cortical precursor within its state space, increases the diversity in the cortical lineage tree and explores changes in phylogenetic complexity. These results explore how self-organization underlies the complexity of the cortex and suggest evolutionary mechanisms. (Abstract)
Kishikawa, Kiisa. Evolutionary Convergence in Nervous Systems: Insights from Comparative Phylogenetic Studies. Brain, Behavior and Evolution. 59/5-6, 2002. A persistently convergent evolution in many anatomical and cerebral domains is now realized to be quite widespread.
Over the past 20 years, cladistic analyses have revolutionized our understanding of brain evolution by demonstrating that many structures, some of which had previously been assumed to be homologous, have evolved many times independently. These and other studies demonstrate that evolutionary convergence in brain anatomy and function is widespread……One reason that convergence is so common in the biological world may be that the evolutionary appearance of novel functions is associated with constraints, for example in the algorithms used for a given neural computation. Convergence in functional organization may thus reveal basic design features of neural circuits in species that possess unique evolutionary histories but use similar algorithms to solve basic computational problems. (240)
Kording, Konrad. Bayesian Statistics: Relevant for the Brain? Current Opinion in Neurobiology. 25/130, 2014. In a special issue on Theoretical and Computational Neuroscience, a Northwestern University biophysicist advocates this approach which is lately coming into use across the sciences for optimal choices from a population of options. A best or sufficient bet is achieved by according new experience and/or responses with prior learned memory. For example, Richard Watson, et al (search 2014) proposes life’s evolution as proceeding this way. See also Automatic Discovery of Cell Types and Microcircuitry from Neural Connectomics by Kording and Eric Jonas at arXiv:1407.4137. The whole issue of some 32 articles, e.g. by Adrienne Fairhall, Stanislav Dehaene, and Leslie Valiant, is a significant entry to an endeavor by worldwise humanity to reveal the creaturely cerebration that brought me and We to be. With “connectome” often cited, the papers seem as if they could equally apply to genomes. Might a better term be a “neurome” equivalent?
Bayesian statistics can be seen as a model of the way we understand things. Our sensors are noisy and ambiguous as several worlds could give rise to the same sensor readings. We therefore have uncertainty in our data and cannot be certain which model or hypothesis we should believe in. However, we can considerably reduce uncertainty about the world using previously acquired knowledge and by interpreting data across sensors and time. As new data comes in, we update our hypotheses. Bayesian statistics is the rigorous way of calculating the probability of a given hypothesis in the presence of such kinds of uncertainty. With Bayesian statistics, previously acquired knowledge is called prior, while newly acquired sensory information is called likelihood. (130)
Laughlin, Simon and Terrence Sejnowski. Communication in Neuronal Networks. Science. 301/1870, 2003. The article reports on a linear relation between cortical white and grey matter for 59 mammalian species expressed by a power law which spans five orders of magnitude from the pygmy shrew to the elephant.
Lefebvre, Louis and Daniel Sol. Brains, Lifestyles and Cognition: Are There General Trends? Brain, Behavior and Evolution. 72/2, 2008. McGill University neurobiologists contribute to the discovery of an amplifying encephalization and erudition being found across the Metazoan kingdoms. Upon reflection, might we consequent embrained, collaborative humans be able to finally perceive the grand learning process of a self-discovering genesis universe?
Comparative and experimental approaches to cognition in different animal taxa suggest some degree of convergent evolution. Similar cognitive trends associated with similar lifestyles (sociality, generalism, new habitats) are seen in taxa that are phylogenetically distant and possess remarkably different brains. Many cognitive measures show positive intercorrelations at the inter-individual and inter-taxon level, suggesting some degree of general intelligence. (135) From apes to birds, fish and beetles, a few common principles appear to have influenced the evolution of brains and cognition in widely divergent taxa. (135)
Lefebvre, Louis, et al. Large Brains and Lengthened Life History Periods in Odontocetes. Brain, Behavior and Evolution. 68/4, 2006. Whales and dolphins exhibit the same parallel between cerebral volume and length of life as do other phyla. Upon reflection, one might perceive an evolutionary propensity for life to manifestly grow in cognizance and yearly duration, so as to ramify into a more prominent cosmic presence.
Most of the studies on mammalian life history correlates of brain size have concentrated on primates. In general, the studies show that life span and time to sexual maturity are positively associated with relative brain size. Similar patterns have been found in other groups of mammals, as well as birds, suggesting a general association among longevity, development time and encephalization. (219)
Liebeskind, Benjamin, et al. Evolution of Animal Neural Systems. Annual Review of Ecology, Evolution, and Systematics. 48/377, 2017. UT Austin senior computational biologists Liebeskind, Hans Hofmann, Danny Hillis, and Harold Zakon provide a most sophisticated review to date of how early sensory cerebral capacities across the phyla came to form, sense, learn, and develop. Their detailed reconstructions, an incredible achievement by our collaborative humankinder phase, are depicted by cladogram, deep homology, molecular novelty, and systems drift models. An “urbilaterian” origin is seen to deploy into Nematode, Cnidarian, Ctenophore, Drosophila and Xenopus ancestries. Once again an overall appearance, one might muse, seems to be an embryonic gestation.
Nervous systems are among the most spectacular products of evolution. Their provenance and evolution have been of interest and often the subjects of intense debate since the late nineteenth century. The genomics era has provided researchers with a new set of tools with which to study the early evolution of neurons, and recent progress on the molecular evolution of the first neurons has been both exciting and frustrating. It has become increasingly obvious that genomic data are often insufficient to reconstruct complex phenotypes in deep evolutionary time because too little is known about how gene function evolves over deep time. Therefore, additional functional data across the animal tree are a prerequisite to a fuller understanding of cell evolution. To this end, we review the functional modules of neurons and the evolution of their molecular components, and we introduce the idea of hierarchical molecular evolution. (Abstract)
Lopez-Larrea, Carlos, ed. Sensing in Nature. Dordrecht: Springer, 2012. A comprehensive collection across the creaturely scales and their relative cerebration as to how we all are aware, respond, interact, survive, and prevail. The abiding theme is a gradated consistency from the earliest rudiments to reflective humans. See for example Eusocial Evolution and Recognition Systems, Plant Communication, Identifying Self- and Nonself-Generated Signals, onto the Neurobiology of Sociability and Immune Systems Evolution. Concluding chapters The Neural Basis of Semantic and Episodic forms of Self-Knowledge by D’Argembean and Salmon, and Hallmarks of Consciousness by Ann Butler are reviewed separately.
Biological systems are an emerging discipline that may provide integrative tools by assembling the hierarchy of interactions among genes, proteins and molecular networks involved in sensory systems. The aim of this volume is to provide a picture, as complete as possible, of the current state of knowledge of sensory systems in nature. The presentation in this book lies at the intersection of evolutionary biology, cell and molecular biology, physiology and genetics. Sensing in Nature is written by a distinguished panel of specialists and is intended to be read by biologists, students, scientific investigators and the medical community. (Publisher)