VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies
1. The Evolution of Cerebral Form and Cognizance
Innocenti, Giorgio and Jon Kaas. The Cortex. Trends in Neuroscience. 18/9, 1995. A special issue devoted to the evolutionary emergence of the mammalian neocortex. There is a homologous correspondence and similarity among species in brain design with regard to overall dimensions and the number of connected neurons.
Iwaniuk, Andrew, et al.
A Mosaic Pattern Characterizes the Evolution of the Avian Brain.
Proceedings of the Royal Society of London B.
Biology Letters S. 4,
Two main theories are in play for how brains evolve and grow in size. The “developmental constraints” view says that modular brain regions scale-up together. A “mosaic” school argues that these specific areas change in size independently of each other. The authors lean toward the latter for birds, but advise that both modes are variously going on during vertebrate cerebral evolution.
Jerison, Harry. On the Evolution of Mind. David Oakley, ed. Brain and Mind. London: Metheun, 1985. A pioneer in the field explains how large brains, as information processing systems, are arranged in a nested hierarchical fashion.
Kaas, Jon, editor-in-chief. Evolution of Nervous Systems. Amsterdam: Elsevier/Academic Press, 2nd edition, 2016. An authoritative encyclopedia with these 4 volumes: Theories, Development, Invertebrates; Non-Mammalian Vertebrates; Mammals; and Primates. Its especial value is to cover, as the publisher avers, every aspect of how life evolved from original sensation through the ramifying Metazoa to our human cerebral neocortex. And one might add, as our humankinder phase views retrospectively from which we all came, the ascendant course well seems as a single cognitive gestation.
Evolution of Nervous Systems, Second Edition is a unique, major reference for evolution and nervous systems. All animals mediate their behaviors, many of them species specific, yet these sensory abilities all evolved from a common, simpler ancestor. In the first edition, over 100 distinguished neuroscientists assembled the current state-of-the-art knowledge on how nervous systems have evolved throughout the animal kingdom. This second edition remains rich in detail and broad in scope, outlining the changes in brain and nervous system organization that occurred from the first invertebrates and vertebrates, to fishes, reptiles, birds, mammals, and primates, including humans. The book also includes fully updated chapters and new content on developments in the field. (Publisher)
Kaas, Jon, editor-in-chief.
Amsterdam: Academic Press,
The 1,000 page compendium could be seen as an evolutionary event in itself as decades of international brain research studies reach a mature confluence. Four parts: An Introduction to History, Theory, Methods and Concepts; The Evolution of Brains in Early Vertebrates, Fishes, Amphibians, Reptile and Birds; and Evolution of Mammalian Brains; and Primate Brain Evolution, explain an ancient developmental enchepalization from pre-Cambrium creatures to regnant human beings. A lead chapter “History of Ideas on Brain Evolution” by Georg Striedter introduces major themes that can now be gleaned from this achievement (search also Cela-Conde herein). Other notable chapters are “The Evolution of Vocal Learning Systems in Birds and Humans” by Erich Jarvis, “How can Fossils Tell us About the Evolution of the Neocortex?” by Harry Jerison, and “The Evolution of Language Systems in the Human Brain” by Terrence Deacon.
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)