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Cerebral cortex is probably the most complex biological network. Here many millions of individual neurons, the functional units of cortex, are interconnected through a massive yet highly organized pattern of axonal and dendritic wiring. This wiring enables both near and distant cells to coordinate their responses and generate a rich variety of cognitions and behaviours. When the wiring is damaged through disease or trauma it may reorganize but this may lead to characteristic pathological behaviours. While there have been significant advances in mapping cortical connectivity, the organizing principles and function of this connectivity are not well understood. On the one hand, there appears to be general design constraints governing cortical wiring, as first recognised by Rámon y Cajal's in his laws of conduction, material, and volume conservation. Yet on the other hand, particular patterns of cortical wiring exist to serve specific functions. There is a wide gap in understanding how the response and connectivity properties of a single neuron contribute to emergent network functions such as in detecting perceptually relevant features. Unravelling this intimate causal relationship represents one of the major challenges in neuroscience. This Research Topic will examine progress in understanding cortical wiring principles. This Research Topic aims to draw together recent advances in methods and understanding as well as recent challenges to existing ideas about how cerebral cortex is wired. This is particularly timely because new automated techniques may soon yield huge datasets in need of explanation. Recent studies have, for instance, empirically evaluated Rámon y Cajal's conservation laws for cerebral cortex, while others have shown some unexpected connectivity features that may refine the traditional view of how corticocortical connections are organised with regard to functional representations of auditory, somatosensory and visual cortices. Understanding these data will help improve the fidelity of neural models of cerebral cortical function and take into account the diversity of connections at both micro- and mesoscopic scales not seen at such a depth before.

brain connectivity --- morphology --- Axon --- Cerebral Cortex --- connectome --- sensory systems --- Grey Matter --- Brain wiring --- networks --- Dendrite

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What neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?As Peter Robin Hiesinger argues, “the information problem” underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development—and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of “grown” networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.

Neural networks (Computer science) --- Learning --- Physiological aspects. --- Gary Macus. --- How to Create a Mind. --- Peter Sterling. --- Principles of Neural Design. --- Ray Kurzweil. --- Roger Sperry. --- Seymour Benzer. --- Simon Laughlin. --- Sydney Brenner. --- The Birth of the Mind. --- algorithm. --- algorithmic growth. --- artificial life. --- artificial neural network. --- axon guidance. --- behavior. --- brain development. --- brain wiring. --- cellular automaton. --- cognitive bias. --- complexity. --- computer intelligence. --- connectome. --- cybernetics. --- deep learning. --- evolution. --- filopodia. --- gene. --- guidance cue. --- information theory. --- machine learning. --- memory. --- neural circuit. --- neurogenetics. --- self-organization. --- synapse.