From Neuron To Cognition Via Computational Neuroscience Pdf
Download From Neuron To Cognition Via Computational Neuroscience Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get From Neuron To Cognition Via Computational Neuroscience Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
From Neuron to Cognition via Computational Neuroscience
A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille
Gradient Expectations
An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems. Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today’s deep learning. Digging into the connections between predictions and gradients, and their manifestation in the brain and neural networks, is one compelling example of how Downing enriches both our understanding of such relationships and their role in strengthening AI tools. Synthesizing critical research in neuroscience, cognitive science, and connectionism, Gradient Expectations offers unique depth and breadth of perspective on predictive neural-network models, including a grasp of predictive neural circuits that enables the integration of computational models of prediction with evolutionary algorithms.
Cognitive Science: Recent Advances and Recurring Problems
This book consists of an edited collection of original essays of the highest academic quality by seasoned experts in their fields of cognitive science. The essays are interdisciplinary, drawing from many of the fields known collectively as “the cognitive sciences.” Topics discussed represent a significant cross-section of the most current and interesting issues in cognitive science. Specific topics include matters regarding machine learning and cognitive architecture, the nature of cognitive content, the relationship of information to cognition, the role of language and communication in cognition, the nature of embodied cognition, selective topics in visual cognition, brain connectivity, computation and simulation, social and technological issues within the cognitive sciences, and significant issues in the history of neuroscience. This book will be of interest to both professional researchers and newer students and graduate students in the fields of cognitive science—including computer science, linguistics, philosophy, psychology and neuroscience. The essays are in English and are designed to be as free as possible of technical jargon and therefore accessible to young scholars and to scholars who are new to the cognitive neurosciences. In addition to several entries by single authors, the book contains several interesting roundtables where researchers contribute answers to a central question presented to those in the focus group on one of the core areas listed above. This exciting approach provides a variety of perspectives from across disciplines on topics of current concern in the cognitive sciences.