Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function.• Includes contributions by some of the most influential people in the field of computational neuroscience• Demonstrates how computational approaches are being used today to interpret experimental data• Covers a wide range of topics from single neurons, to neural systems, to abstract models of learning

The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the first edition. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain.

Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.

"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

Delve into the fascinating world of machine learning with this comprehensive guide, which unpacks the algorithms driving today's intelligent systems. From foundational concepts to advanced applications, this book is essential for anyone looking to understand the mechanics behind AI.

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

Improvements in task performance following practice can occur as a result of changes in distinct cognitive and neural processes. In some cases, we can improve our performance by selecting a more successful behavior that is already part of our available repertoire. Skill learning, on the other hand, refers to a slower process that results in improving the ability to perform a behavior, i.e., it involves the acquisition of a behavior that was not available to the controller before training. Skill learning can take place both in the sensory and in the motor domains. Sensory skill acquisition in perceptual learning tasks is measured by improvements in sensory acuity through practice-induced changes in the sensitivity of relevant neural networks. Motor skill is harder to define as the term is used whenever a motor learning behavior improves along some dimension. Nevertheless, we have recently argued that as in perceptual learning, acuity is an integral component in motor skill learning. In this special topic we set out to integrate experimental and theoretical work on perceptual and motor skill learning and to stimulate a discussion regarding the similarities and differences between these two kinds of learning.

We are all familiar with the feeling of being stuck when a problem we are faced with seems intractable and we are unable to find a solution. But sometimes, a new way of seeing the problem pops into the mind from out of the blue. The missing piece of the puzzle is found, the gap is filled, and the solution is now obvious. This is the insight experience - the Aha! Moment - which has been a source of fascination to those who study problem solving for centuries. Written by leading researchers from around the world, this volume explores cutting-edge perspectives on insight, the processes that underlie it, and the conditions that promote it. Chapters draw on key themes: from attention, to memory and learning, to evolutionary perspectives. Students and researchers in applied, cognitive, and educational psychology, as well as those studying creativity, insight, and cognitive neuroscience, will benefit from these perspectives.

This book provides an essential overview of computational neuroscience. It addresses a broad range of aspects, from physiology to nonlinear dynamical approaches to understanding neural computation, and from the simulation of brain circuits to the development of engineering devices and platforms for neuromorphic computation. Written by leading experts in such diverse fields as neuroscience, physics, psychology, neural engineering, cognitive science and applied mathematics, the book reflects the remarkable advances that have been made in the field of computational neuroscience, an emerging discipline devoted to the study of brain functions in terms of the information-processing properties of the structures forming the nervous system. The contents build on the workshop “Nonlinear Dynamics in Computational Neuroscience: from Physics and Biology to ICT,” which was held in Torino, Italy in September 2015.