Insights in computational neuroscience
Author: Si Wu
Publisher: Frontiers Media SA
Published: 2023-04-11
Total Pages: 150
ISBN-13: 2832520502
DOWNLOAD EBOOK →Lanterna Rally
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Computational Neuroscience: Theoretical Insights into Brain Function
Author: Paul Cisek
Publisher: Elsevier
Published: 2007-11-14
Total Pages: 570
ISBN-13: 9780080555027
DOWNLOAD EBOOK →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
Data-Driven Computational Neuroscience
Author: Concha Bielza
Publisher: Cambridge University Press
Published: 2020-11-26
Total Pages: 709
ISBN-13: 110849370X
DOWNLOAD EBOOK →Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications
Author: Alonso, Eduardo
Publisher: IGI Global
Published: 2010-11-30
Total Pages: 396
ISBN-13: 1609600231
DOWNLOAD EBOOK →"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"--
Fundamentals of Computational Neuroscience
Author: Thomas Trappenberg
Publisher: Oxford University Press
Published: 2010
Total Pages: 417
ISBN-13: 0199568413
DOWNLOAD EBOOK →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.
Recent insights into perceptual and motor skill learning (The computational and neural processes underlying perceptual and motor skill learning)
Author: Lior Shmuelof
Publisher: Frontiers Media SA
Published: 2015-03-18
Total Pages: 133
ISBN-13: 2889194469
DOWNLOAD EBOOK →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.
Algorithms of Intelligence: Exploring the World of Machine Learning
Author: Dr R. Keerthika
Publisher: Inkbound Publishers
Published: 2022-01-20
Total Pages: 224
ISBN-13: 8196822340
DOWNLOAD EBOOK →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.
Neural Engineering
Author: Chris Eliasmith
Publisher: MIT Press
Published: 2003
Total Pages: 384
ISBN-13: 9780262550604
DOWNLOAD EBOOK →A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
From Neuron to Cognition via Computational Neuroscience
Author: Michael A. Arbib
Publisher: MIT Press
Published: 2016-11-04
Total Pages: 808
ISBN-13: 0262335271
DOWNLOAD EBOOK →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
Computational Neuroscience
Author: Wanpracha Chaovalitwongse
Publisher: Springer Science & Business Media
Published: 2010-07-03
Total Pages: 330
ISBN-13: 0387886303
DOWNLOAD EBOOK →This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.
