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).ContributorsMichael 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
|Cerebral Cortex: Principles of Operation|
by Edmund T. Rolls (Author)
"A book remarkable in its ambition, and even more remarkable in its content. A truly landmark achievement by a neuroscientist who has brought together his lifetime of research knowledge and experience into this outstanding volume. Edmund Rolls is to be congratulated on this impressive synthesis of decades of neuroscience data."
David Nutt, Professor of Neuropsychopharmacology at Imperial College London and President of the European Brain Council
The aim of this book is to provide...
|Deep Learning (Adaptive Computation and Machine Learning series)|
by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author)
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
-- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and...
|Brain Computation as Hierarchical Abstraction|
by Dana H. Ballard (Author)
The vast differences between the brain's neural circuitry and a computer's silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its...
|Tutorial on Neural Systems Modeling|
by Thomas J. Anastasio (Author)
Neural systems models are elegant conceptual tools that provide satisfying insight into brain function. The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer programming.
The book opens with an introduction to computer programming. Each of twelve subsequent chapters presents a...
|Principles of Neural Design (MIT Press)|
by Peter Sterling (Author), Simon Laughlin (Author)
Two distinguished neuroscientists distil general principles from more than a century of scientific study, "reverse engineering" the brain to understand its design.
Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading...
|Case Studies in Neural Data Analysis: A Guide for the Practicing Neuroscientist (Computational Neuroscience Series)|
by Mark A. Kramer (Author), Uri T. Eden (Author)
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data.
As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which...
|Fundamentals of Brain Network Analysis|
by Alex Fornito (Author), Andrew Zalesky (Author), Edward Bullmore (Author)
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and...
|Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)|
by Peter Dayan (Author), Laurence F. Abbott (Author)
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship...
|Visual Cortex and Deep Networks: Learning Invariant Representations (Computational Neuroscience Series)|
by Tomaso A. Poggio (Author), Fabio Anselmi (Author)
A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications.
The ventral visual stream is believed to underlie object recognition in primates. Over the past fifty years, researchers have developed a series of quantitative models that are increasingly faithful to the biological architecture. Recently, deep learning convolution networks -- which do not reflect several important features...
|Fundamentals of Computational Neuroscience|
by Thomas Trappenberg (Author)
Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the...