Computational Neuroscience Series

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Biological Learning and Control

How the Brain Builds Representations, Predicts Events, and Makes Decisions

by Reza Shadmehr and Sandro Mussa-Ivaldi

ISBN: 9780262549554

Publisher: The MIT Press

Pub Date: October 31, 2023

A novel theoretical framework that describes a possible rationale for the regularity in how we move, how we learn, and how our brain predicts events.

Neural Control Engineering

The Emerging Intersection between Control Theory and Neuroscience

by Steven J. Schiff

ISBN: 9780262546713

Publisher: The MIT Press

Pub Date: November 1, 2022

How powerful new methods in nonlinear control engineering can be applied to neuroscience, from fundamental model formulation to advanced medical applications.

An Introductory Course in Computational Neuroscience

by Paul Miller

ISBN: 9780262038256

Publisher: The MIT Press

Pub Date: October 2, 2018

A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior.

Dynamical Systems in Neuroscience

The Geometry of Excitability and Bursting

by Eugene M. Izhikevich

ISBN: 9780262514200

Publisher: The MIT Press

Pub Date: January 22, 2010

Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition.

From Neuron to Cognition via Computational Neuroscience

Edited by Michael A. Arbib and James J. Bonaiuto

ISBN: 9780262034968

Publisher: The MIT Press

Pub Date: November 11, 2016

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.

Visual Cortex and Deep Networks

Learning Invariant Representations

by Tomaso A. Poggio and Fabio Anselmi

ISBN: 9780262034722

Publisher: The MIT Press

Pub Date: September 23, 2016

A mathematical framework that describes learning of invariant representations in the ventral stream, offering both theoretical development and applications.

Brain Computation as Hierarchical Abstraction

by Dana H. Ballard

ISBN: 9780262534123

Publisher: The MIT Press

Pub Date: February 20, 2015

An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is.

Bayesian Brain

Probabilistic Approaches to Neural Coding

Edited by Kenji Doya, Shin Ishii, Alexandre Pouget and Rajesh P.N. Rao

ISBN: 9780262516013

Publisher: The MIT Press

Pub Date: January 21, 2011

Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Theoretical Neuroscience

Computational and Mathematical Modeling of Neural Systems

by Laurence F. Abbott and Peter Dayan

ISBN: 9780262041997

Publisher: The MIT Press

Pub Date: October 26, 2001

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...