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The Computational Brain$
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Patricia S. Churchland and Terrence J. Sejnowski

Print publication date: 2016

Print ISBN-13: 9780262533393

Published to MIT Press Scholarship Online: January 2018

DOI: 10.7551/mitpress/9780262533393.001.0001

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Computational Overview

Computational Overview

(p.61) 3 Computational Overview
The Computational Brain

Patricia S. Churchland

Terrence J. Sejnowski

The MIT Press

This chapter provides an overview of computational principles that may be useful when addressing the question of computation in nervous systems as well as questions of biological systems. It begins by introducing several key mathematical concepts, including “function,” and the distinction between computable and noncomputable functions, and between linear and nonlinear functions. It then considers a number of computational principles, such as the look-up table and linear associators, before discussing a new type of principle that can accomplish the satisfaction of constraints by a process of “relaxation.” In particular, it describes Hopfield networks and Boltzmann machines. It also examines learning in neural nets, competitive learning, curve fitting, feedforward nets, and recurrent nets. Finally, it assesses the importance of optimization procedures to neuroscience, along with the use of realistic and abstract network models in neuroscience.

Keywords:   computational principles, computation, nervous systems, function, look-up table, linear associators, constraints, learning, neural nets, network models

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