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3 - Cell assemblies and serial computation in neural circuits

Published online by Cambridge University Press:  14 August 2009

By
Kenneth D. Harris
Edited by
Christian Holscher  and
Matthias Munk
Christian Holscher
Affiliation:
University of Ulster
Matthias Munk
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
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Summary

Introduction

Analogies between the brain and the digital computer have been out of fashion for a long time. The differences between brains and computers are numerous. Computers run pre-specified programs written by people. Computers store programs and data in specialized RAM circuits, and have one CPU (or at most a handful) which follows a coded list of instructions to the letter. A computer has a central clock, which allows all of its components to march through a program in lockstep. The brain, on the other hand, has billions of neurons operating in parallel, no central clock, no externally supplied list of instructions, and no separation of RAM and CPU. Although the inventors of the modern computer held the brain as a model, the analogy is rarely taken seriously today.

A more popular analogy for the brain in recent years has been artificial neural networks (ANNs). ANNs, although typically simulated on a digital computer, have an apparently more “brain-like” design. They consist of elements that function (at least a bit) like neurons, connected by “synapses” whose strength can be modified by the network's history. ANNs do not need an external program, but “learn” from a set of training examples. The most successful of these, the multilayer perceptron or “backprop” net, is good enough at generalizing from training examples to be used in real-world information processing tasks, by people who have no interest in how the brain works.

Type
Chapter
Information
Information Processing by Neuronal Populations , pp. 49 - 73
Publisher: Cambridge University Press
Print publication year: 2008

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