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Models of Large Networks: Analytic Approaches

  • Ronald J. MacGregor
  • Edwin R. Lewis

Abstract

Chapter 8 on neuromimes described models for simulating the activities of single or small numbers of neurons, say, less than about 20, while Chapter 11 considered techniques for analyzing up to hundreds or thousands of interconnected cells. In this and the next chapter we consider techniques for predicting the activity of vast numbers of interconnected neurons, say, from tens of thousands to millions or more. We will find that the various existing techniques fall into two categories: those that are essentially analytic in character, and those that are explicitly or primarily appropriate for realiza¬tion on large-scale, high-speed computers. Corresponding to these two categories, there are two main germinal contributions which have greatly influenced most subsequent work in their respective categories. The primary starting point for the analytic studies is a paper published in 1956 by Beurle, while that for the computer-oriented approaches is a classic paper by McCulloch and Pitts which appeared in 1943. In this chapter we take up the analytic approaches to large neural networks which stem largely from the work of Beurle.

Keywords

Large Network Excitatory Synapse Inhibitory Synapse Neural Code Classical Statistical Mechanic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1977

Authors and Affiliations

  • Ronald J. MacGregor
    • 1
  • Edwin R. Lewis
    • 2
  1. 1.University of ColoradoBoulderUSA
  2. 2.University of CaliforniaBerkeleyUSA

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