- Subject index
This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python.
Key Features include: Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spreadsheet methods; Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for context; Numerous worked examples to demonstrate the themes and procedures of cognitive modelling.
An excellent text for upper-level undergraduate and postgraduate students taking courses in research methods, computational neuroscience / computational modelling, and cognitive science / neuroscience. It will be especially valuable to psychology students.
Chapter 13: Auto-Associative Memory and the Hopfield Net
Auto-Associative Memory and the Hopfield Net
After reading this chapter you should be able to:
- compare the perceptron and Hopfield networks;
- understand how the Hopfield network can be considered a model of memory;
- understand the concept of a dynamical system;
- use different distance metrics; and
- create a simple implementation of the Hopfield net.
In this chapter we compare the Hopfield network to the perceptron and consider what it means for something to be ...