Improving Associative Memory in a Network of Spiking Neurons

  • Authors:

  • Russell Hunter;Stuart Cobb;Bruce P. Graham

  • Affiliations:

  • Department of Computing Science and Mathematics, University of Stirling, Stirling, U.K. FK9 4LA;Division of Neuroscience and Biomedical Systems, University of Glasgow, Glasgow, U.K. G12 8QQ;Department of Computing Science and Mathematics, University of Stirling, Stirling, U.K. FK9 4LA

  • Venue:
  • ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
  • Year:
  • 2008

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Abstract

Associative neural network models are a commonly used methodology when investigating the theory of associative memory in the brain. Comparisons between the mammalian hippocampus and neural network models of associative memory have been investigated [7]. Biologically based networks are complex systems built of neurons with a variety of properties. Here we compare and contrast associative memory function in a network of biologically-based spiking neurons [14] with previously published results for a simple artificial neural network model [6]. We investigate biologically plausible implementations of methods for improving recall under biologically realistic conditions, such as a sparsely connected network.