Journal of Computational Neuroscience

, Volume 18, Issue 2, pp 183–203 | Cite as

A Controlled Attractor Network Model of Path Integration in the Rat

Article

Abstract

Cells in several areas of the hippocampal formation show place specific firing patterns, and are thought to form a distributed representation of an animal’s current location in an environment. Experimental results suggest that this representation is continually updated even in complete darkness, indicating the presence of a path integration mechanism in the rat. Adopting the Neural Engineering Framework (NEF) presented by Eliasmith and Anderson (2003) we derive a novel attractor network model of path integration, using heterogeneous spiking neurons. The network we derive incorporates representation and updating of position into a single layer of neurons, eliminating the need for a large external control population, and without making use of multiplicative synapses. An efficient and biologically plausible control mechanism results directly from applying the principles of the NEF. We simulate the network for a variety of inputs, analyze its performance, and give three testable predictions of our model.

Keywords

path integration attractor network neural control hippocampus subiculum 

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Systems Design EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Philosophy and Systems Design EngineeringUniversity of WaterlooWaterlooCanada

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