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c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Controlled Attractor Network Model of Path Integration in the Rat

By John Conklin

Abstract

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, subiculu

Year: 2004
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.1098
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