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Presentation Summary

**Title : ** Learning simple computations in spiking basis function networks using spike time-dependent plasticity

**Authors : ** Andrew Davison

**Year : ** 2004

**Supported by : **
SenseMaker

Abstract

Spatial variables, such as the location of a stimulus in space, are commonly represented in the brain by population codes. Denève, Latham and Pouget (2001; Nature Neurosci 4:826-831) have shown that basis function networks can perform a variety of computations on population-encoded variables. This approach has proven to be very successful and could, for example, be used to perform coordinate transformations. However, the model was based on firing-rate representations of neuronal activity, and the synaptic weights were either calculated or learned using classical neural network learning algorithms such as the delta rule. In order to make the approach more biologically realistic, we implemented models of basis function networks with spiking neurons and with spike timing-dependent synaptic plasticity. These models are able to learn the connection weights required to compute coordinate transformations using population codes, in a manner equivalent to rate-based models but with a greater degree of biological realism. These results support the hypothesis that the brain may indeed use such principles for performing computations. Supported by EU project IST-2001-34712.