PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models
Alexander Kaske and Wolfgang Maass
Neural Networks 2004.

Abstract

It is shown that real-time computations on spike patterns and temporal integration of information is compatible with oscillatory background inputs to generic neural microcircuit models. A minor change in the connection statistics of such circuits (making synaptic connections to more distal target neurons more likely for excitatory than for inhibitory neurons) endows such generic neural microcircuit model with the ability to generate periodic patterns autonomously. We show that such pattern generation can also be multiplexed with pattern classification and temporal integration of information in the same neural circuit. These results can be interpreted as showing that periodic activity provides a second channel for communication in neural systems which can be used to synchronize or coordinate spatially separated processes, without encumbering local real-time computations on spike trains in diverse neural circuits.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:568
Deposited By:Wolfgang Maass
Deposited On:26 December 2004