Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study
Nicolas Pajot,
Mounir Boukadoum
Abstract:Solving classification problems by Liquid State Machines (LSM) usually ignores the influence of the liquid state representation on performance, leaving the role to the reader circuit. In most studies, the decoding of the internally generated neural states is performed on spike rate-based vector representations. This approach occults the interspike timing, a central aspect of biological neural coding, with potentially detrimental consequences on the LSM performance. In this work, we propose a model of liquid st… Show more
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