| On the computational power of circuits of spiking neurons (2003) | |||||||||||||||||
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| It is quite difficult to construct circuits of spiking neurons that can carry out complex computational tasks. On the other hand even randomly connected circuits of spiking neurons can in principle be used for complex computational tasks such as time-warp invariant speech recognition. This is possible because such circuits have an inherent tendency to integrate incoming information in such a way that simple linear readouts can be trained to transform the current circuit activity into the target output for a very large number of computational tasks. Consequently we propose to analyze circuits of spiking neurons in terms of their roles as analog fading memory and nonlinear kernels, rather than as im-plementations of specific computational operations and algorithms. This article is a sequel to [31], and contains new results about the performance of generic neural microcircuit models for the recognition of speech that is subject to linear | |||||||||||||||||
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