Fading Memory and Kernel Properties of Generic Cortical Microcircuit
W. Maass, T. Natschlaeger, and H. Markram
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 implementations of specific
computational operations and algorithms. This article is a sequel to
[#!LSM!#], and contains new results about the performance of generic neural
microcircuit models for the recognition of speech that is subject to linear
and nonlinear time-warps, as well as for computations on time-varying firing
rates. These computations rely, apart from general properties of generic
neural microcircuit models, just on capabilities of simple linear readouts
trained by linear regression. This article also provides detailed data on the
fading memory property of generic neural microcircuit models, and a quick
review of other new results on the computational power of such circuits of
Reference: W. Maass, T. Natschlaeger, and H. Markram.
Fading memory and kernel properties of generic cortical microcircuit models.
Journal of Physiology - Paris, 98(4-6):315-330, 2004.