Processing of time series by neural circuits with biologically realistic synaptic dynamics

Natschläger, T., Maass, W., Sontag, E. D., Zador, A. (2000) Processing of time series by neural circuits with biologically realistic synaptic dynamics. Neural Information Processing Systems Foundation.

URL: http://papers.nips.cc/paper/1903-processing-of-tim...

Abstract

Experimental data show that biological synapses behave quite differently from the symbolic synapses in common artificial neural network models. Biological synapses are dynamic, i.e., their "weight" changes on a short time scale by several hundred percent in dependence of the past input to the synapse. In this article we explore the consequences that these synaptic dynamics entail for the computational power of feedforward neural networks. We show that gradient descent suffices to approximate a given (quadratic) filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analysis which show that even with just a single hidden layer such networks can approximate a surprisingly large large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.

Item Type: Book
Subjects: organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits
organs, tissues, organelles, cell types and functions > cell types and functions > cell types > neurons > neuronal circuits

organs, tissues, organelles, cell types and functions > cell types and functions > cell functions > synaptic plasticity
CSHL Authors:
Communities: CSHL labs > Zador lab
Depositing User: Matt Covey
Date: 2000
Date Deposited: 04 Dec 2014 15:19
Last Modified: 04 Dec 2014 15:19
URI: http://repository.cshl.edu/id/eprint/30940

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