Efficient temporal processing with biologically realistic dynamic synapses

Natschlger, T., Maass, W., Zador, A. M. (2001) Efficient temporal processing with biologically realistic dynamic synapses. Network: Computation in Neural Systems, 12 (1). pp. 75-87.

URL: https://www.ncbi.nlm.nih.gov/pubmed/11254083
DOI: 10.1080/net.


Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such 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 analyses which show that even with just a single hidden layer such networks can approximate a surprisingly 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: Paper
Subjects: bioinformatics > computational biology
organs, tissues, organelles, cell types and functions > sub-cellular tissues: types and functions > synapse
CSHL Authors:
Communities: CSHL labs > Zador lab
Depositing User: Leigh Johnson
Date: 2001
Date Deposited: 27 Mar 2012 20:54
Last Modified: 23 Feb 2017 20:53
Related URLs:
URI: https://repository.cshl.edu/id/eprint/25619

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