@inproceedings{f60aa7d90f994bc698d2429343426cee,
title = "Processing of time series by neural circuits with biologically realistic synaptic dynamics",
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.",
author = "Thomas Natschl{\"a}ger and Wolfgang Maass and Sontag, {Eduardo D.} and Anthony Zador",
year = "2001",
language = "English (US)",
isbn = "0262122413",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
booktitle = "Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000",
note = "14th Annual Neural Information Processing Systems Conference, NIPS 2000 ; Conference date: 27-11-2000 Through 02-12-2000",
}