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Cited by 288

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns

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Gerstner, Wulfram, Ritz, Raphael, van Hemmen, J. Leo·1993·Biological Cybernetics
DOI 10.1007/bf00199450
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Abstract

Hebbian learning allows a network of spiking neurons to store and retrieve spatio-temporal patterns with a time resolution of 1 ms, despite the long postsynaptic and dendritic integration times. To show this, we introduce and analyze a model of spiking neurons, the spike response model, with a realistic distribution of axonal delays and with realistic postsynaptic potentials. Learning is performed by a local Hebbian rule which is based on the synchronism of presynaptic neurotransmitter release and some short-acting postsynaptic process. The time window of this synchronism determines the temporal resolution of pattern retrieval, which can be initiated by applying a short external stimulus pattern. Furthermore, a rate quantization is found in dependence upon the threshold value of the neurons, i.e., in a given time a pattern runsn times as often as learned, wheren is a positive integer (n ⩾ 0). We show that all information about the spike pattern is lost if only mean firing rates (temporal average) or ensemble activities (spatial average) are considered. An average over several retrieval runs in order to generate a post-stimulus time histogram may also deteriorate the signal. The full information on a pattern is contained in the spike raster of a single run. Our results stress the importance, and advantage, of coding by spatio-temporal spike patterns instead of firing rates and average ensemble activity. The implications regarding modelling and experimental data analysis are discussed.

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Metadata

Authors
Gerstner, W., Ritz, R., & van Hemmen, J. L.
Venue
Biological Cybernetics
Publisher
Springer Science and Business Media LLC
Published
1993-10
Volume
69
Issue
5-6
Pages
503-515
ISSN
  • 0340-1200 (ISSN-L)
  • 1432-0770
DOI
10.1007/bf00199450
OpenAlex
W2063810326
Cited by
288
References
45
FWCI
4.36
Type
journal article
Language
en
Open access
green

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