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A model of stimulus-specific adaptation in neuromorphic analog VLSI


Mill, R; Sheik, S; Indiveri, G; Denham, S L (2011). A model of stimulus-specific adaptation in neuromorphic analog VLSI. IEEE Transactions on Biomedical Circuits and Systems, 5(5):413-419.

Abstract

Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron decreases with repetitions of the same stimulus, and recovers when a different stimulus is presented. SSA therefore effectively highlights rare events in stimulus sequences, and suppresses responses to repetitive ones. In this paper we present a model of SSA based on synaptic depression and describe its implementation in neuromorphic analog very-large-scale integration (VLSI). The hardware system is evaluated using biologically realistic spike trains with parameters chosen to reflect those of the stimuli used in physiological experiments. We examine the effect of input parameters and stimulus history upon SSA and show that the trends apparent in the results obtained in silico compare favorably with those observed in biological neurons.

Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron decreases with repetitions of the same stimulus, and recovers when a different stimulus is presented. SSA therefore effectively highlights rare events in stimulus sequences, and suppresses responses to repetitive ones. In this paper we present a model of SSA based on synaptic depression and describe its implementation in neuromorphic analog very-large-scale integration (VLSI). The hardware system is evaluated using biologically realistic spike trains with parameters chosen to reflect those of the stimuli used in physiological experiments. We examine the effect of input parameters and stimulus history upon SSA and show that the trends apparent in the results obtained in silico compare favorably with those observed in biological neurons.

Citations

6 citations in Web of Science®
7 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:Analog very-large-scale integration (VLSI);Neuromorphic hardware;Oddball sequence;Stimulus specific adaptation;Synaptic depression
Language:English
Date:1 October 2011
Deposited On:05 Mar 2012 13:57
Last Modified:05 Apr 2016 15:42
Publisher:IEEE
ISSN:1932-4545
Publisher DOI:10.1109/TBCAS.2011.2163155

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