Neural bases of accumulator models

Abstract

One class of cognitive models, accumulator models (AMs), is a framework to explain cognitive mechanisms of reaction timing. In AMs, an internal signal, which grows linearly with time upon the onset of an external stimulus, is postulated. Reaction time is de-ned by the time at which the signal reaches a prede-ned threshold level. Indeed, cortical neurons with activity that grows seemingly linearly with time are largely found, which is consistent with AMs. Here we show that stochastic dynamics of a recurrent network of bistable spiking neurons can produce linear growth of neuronal activity. This suggests possible neural bases of AMs. c © 2003 Elsevier Science B.V. All rights reserved.

DOI: 10.1016/S0925-2312(02)00778-6

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Cite this paper

@article{Okamoto2003NeuralBO, title={Neural bases of accumulator models}, author={Hiroshi Okamoto and Tomoki Fukai}, journal={Neurocomputing}, year={2003}, volume={52-54}, pages={285-288} }