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Lookup NU author(s): Dr Wei Xu,
Professor Stuart BakerORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model's output.
Author(s): Xu W, Baker SN
Publication type: Article
Publication status: Published
Journal: Frontiers in Computational Neuroscience
Online publication date: 01/12/2016
Acceptance date: 15/11/2016
Date deposited: 26/01/2017
ISSN (electronic): 1662-5188
Publisher: Frontiers Research Foundation
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