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Emergent Neural Computational Architectures Based on Neuroscience pp 285–295Cite as

Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

Abstract

Time delays are a ubiquitous feature of neuronal systems. Synaptic integration between spiking neurones is subject to time delays at the axonal and dendritic level. Recent evidence suggests that temporal coding on a millisecond time scale may be an important functional mechanism for synaptic integration. This study uses biophysical neurone models to examine the influence of dendritic and axonal conduction time delays on the sensitivity of a neurone to temporal coding in populations of synaptic inputs. The results suggest that these delays do not affect the sensitivity of a neurone to the presence of temporal correlation amongst input spike trains, and point to a mechanism other than electrotonic conduction of EPSPs to describe neural integration under conditions of large scale synaptic input. The results also suggest that it is the common modulation rather than the synchronous aspect of temporal coding in the input spike trains which neurones are sensitive to.

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Authors and Affiliations

  1. Department of Electronics, University of York, Heslington, YORK, YO, 10 5DD, UK

    David M. Halliday

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  1. David M. Halliday
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Editors and Affiliations

  1. Centre of Informatics, SCET, University of Sunderland, St PetersWay, Sunderland SR6 0DD, UK

    Stefan Wermter

  2. Department of Computer Science, University of York, York, Y010 5DD, UK

    Jim Austin

  3. Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, UK

    David Willshaw

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© 2001 Springer-Verlag Berlin Heidelberg

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Halliday, D.M. (2001). Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_21

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  • DOI: https://doi.org/10.1007/3-540-44597-8_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

  • eBook Packages: Springer Book Archive

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