June 2006
Volume 6, Issue 6
Free
Vision Sciences Society Annual Meeting Abstract  |   June 2006
A discrete-time feedback model can account for spike timing data in LGN
Author Affiliations
  • Janneke F. M. Jehee
    Center for Visual Science, University of Rochester, and Department of Computer Science, University of Rochester
  • Dana H. Ballard
    Center for Visual Science, University of Rochester, and Department of Computer Science, University of Rochester
Journal of Vision June 2006, Vol.6, 887. doi:10.1167/6.6.887
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      Janneke F. M. Jehee, Dana H. Ballard; A discrete-time feedback model can account for spike timing data in LGN. Journal of Vision 2006;6(6):887. doi: 10.1167/6.6.887.

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Abstract

Evidence for the importance of spike timing is widespread. Synchronous activity has been revealed in single-cell recordings and EEG, and correlates with behavioral measures. Synchronous spikes are more effective in evoking a postsynaptic response, and synaptic plasticity critically depends on the precise timing of pre- and postsynaptic spikes.

Yet, the standard view of neural coding holds that information is coded by spike rate, ignoring correlated patterns of neuronal activity. This rate code strategy is corroborated by a huge number of studies that reveal the correlation of increased firing rate with behavioral measures.

How can these data be reconciled? We suggest that the cortex has adopted a signaling strategy that makes extensive use of spike timing for fast communication, but does it in a way that is consistent with the rate-code indications. Here, we show in simulations how this can be done. Specifically, we show that synchronous spike codes on both feed forward and feedback connections between cortical areas can be used to form oriented receptive fields given natural images as input. Moreover, we show that the decaying visual response of LGN neurons [1] is captured by our model, and that it critically depends on the model's feedback connections.

The novel feature of our spike model is that it combines synchronous updating of an over-complete number of cells together with a probabilistic spike-routing strategy. These features allow the reproduction of synchronicity measurements as well as classical rate-code features.

[1]. J.-M. Alonsoand, W. M. Ursrey and R. C Reid. (1996). Nature, 383, 815-819.

Jehee, J. F. M. Ballard, D. H. (2006). A discrete-time feedback model can account for spike timing data in LGN [Abstract]. Journal of Vision, 6(6):887, 887a, http://journalofvision.org/6/6/887/, doi:10.1167/6.6.887. [CrossRef]
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