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Spike Based Information Processing in Spiking Neural Networks

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Proceedings of the 4th International Conference on Applications in Nonlinear Dynamics (ICAND 2016) (ICAND 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 6))

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Abstract

Spiking neural networks are seen as the third generation of neural networks and the closest emulators of their biological counter parts. These networks use spikes as means of transmitting information between neurons. We study the merits and capacity of information transfer using spikes across different encoding and decoding schemes and show that spatio-temporal encoding scheme provides a very high efficiency in information transfer. We then explore learning rules based on neural dynamics that enable learning of spatio-temporal spike patterns. We explore various learning rules that can be used to learn spatio-temporal spike patterns.

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Notes

  1. 1.

    The original experimental findings of Adrian E [1] were reported as the number of spikes within a time window after the stimulus onset and so it is probably more accurate to associate those measurements as count code.

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Acknowledgements

The authors would like to thank Gert Cauwenberghs, Giacomo Indiveri, Elisabetta Chicca and Martin Coath for their invaluable feedback and comments on this manuscript.

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Authors and Affiliations

  1. BioCircuits Institute, UCSD, San Diego, USA

    Sadique Sheik

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  1. Sadique Sheik
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Correspondence to Sadique Sheik .

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Editors and Affiliations

  1. Code 2363, Space and Naval Warfare Systems Center Code 2363, San Diego, California, USA

    Visarath In

  2. Code 2363, Space and Naval Warfare Systems Center , San Diego, California, USA

    Patrick Longhini

  3. Department of Mathematics & Statistics, San Diego State University , San Diego, California, USA

    Antonio Palacios

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Sheik, S. (2017). Spike Based Information Processing in Spiking Neural Networks. In: In, V., Longhini, P., Palacios, A. (eds) Proceedings of the 4th International Conference on Applications in Nonlinear Dynamics (ICAND 2016). ICAND 2016. Lecture Notes in Networks and Systems, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-52621-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-52621-8_16

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