Abstract
In standard neural network models neurons are described in terms of mean firing rates, viz., an analog signal. Most real neurons, however, communicate by pulses, called action potentials, or simply ‘spikes’. In this chapter the main differences between spike coding and rate coding are described. The ‘integrate and fire’ model is studied as a simple model of a spiking neuron. Fast transients, synchrony, and coincidence detection are discussed as examples where spike coding is relevant. A description by spikes rather than rates has implications for learning rules. We show the relation of a spike time dependent learning rule to standard Hebbian learning. Finally, learning rule and temporal coding are illustrated using the example of a coincidence detecting neuron in the barn owl auditory system.
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Gerstner, W. (2001). What is Different with Spiking Neurons?. In: Mastebroek, H.A.K., Vos, J.E. (eds) Plausible Neural Networks for Biological Modelling. Mathematical Modelling: Theory and Applications, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0674-3_2
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DOI: https://doi.org/10.1007/978-94-010-0674-3_2
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