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 di#erences 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. Keywords: temporal coding, coincidence detection, spikes, spiking neurons, integrateand -fire neurons, auditory system, Hebbian learning, spike-time dependent plasticity 1. SPIKES AND RATES In mos...

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