Abstract
Increasing evidence indicates that biological neurons process information conveyed by the precise timings of individual spikes. Such observations have prompted studies on artificial networks of spiking neurons, or Spiking Neural Networks (SNNs), that use temporal encodings to represent input features. Potentially, SNNs used in this way are capable of increased computational power in comparison with rate-based networks. This thesis investigates general learning methods for SNNs which utilise the timings of single and multiple output spikes to encode information. To this end, three distinct contributions to SNN learning are made as follows. The first contribution is a proposed reward-modulated synaptic plasticity method for training SNNs to learn sequences of precisely-timed output spikes in response to spatio-temporal input patterns. Results demonstrate the high temporal accuracy of this method, even when synaptic weights in the network are modified by a delayed feedback signal. This method is potentially of biological significance, since synaptic strength modifications have been observed to be modulated by a reward signal, such as dopamine, in the nervous system. The second contribution proposes two new supervised learning rules for SNNs that perform input-output transformations of spatio-temporal spike patterns. Simulations demonstrate the rules are capable of encoding large numbers of input patterns as precisely timed output spikes, comparing favourably with existing work. The final contribution is a new supervised learning rule, termed MultilayerSpiker, for training SNNs containing hidden layers of spiking neurons to temporally encode spatio-temporal spike patterns using single or multiple output spikes. Simulations show MultilayerSpiker supports a very large number of encodings, that is a substantial improvement over existing spike-based multilayer rules, and provides increased classification accuracy when using the timings of multiple rather than single output spikes to identify input patterns.