Evolving spiking neural networks for pattern classification problems - Shirin Dora (2017)
This thesis focuses on the development of new batch/online learning algorithms for evolving spiking neural networks that can be used for pattern classification problems. The input and output signals of spiking neurons consist of discrete events (spikes) in time. The inherent discontinuous nature of spikes is an issue in developing learning algorithms for spiking neurons. This has inspired many researchers to study plasticity mechanisms observed in the brain to develop efficient learning techniques for spiking neurons. Spike Timing Dependent Plasticity (STDP) is one of the most studied biological plasticity mechanism. STDP relies only on the locally available information to update the weights of a given synapse. The local nature of STDP may result in an unbalanced distribution of weights and lead to convergence issues.