  Spiking neural networks (SNN) are considered as a perspective basis for
performing all kinds of learning tasks - unsupervised, supervised and
reinforcement learning. Learning in SNN is implemented through synaptic
plasticity - the rules which determine dynamics of synaptic weights depending
usually on activity of the pre- and post-synaptic neurons. Diversity of various
learning regimes assumes that different forms of synaptic plasticity may be
most efficient for, for example, unsupervised and supervised learning, as it is
observed in living neurons demonstrating many kinds of deviations from the
basic spike timing dependent plasticity (STDP) model. In the present paper, we
formulate specific requirements to plasticity rules imposed by unsupervised
learning problems and construct a novel plasticity model generalizing STDP and
satisfying these requirements. This plasticity model serves as main logical
component of the novel supervised learning algorithm called SCoBUL (Spike
Correlation Based Unsupervised Learning) proposed in this work. We also present
the results of computer simulation experiments confirming efficiency of these
synaptic plasticity rules and the algorithm SCoBUL.
