  We present a digital implementation of the Spike Timing Dependent Plasticity
(STDP) learning rule. The proposed digital implementation consists of an
exponential decay generator array and a STDP adaptor array. On the arrival of a
pre- and post-synaptic spike, the STDP adaptor will send a digital spike to the
decay generator. The decay generator will then generate an exponential decay,
which will be used by the STDP adaptor to perform the weight adaption. The
exponential decay, which is computational expensive, is efficiently implemented
by using a novel stochastic approach, which we analyse and characterise here.
We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors
and decay generators with only one physical implementation of each. We have
validated our stochastic STDP approach with measurement results of a balanced
excitation/inhibition experiment. Our stochastic approach is ideal for
implementing the STDP learning rule in large-scale spiking neural networks
running in real time.
