  We present a fully memristive spiking neural network (MSNN) consisting of
physically-realizable memristive neurons and memristive synapses to implement
an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The
system is fully memristive in that both neuronal and synaptic dynamics can be
realized by using memristors. The neuron is implemented using the SPICE-level
memristive integrate-and-fire (MIF) model, which consists of a minimal number
of circuit elements necessary to achieve distinct depolarization,
hyperpolarization, and repolarization voltage waveforms. The proposed MSNN
uniquely implements STDP learning by using cumulative weight changes in
memristive synapses from the voltage waveform changes across the synapses,
which arise from the presynaptic and postsynaptic spiking voltage signals
during the training process. Two types of MSNN architectures are investigated:
1) a biologically plausible memory retrieval system, and 2) a multi-class
classification system. Our circuit simulation results verify the MSNN's
unsupervised learning efficacy by replicating biological memory retrieval
mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in
a large scale discriminative MSNN.
