  Spike-timing-dependent plasticity (STDP) incurs both causal and acausal
synaptic weight updates, for negative and positive time differences between
pre-synaptic and post-synaptic spike events. For realizing such updates in
neuromorphic hardware, current implementations either require forward and
reverse lookup access to the synaptic connectivity table, or rely on
memory-intensive architectures such as crossbar arrays. We present a novel
method for realizing both causal and acausal weight updates using only forward
lookup access of the synaptic connectivity table, permitting memory-efficient
implementation. A simplified implementation in FPGA, using a single timer
variable for each neuron, closely approximates exact STDP cumulative weight
updates for neuron refractory periods greater than 10 ms, and reduces to exact
STDP for refractory periods greater than the STDP time window. Compared to
conventional crossbar implementation, the forward table-based implementation
leads to substantial memory savings for sparsely connected networks supporting
scalable neuromorphic systems with fully reconfigurable synaptic connectivity
and plasticity.
