A Novel Learning Rule for Long-Term Plasticity of Short-Term Synaptic Plasticity Enhances Temporal Processing

It is well established that short-term synaptic plasticity (STP) of neocortical synapses is itself plastic - e.g., the induction of LTP and LTD tend to shift STP towards short-term depression and facilitation, respectively. What has not been addressed theoretically or experimentally is whether STP is "learned"; that is, is STP regulated by specific learning rules that are in place to optimize the computations performed at synapses, or, are changes in STP essentially an epiphenomenon of long-term plasticity? Here we propose that STP is governed by specific learning rules that operate independently and in parallel of the associative learning rules governing baseline synaptic strength. We describe a learning rule for STP and, using simulations, demonstrate that it significantly enhances the discrimination of spatiotemporal stimuli. Additionally we generate a set of experimental predictions aimed at testing our hypothesis.