Iono-neuromorphic implementation of spike-timing-dependent synaptic plasticity

Spike-timing-dependent plasticity (STDP) is the ability of a synapse to increase or decrease its efficacy in response to specific temporal pairing of pre- and post-synaptic activities. It is widely believed that such activity-dependent long-term changes in synaptic connection strength underlie the brain's capacity of learning and memory. However, current phenomenological models of STDP fail to reproduce classical forms of synaptic plasticity that are based on stimulus frequency (BCM rule) instead of timing (STDP rule). In this paper, we implemented a novel biophysical synaptic plasticity model by using analog VLSI (aVLSI) circuits biased in the subthreshold regime. We show that the aVLSI synapse model successfully emulates both the STDP and BCM forms of synaptic plasticity as predicted by the biophysical model.