Efficient Design of Triplet Based Spike-Timing Dependent Plasticity
This work addresses a bottleneck in neuromorphic computing by enabling more biologically accurate synaptic plasticity, though it is incremental as it builds on existing STDP models.
The authors tackled the inability of classical spike-timing dependent plasticity (STDP) models to replicate complex biological synaptic changes by developing a new VLSI circuit based on triplets of spikes, which successfully reproduces experimental results from higher-order spike trains.
Spike-Timing Dependent Plasticity (STDP) is believed to play an important role in learning and the formation of computational function in the brain. The classical model of STDP which considers the timing between pairs of pre-synaptic and post-synaptic spikes (p-STDP) is incapable of reproducing synaptic weight changes similar to those seen in biological experiments which investigate the effect of either higher order spike trains (e.g. triplet and quadruplet of spikes), or, simultaneous effect of the rate and timing of spike pairs on synaptic plasticity. In this paper, we firstly investigate synaptic weight changes using a p-STDP circuit and show how it fails to reproduce the mentioned complex biological experiments. We then present a new STDP VLSI circuit which acts based on the timing among triplets of spikes (t-STDP) that is able to reproduce all the mentioned experimental results. We believe that our new STDP VLSI circuit improves upon previous circuits, whose learning capacity exceeds current designs due to its capability of mimicking the outcomes of biological experiments more closely; thus plays a significant role in future VLSI implementation of neuromorphic systems.