Triplet Spike Time-Dependent Plasticity in a Floating-Gate Synapse

Synapse plays an important role in learning in a neural network; the learning rules that modify the synaptic strength based on the timing difference between the preand postsynaptic spike occurrence are termed spike time-dependent plasticity (STDP) rules. The most commonly used rule posits weight change based on time difference between one presynaptic spike and one postsynaptic spike and is hence termed doublet STDP (D-STDP). However, D-STDP could not reproduce results of many biological experiments; a triplet STDP (T-STDP) that considers triplets of spikes as the fundamental unit has been proposed recently to explain these observations. This paper describes the compact implementation of a synapse using a single floating-gate (FG) transistor that can store a weight in a nonvolatile manner and demonstrates the T-STDP learning rule by modifying drain voltages according to triplets of spikes. We describe a mathematical procedure to obtain control voltages for the FG device for T-STDP and also show measurement results from an FG synapse fabricated in TSMC 0.35-μm CMOS process to support the theory. Possible very large scale integration implementation of drain voltage waveform generator circuits is also presented with the simulation results.