A CMOS circuit for STDP with a symmetric time window

In some spiking neuron models, analog information is expressed by the timing of neuronal spike firing events, and synaptic weights change depending on the relative timing between asynchronous spikes, which is called spike-timing dependent synaptic plasticity (STDP). In this paper, we propose an analog CMOS circuit for STDP with a symmetric time window and a Hopfield-type VLSI neural network using the synapse circuit. We have confirmed by circuit simulations that the network can perform autocorrelation learning from input spike patterns.