Spiking neural network circuit with op-amp based LIF neuron and RRAM synaptic array - M.V., Eashwar; T., Nivetha; B., Bindu; Kamsani, Noor Ain (Elsevier GmbH, 2025)
Spiking Neural Networks (SNNs) have become crucial in neuromorphic computing for efficiently processing the vast amounts of digital data generated in the technology-driven world. The resistive RAM (RRAM) based SNNs offer superior energy efficiency, high-speed processing, parallelism, and scalability for neuromorphic computing applications. In this article, an SNN circuit with a 1T-1R RRAM synaptic array along with op-amp and 555 timer-based leaky integrate-and-fire (LIF) neuron is implemented to use for pattern recognition. The input pre-spikes from the pattern are applied to the RRAM synaptic array, which exhibits synaptic plasticity. The LIF neuron processes the synaptic array output to produce post-spikes, which modify the conductance of the RRAM synaptic array based on the spike-timing-dependent plasticity (STDP) mechanism. The unique output spikes obtained for different characters can be used for pattern recognition of the characters.