Asynchronous and Non‐Periodic Spiking Data Processing in Parallel‐Synapse Leaky Integrate‐and‐Fire Neuron Circuit

Abstract Neuromorphic computing provides a promising alternative to conventional von Neumann architectures by addressing memory bottlenecks and energy inefficiencies. In this work, a hybrid memristor‐based neuromorphic system is presented, comprising a volatile Pt/V/AlO x /Pt threshold switching memristor (TSM) as the spiking neuron and a non‐volatile Ta/ZrO x /Pt resistive switching memristor (RSM) as the artificial synapse. The neuron circuit realizes a leaky‐integrate‐and‐fire (LIF) model with tunable synaptic weights and temporal encoding functions. The artificial synapses exhibit multi‐level resistance states and retention characteristics, ensuring long‐term conductance retention. By integrating the neuron circuit with multiple artificial synapses, it is demonstrated that membrane potential (V M ) dynamics are jointly governed by input strength and timing, thereby enabling precise modulation of spike generation. In addition, the TSM‐based neuron can also serve as a spike encoder, where the output firing rate or time‐to‐first‐spike (TTFS) is directly modulated by the amplitude of the input signal. This dual functionality allows the same circuit to function as both a computational unit and an analog‐to‐spike encoder. Overall, the circuit replicates essential neuronal dynamics, including subthreshold integration, temporal summation, and synchronized firing, validating its potential for scalable neuromorphic hardware implementation.