The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons.

Despite the success of artificial neural networks in solving numerous tasks, they face significant challenges, including difficulties in online adaptation and rapidly increasing energy consumption. As a biologically plausible alternative, spiking neural networks offer promising capabilities for efficient cognitive computing. Recently, a three-element spiking neuron model consisting of a threshold selector, a tunnel diode, and a capacitor was proposed. In this work, we experimentally validate this model using a threshold selector hardware emulator and demonstrate its dynamical equivalence to the biologically plausible Izhikevich neuron model. To evaluate the novel neuron’s applicability for cognitive computing, we implement a liquid state machine (LSM) reservoir architecture with spatially dependent random topology for synaptic weight distribution. Our simulations on the MNIST and Fashion-MNIST benchmarks demonstrate competitive classification accuracy (97.9% and 89.5%, respectively) while offering estimated energy efficiency and processing speed enhancements compared to existing FPGA-based and memristor-based spiking reservoir implementations. The developed reservoir is feasible for processing neuromorphic sensors output, including visual perception tasks.

This paper investigates new computer architectures for the hardware implementation of dynamic (spiking) neural networks capable to replace up-to-date networks built on neurons with a static activation function. We propose for the first time the use of a recently developed compact analog model of a spiking neuron, consisting of only three elements (a volatile memristor, a tunnel diode, and a capacitor), as the basic element of a reservoir computer of Liquid State Machine (LSM) type. A computer model of the reservoir is proposed, including 7,480 neurons and approximately 254,000 connections, with a topology formed using the biologically motivated LSM stochastic synapse distribution algorithm. The results of the proposed solution are demonstrated on the task of recognizing handwritten digits from the MNIST dataset. A classification accuracy of 93 % is achieved, which is comparable to known LSM implementations. Estimates for the proposed reservoir performance of the future hardware implementation exceed those of existing analogs by an order, and in terms of energy efficiency by 3-4 orders. Thus, the proposed study demonstrates for the first time the practical applicability of the three-element neuron model for machine learning tasks and confirms its potential as a basic element for constructing scalable and energy-efficient neuromorphic computing systems.