Leveraging Non-Equilibrium ECRAM Dynamics for Short-Term Plasticity in Neuromorphic Circuits

2026-05-11

AI Summary

This work proposes a device–circuit–system co-design methodology to efficiently implement short-term plasticity (STP) in neuromorphic hardware for temporal information processing. By harnessing the nonequilibrium ion dynamics of electrochemical random-access memory (ECRAM) devices—traditionally regarded as a defect—as an intrinsic STP mechanism, the authors construct leaky integrate-and-fire (LIF) neuron circuits with delayed feedback. This architecture enables concurrent dynamic modulation of synaptic weights and neuronal excitability through transient conductance changes. A compact behavioral model, calibrated against experimental characterization, is employed in circuit- and network-level simulations, demonstrating tunable temporal filtering and frequency-selective spiking responses at an ultralow energy cost of merely 2 pJ per spike. The approach proves adaptable across diverse neuronal topologies, offering a scalable solution for energy-efficient neuromorphic computing.

Abstract

Short-term plasticity (STP) is fundamental to temporal information processing in biological neural systems but remains difficult to realize efficiently in neuromorphic hardware. Memristive electrochemical random-access memory (ECRAM) devices naturally exhibit non-equilibrium ionic dynamics that produce transient conductance modulation; however, these behaviors are typically treated as undesirable variability or tolerated as side effects in memory-centric computing paradigms. In this work, we instead transform these volatile dynamics from a tolerated device artifact into a computational resource through a cross-layer device-circuit-system co-design framework. We introduce a delay-feedback leaky integrate-and-fire (LIF) neuron architecture co-designed with ECRAM synapses that exploits activity-dependent conductance modulation with negligible additional circuit overhead. The architecture integrates ECRAM-based synapses with a tunable delay-feedback spike-generation path, enabling transient device dynamics to directly modulate neuron excitability and synaptic efficacy. We used experimentally characterized ECRAM devices exhibiting transient conductance modulation (1.5 KOhms per spike) to develop a compact behavioral model suitable for circuit-level simulation. Circuit simulations demonstrate two key STP behaviors -- synaptic facilitation and intrinsic excitability modulation -- while consuming 2 pJ per spike, and the same device-driven mechanisms extend across multiple neuron topologies. Network-level analysis further demonstrates frequency-selective spike processing, allowing individual synapses to act as tunable temporal filters within spiking neural networks. This work demonstrates that non-equilibrium ECRAM dynamics can serve as a native hardware substrate for STP and temporal computation in neuromorphic circuits.

Problem

• short-term plasticity
• neuromorphic circuits
• ECRAM
• temporal information processing
• memristive devices

Innovation

• ECRAM
• short-term plasticity
• neuromorphic computing
• non-equilibrium dynamics
• spiking neural networks