A Memristive Synapse for Online STDP Learning and Inference in SNNs

2026-09-14

AI Summary

该研究提出了一种全模拟忆阻突触电路，用于脉冲神经网络中的在线STDP学习，通过局部STDP电路直接根据前后突触尖峰生成渐进的时序依赖性电导更新。

Abstract

This work presents a fully analog memristive synaptic circuit for online spike-timing-dependent plasticity (STDP) learning in spiking neural networks (SNNs). The proposed synapse integrates a local STDP circuit generating gradual timing-dependent conductance updates directly from pre- and post-synaptic spikes. Learning occurs during normal network operation without requiring external digital control or explicit STDP waveform synthesis. Post-layout simulations of the memristive synapse implemented in a 130 nm CMOS technology show spike-timing-dependent conductance adaptation during SNN operation. A 2x2 SNN simulation further illustrates online neuron specialization through unsupervised learning.

Problem

• STDP
• SNNs
• memristive synapse
• online learning
• unsupervised learning

Innovation

• memristive synapse
• online STDP learning
• spiking neural networks (SNNs)
• analog circuit