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Related Experiment Video

Updated: Jul 19, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Memristor-based spiking neural network with online reinforcement learning.

Danila Vlasov¹, Anton Minnekhanov¹, Roman Rybka²

¹NRC "Kurchatov Institute", Akademika Kurchatova sq., 1 Moscow, Russian Federation.

Neural Networks : the Official Journal of the International Neural Network Society

|August 14, 2023

Summary

This study introduces an online reinforcement learning algorithm for spiking neural networks (SNNs) using memristor-based synapses. The novel algorithm enables efficient, real-time learning in neuromorphic systems for control tasks.

Keywords:

Conductive filament Memristor Reinforcement learning Resistive switching STDP Spiking neural network

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Last Updated: Jul 19, 2025

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Area of Science:

Neuromorphic Engineering
Artificial Intelligence
Materials Science

Background:

Memristor-based hardware offers efficient in-memory computation for neural networks.
Traditional learning methods like back-propagation are challenging for memristor hardware.
Spiking neural networks (SNNs) enable local, self-organized weight changes, suitable for such hardware.

Purpose of the Study:

To develop an online reinforcement learning algorithm for SNNs implemented on memristor hardware.
To integrate STDP-like learning rules derived from real memristor devices.
To demonstrate the feasibility of real-time agent learning in continuous-time environments using neuromorphic systems.

Main Methods:

An online reinforcement learning algorithm was developed where connection weights update after each environment state interaction.
The algorithm was applied to SNNs utilizing memristor-based STDP-like learning rules.
Plasticity functions were derived from experimentally assembled poly-p-xylylene and CoFeB-LiNbO3 nanocomposite memristors.

Main Results:

The SNN, composed of leaky integrate-and-fire neurons, successfully solved the Cart-Pole benchmark task.
Environmental states were encoded by input spike timings, and control actions decoded by the first spike.
The algorithm demonstrated effective weight changes based on local learning rules.

Conclusions:

The proposed online reinforcement learning algorithm is effective for SNNs with memristive synapses.
This work represents a significant step towards real-time agent learning in continuous-time environments on neuromorphic systems.
The use of memristor-derived STDP-like rules facilitates efficient and localized learning.