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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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Published on: June 24, 2015

Timing-Dependent Spiking Neural Network: Board-Level Hardware Implementation with Photoelectroactive Van der Waals

Seongjun Kim¹, Jeong-Ick Cho², Sungsoo Lee²

¹Division of Electrical Engineering, Hanyang University ERICA, Ansan, 15588, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)

|December 8, 2025

Summary

This summary is machine-generated.

This study demonstrates the first multi-channel timing-dependent spiking neural network (TD-SNN) using novel photoelectroactive synaptic devices. This hardware achieves high accuracy in pattern classification, paving the way for efficient, real-time learning systems.

Keywords:

2D materials multi‐channel timing‐dependent spiking neural networks neuromorphic devices photoelectroactive artificial synapses van der Waals heterojunctions

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Area of Science:

Neuromorphic Engineering
Artificial Intelligence Hardware
Materials Science

Background:

The increasing volume of unstructured data necessitates energy-efficient, real-time computing solutions.
Biological brains offer a model for efficient computation through spike-timing-dependent plasticity (STDP).

Purpose of the Study:

To experimentally realize a multi-channel timing-dependent spiking neural network (TD-SNN) at the board level.
To emulate STDP using novel photoelectroactive synaptic devices integrated with neuron circuits.
To demonstrate adaptive and real-time learning capabilities in neuromorphic hardware.

Main Methods:

Integration of photoelectroactive synaptic devices with analog leaky integrate-and-fire (LIF) neuron circuits.
Emulation of STDP by exploiting timing between electrical presynaptic and optical postsynaptic spikes.
Engineering presynaptic pulse shapes to achieve diverse STDP learning rules (Hebbian, anti-Hebbian, all-LTP, all-LTD).

Main Results:

Demonstrated reversible and bidirectional modulation of synaptic weights via photoelectroactive doping.
Achieved self-learning, system-level adaptation, and competitive behaviors in integrated networks.
Attained up to 90.9% accuracy on MNIST tasks with STDP parameters, showing robust pattern classification when the LTP/LTD area ratio (PDR) ≥ 1.25.

Conclusions:

This work presents the first experimental realization of a multi-channel TD-SNN at the board level.
The developed photoelectroactive synaptic devices enable diverse STDP learning rules and adaptive network behaviors.
The findings represent a significant advancement in timing-dependent neuromorphic hardware, demonstrating feasibility for adaptive, real-time learning systems.