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Related Experiment Video

Updated: Mar 16, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Fast agreement-driven device-calibrated local learning paradigms for spiking neural networks.

Saptarshi Bej¹, Muhammed Sahad E², Gouri Lakshmi S¹

¹School of Data Science, Indian Institute of Science Education and Research, Thiruvananthapuram, India.

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

|March 14, 2026

Summary

New synaptic learning rules, Spike Agreement Dependent Plasticity (SADP) and Spike Correlation Dependent Plasticity (SCDP), enable Spiking Neural Networks (SNNs) to learn faster. These biologically inspired rules offer efficient hardware implementation for next-generation neuromorphic systems.

Keywords:

Memtransistor-based learning Spiking neural networks Synaptic plasticity

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Area of Science:

Computational Neuroscience
Machine Learning
Artificial Intelligence

Background:

Classical Spike-Timing-Dependent Plasticity (STDP) relies on precise spike timing, limiting computational efficiency.
Spiking Neural Networks (SNNs) offer potential for energy-efficient AI but require effective learning rules.
Biologically inspired learning rules are crucial for advancing neuromorphic computing.

Purpose of the Study:

Introduce novel synaptic learning rules: Spike Agreement Dependent Plasticity (SADP) and Spike Correlation Dependent Plasticity (SCDP).
Evaluate the efficiency and effectiveness of SADP and SCDP compared to classical STDP.
Explore the potential of agreement-driven plasticity for neuromorphic systems.

Main Methods:

Developed SADP and SCDP, focusing on spike train agreement and correlation, not precise timing.
Implemented SADP and SCDP with linear-time complexity for efficient hardware realization.
Validated SADP and SCDP on MNIST and Fashion-MNIST datasets using single-layer networks.

Main Results:

SADP and SCDP demonstrated significantly faster feature learning than STDP.
Achieved strong downstream classification accuracy with an order of magnitude less training time.
Showcased efficient hardware implementation capabilities via event-driven, bitwise logic.

Conclusions:

Agreement-driven plasticity provides a fast, local, and biologically plausible learning framework.
SADP and SCDP are viable unsupervised learning mechanisms for neuromorphic systems.
The framework offers potential extensions to supervised learning paradigms for enhanced AI capabilities.