Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks

• URL: http://arxiv.org/abs/2601.08526v1
• Date: Tue, 13 Jan 2026 13:09:34 GMT
• Title: Supervised Spike Agreement Dependent Plasticity for Fast Local Learning in Spiking Neural Networks
• Authors: Gouri Lakshmi S, Athira Chandrasekharan, Harshit Kumar, Muhammed Sahad E, Bikas C Das, Saptarshi Bej,
• Abstract summary: We introduce a supervised extension of Spike Agreement-Dependent Plasticity (SADP)<n>SADP replaces pairwise spike-timing comparisons with population-level agreement metrics such as Cohen's kappa.<n>Experiments on MNIST, Fashion-MNIST, CIFAR-10, and biomedical image classification tasks demonstrate competitive performance and fast convergence.
• Score: 6.376927936764407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spike-Timing-Dependent Plasticity (STDP) provides a biologically grounded learning rule for spiking neural networks (SNNs), but its reliance on precise spike timing and pairwise updates limits fast learning of weights. We introduce a supervised extension of Spike Agreement-Dependent Plasticity (SADP), which replaces pairwise spike-timing comparisons with population-level agreement metrics such as Cohen's kappa. The proposed learning rule preserves strict synaptic locality, admits linear-time complexity, and enables efficient supervised learning without backpropagation, surrogate gradients, or teacher forcing. We integrate supervised SADP within hybrid CNN-SNN architectures, where convolutional encoders provide compact feature representations that are converted into Poisson spike trains for agreement-driven learning in the SNN. Extensive experiments on MNIST, Fashion-MNIST, CIFAR-10, and biomedical image classification tasks demonstrate competitive performance and fast convergence. Additional analyses show stable performance across broad hyperparameter ranges and compatibility with device-inspired synaptic update dynamics. Together, these results establish supervised SADP as a scalable, biologically grounded, and hardware-aligned learning paradigm for spiking neural networks.

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