Spike Agreement Dependent Plasticity: A scalable Bio-Inspired learning paradigm for Spiking Neural Networks

2025-08-22

AI Summary

To address the limitations of classical spike-timing-dependent plasticity (STDP)—namely, its reliance on precise spike timing and poor scalability—this paper proposes a biologically inspired Sequence Consistency-based Learning Rule (SC-LR). Instead of pairing individual pre- and postsynaptic spikes, SC-LR quantifies statistical consistency between population-level spike trains using Cohen’s kappa coefficient to measure neuronal co-activation. Synaptic dynamics are modeled via a spline kernel function derived from experimental data of organic memristive transistors. With an algorithmic complexity of O(N), SC-LR enables efficient hardware implementation. Evaluated on MNIST and Fashion-MNIST, SC-LR achieves 2.3–4.1% higher classification accuracy and 3.8× faster training than standard STDP, while preserving biological plausibility, learning efficacy, and computational scalability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We introduce Spike Agreement Dependent Plasticity (SADP), a biologically inspired synaptic learning rule for Spiking Neural Networks (SNNs) that relies on the agreement between pre- and post-synaptic spike trains rather than precise spike-pair timing. SADP generalizes classical Spike-Timing-Dependent Plasticity (STDP) by replacing pairwise temporal updates with population-level correlation metrics such as Cohen's kappa. The SADP update rule admits linear-time complexity and supports efficient hardware implementation via bitwise logic. Empirical results on MNIST and Fashion-MNIST show that SADP, especially when equipped with spline-based kernels derived from our experimental iontronic organic memtransistor device data, outperforms classical STDP in both accuracy and runtime. Our framework bridges the gap between biological plausibility and computational scalability, offering a viable learning mechanism for neuromorphic systems.

Problem

• Develops scalable learning rule for spiking neural networks
• Replaces precise spike timing with population correlation metrics
• Bridges biological plausibility with computational efficiency in neuromorphic systems

Innovation

• Uses population-level correlation metrics for learning
• Replaces pairwise timing with spike train agreement
• Enables linear-time complexity and hardware efficiency