ChronoPlastic Spiking Neural Networks

• URL: http://arxiv.org/abs/2601.00805v1
• Date: Wed, 17 Dec 2025 06:58:04 GMT
• Title: ChronoPlastic Spiking Neural Networks
• Authors: Sarim Chaudhry,
• Abstract summary: Spiking neural networks (SNNs) offer a biologically grounded and energy-efficient alternative to conventional neural architectures.<n>CPSNNs embed temporal control directly within local synaptic dynamics.<n>CPSNNs learn long-gap temporal dependencies significantly faster and more reliably than standard SNN baselines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) offer a biologically grounded and energy-efficient alternative to conventional neural architectures; however, they struggle with long-range temporal dependencies due to fixed synaptic and membrane time constants. This paper introduces ChronoPlastic Spiking Neural Networks (CPSNNs), a novel architectural principle that enables adaptive temporal credit assignment by dynamically modulating synaptic decay rates conditioned on the state of the network. CPSNNs maintain multiple internal temporal traces and learn a continuous time-warping function that selectively preserves task-relevant information while rapidly forgetting noise. Unlike prior approaches based on adaptive membrane constants, attention mechanisms, or external memory, CPSNNs embed temporal control directly within local synaptic dynamics, preserving linear-time complexity and neuromorphic compatibility. We provide a formal description of the model, analyze its computational properties, and demonstrate empirically that CPSNNs learn long-gap temporal dependencies significantly faster and more reliably than standard SNN baselines. Our results suggest that adaptive temporal modulation is a key missing ingredient for scalable temporal learning in spiking systems.

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