{"ID":2831510,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07194","arxiv_id":"2512.07194","title":"Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks","abstract":"While surrogate backpropagation proves useful for training deep spiking neural networks (SNNs), incorporating biologically inspired local signals on a large scale remains challenging. This difficulty stems primarily from the high memory demands of maintaining accurate spike-timing logs and the potential for purely local plasticity adjustments to clash with the supervised learning goal. To effectively leverage local signals derived from spiking neuron dynamics, we introduce Dopamine-Modulated Spike-Synchrony-Dependent Plasticity (DA-SSDP), a synchrony-based rule that is sensitive to loss and brings a synchrony-based local learning signal to the model. DA-SSDP condenses spike patterns into a synchrony metric at the batch level. An initial brief warm-up phase assesses its relationship to the task loss and sets a fixed gate that subsequently adjusts the local update's magnitude. In cases where synchrony proves unrelated to the task, the gate settles at one, simplifying DA-SSDP to a basic two-factor synchrony mechanism that delivers minor weight adjustments driven by concurrent spike firing and a Gaussian latency function. These small weight updates are only added to the network`s deeper layers following the backpropagation phase, and our tests showed this simplified version did not degrade performance and sometimes gave a small accuracy boost, serving as a regularizer during training. The rule stores only binary spike indicators and first-spike latencies with a Gaussian kernel. Without altering the model structure or optimization routine, evaluations on benchmarks like CIFAR-10 (+0.42\\%), CIFAR-100 (+0.99\\%), CIFAR10-DVS (+0.1\\%), and ImageNet-1K (+0.73\\%) demonstrated consistent accuracy gains, accompanied by a minor increase in computational overhead. Our code is available at https://github.com/NeuroSyd/DA-SSDP.","short_abstract":"While surrogate backpropagation proves useful for training deep spiking neural networks (SNNs), incorporating biologically inspired local signals on a large scale remains challenging. This difficulty stems primarily from the high memory demands of maintaining accurate spike-timing logs and the potential for purely loca...","url_abs":"https://arxiv.org/abs/2512.07194","url_pdf":"https://arxiv.org/pdf/2512.07194v1","authors":"[\"Yuchen Tian\",\"Samuel Tensingh\",\"Jason Eshraghian\",\"Nhan Duy Truong\",\"Omid Kavehei\"]","published":"2025-12-08T06:10:44Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false,"code_links":[{"ID":606129,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2831510,"paper_url":"https://arxiv.org/abs/2512.07194","paper_title":"Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks","repo_url":"https://github.com/NeuroSyd/DA-SSDP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}