Local Synaptic Rules Can Implement a SIGReg Gradient Without Backpropagation
We prove that two canonical local synaptic learning rules, the potentiation arm of spike-timing-dependent plasticity (STDP$^+$) and homeostatic plasticity (instantiated here via flashlight granule-cell-like neurons), together can implement the exact gradient of a SIGReg-like self-supervised learning objective. The equivalence requires no gradient calculations, no global error signals, no weight transport, and no label information: the only inputs are pre- and post-synaptic firing rates, local firing statistics, and the temporal contiguity of natural sensory streams. On a synthetic clustering task designed to probe whether class structure can be recovered from temporal ordering of inputs a...
| Authors | Martin Andrews |
|---|---|
| Primary category | cs.NE |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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