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Hebbian learning

topic1 events
papersSEP 11 04:00 UTC

arXiv paper adds supervised signal to Hebbian plasticity in spiking networks

A new arXiv preprint proposes a method that injects supervision into spiking neural networks while keeping plasticity Hebbian and spike-driven. Rather than relying on backpropagation or reward-modulated STDP, the approach uses agreement between spikes to guide learning. The authors frame it as a way to retain biologically plausible local learning rules without giving up supervised training.

spiking-neural-networks/hebbian-learning/research/supervised-learning