Feedforward spiking neural networks are not transformers (yet): a learning-theoretic framework for long-range dependencies and biological efficiency

Spiking neural networks offer a promising route toward low-power sequence computation on neuromorphic hardware, but they continue to lag behind attention-based artificial neural networks on long-context tasks. A central open question is whether this gap reflects only implementation and optimization...

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Autors principals:	William Fishell, Gord Fishell, Suraj Honnuraiah
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2026-01-01
Col·lecció:	Neuromorphic Computing and Engineering
Matèries:	spiking neural networks sample complexity covering numbers neuromorphic computing theoretical neuroscience
Accés en línia:	https://doi.org/10.1088/2634-4386/ae8626
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