SOURCE-LINKED INTELLIGENCE
Exploring napping paradigm for Recurrent Spiking Neural Networks
Biological organisms minimize free energy by balancing two competing demands on their internal world model: it must be accurate enough to predict sensory input, yet simple enough to generalize beyond it. Two mechanisms regulate this balance offline: sleep reduces complexity through gradual synaptic downscaling, while stochastic noise attenuates precision, relaxing the constraint sensory input imposes on synaptic reorganization. Engineered Spiking Neural Networks (SNNs) leave this balance unaddressed, favoring instantaneous, noiseless weight normalization instead. This paper investigates the hy
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T13:18:57.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.