AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key research question is which architectural choices best accommodate local and online learning in multi-layer convolutional SNNs. This question is addressed via an original fr...
This work introduces a minimal predictive coding framework for SNNs with two layer variants sharing a predictor block: error units, which transmit signed spiking residuals, and predictive suppression, which uses residual magnitude to dynamically gate and forward only unpredictable,"surprising"activity.
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