Dan Goodman: Psst - neuromorphic folks. Did you know that you can solve the SHD dataset with 90% accuracy using only 22 kb of parameter memory by quantising weights and delays? Check out our preprint with @pengfei-sun.bsky.social and @danakarca.bsky.social, or read the TLDR below. 👇🤖🧠🧪 arxiv.org/abs/2510.27434

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Dan Goodman neural-reckoning.org
Psst - neuromorphic folks. Did you know that you can solve the SHD dataset with 90% accuracy using only 22 kb of parameter memory by quantising weights and delays? Check out our preprint with @pengfei-sun.bsky.social and @danakarca.bsky.social, or read the TLDR below. 👇🤖🧠🧪 arxiv.org/abs/2510.27434
Exploiting heterogeneous delays for efficient computation in low-bit neural networks

Neural networks rely on learning synaptic weights. However, this overlooks other neural parameters that can also be learned and may be utilized by the brain. One such parameter is the delay: the brain...

arxiv.org
Nov 13, 2025 17:40
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Fleur Zeldenrust fleurzeldenrust.bsky.social · Nov 14, 2025
Very cool! Made me think of this work: www.mdpi.com/2313-7673/9/... it helps if you can put the delays at the right values for the task requirements.
https://www.mdpi.com/2313-7673/9/6/355

mdpi.com

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