Brain: Biological noise-based logic
Laszlo B. Kish, Claes-Goran Granqvist, Sergey M. Bezrukov, Tamas, Horvath

TL;DR
This paper explores how the brain may utilize noise-based logic, leveraging the stochastic nature of neural spikes to efficiently process information despite apparent randomness.
Contribution
It proposes a noise-based logic model for brain function, explaining how stochastic neural spikes can be harnessed for efficient information processing.
Findings
Neural spikes exhibit stochastic sequences akin to pulse noise.
Noise-based logic can explain rapid information extraction in the brain.
The brain may use randomness to enhance processing speed.
Abstract
Neural spikes in the brain form stochastic sequences, i.e., belong to the class of pulse noises. This stochasticity is a counterintuitive feature because extracting information - such as the commonly supposed neural information of mean spike frequency - requires long times for reasonably low error probability. The mystery could be solved by noise-based logic, wherein randomness has an important function and allows large speed enhancements for special-purpose tasks, and the same mechanism is at work for the brain logic version of this concept.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
