Stochastic Resonance in Neural Network, Noise Color Effects
Alexandra Pinto Castellanos

TL;DR
This paper investigates how different noise colors affect stochastic resonance in neural networks, revealing pink noise significantly enhances weak signal detection compared to white noise, highlighting noise's functional role in brain dynamics.
Contribution
It demonstrates that pink noise amplifies sub-threshold signals in neural networks more effectively than white noise, providing insights into noise's beneficial role in neural processing.
Findings
Pink noise amplifies weak signals twenty times more than white noise.
Noise color influences the efficiency of stochastic resonance in neural systems.
Background pink noise may play a functional role in brain signal processing.
Abstract
Some systems cannot be predicted by classical theories and it is required the development of combined deterministic and stochastic theories that make used of noise for dynamical prediction. Noise is not always an interfering signal which perturbs the system. On the contrary, noise signal can enhance the performance of some non-linear systems. The advantage of noise is observed in Stochastic Resonance (SR) where noise is used for amplification and subsequent detection of small signals. To detect this phenomena it is necessary that the system have a bistable potential barrier that creates a threshold, the input of the system should be a weak periodic signal which amplitude is below threshold together with an stochastic signal. In this way, the SR is detected when there are weak periodic signals that are added to different noise colors in order to be amplified and optimised. The…
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Taxonomy
TopicsNeural Networks and Applications · stochastic dynamics and bifurcation · Neural dynamics and brain function
