Open AccessImprovement of signal-to-noise ratio in parallel neuron arrays with spatially nearest neighbor correlated noise
Author(s) -
Tianquan Feng,
Qingrong Chen,
Ming Yi,
Xiao Zhang
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0200890
Subject(s) - stochastic resonance , physics , noise (video) , amplitude , signal to noise ratio (imaging) , k nearest neighbors algorithm , signal (programming language) , statistical physics , nonlinear system , transmission (telecommunications) , acoustics , computer science , telecommunications , quantum mechanics , optics , artificial intelligence , image (mathematics) , programming language
We theoretically investigate the signal-to-noise ratio (SNR) of a parallel array of leaky integrate-and-fire (LIF) neurons that receives a weak periodic signal and uses spatially nearest neighbor correlated noise. By using linear response theory, we derive the analytic expression of the SNR. The results show that the amplitude of internal noise can be increased up to an optimal value, which corresponds to a maximum SNR. Given the existence of spatially nearest neighbor correlated noise in the neural ensemble, the SNR gain of the collective ensemble response can exceed unity, especially for a negative correlation. This nonlinear collective phenomenon of SNR gain amplification may be related to the array stochastic resonance. In addition, we show that the SNR can be improved by varying the number of neurons, frequency, and amplitude of the weak periodic signal. We expect that this investigation will be useful for both controlling the collective response of neurons and enhancing weak signal transmission.