Stochastic resonance and optimal information transfer at criticality on a network model of the human connectome

Abstract

Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of SR. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.

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Cited by in corpus (8)

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• Critical excitation-inhibition balance in dense neural networks
• Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform
• Optimal navigability of weighted human brain connectomes in physical space
• Control of Functional Connectivity in Cerebral Cortex by Basal Ganglia Mediated Synchronization