Stochastic resonance in the sensory systems and its applications in neural prosthetics.

Noise is generally considered to be detrimental. In the right conditions, however, noise can improve signal detection or information transmission. This counterintuitive phenomenon is called stochastic resonance (SR). SR has generated significant interdisciplinary interest, particularly in physics, engineering, and medical and environmental sciences. In this review, we discuss a growing empirical literature that suggests that noise at the right intensity may improve the detection and processing of auditory, sensorimotor, and visual stimuli. We focus particularly on applications of SR in sensory biology and investigate whether SR-based technologies present a pathway to improve outcomes for individuals living with sensory impairments. We conclude that there is considerable evidence supporting the application of SR in developing sensory prosthetics. However, the progression of SR-based technologies is variable across the sensory modalities. We suggest opportunities for further advancements in each modality, considering the best approaches to maximise benefits and capitalise on progress already made. Overall, SR can offer opportunities to improve existing technologies or to motivate innovations.

Does stochastic resonance improve performance for individuals with higher autism-spectrum quotient?

While noise is generally believed to impair performance, the detection of weak stimuli can sometimes be enhanced by introducing optimum noise levels. This phenomenon is termed 'Stochastic Resonance' (SR). Past evidence suggests that autistic individuals exhibit higher neural noise than neurotypical individuals. It has been proposed that the enhanced performance in Autism Spectrum Disorder (ASD) on some tasks could be due to SR. Here we present a computational model, lab-based, and online visual identification experiments to find corroborating evidence for this hypothesis in individuals without a formal ASD diagnosis. Our modeling predicts that artificially increasing noise results in SR for individuals with low internal noise (e.g., neurotypical), however not for those with higher internal noise (e.g., autistic, or neurotypical individuals with higher autistic traits). It also predicts that at low stimulus noise, individuals with higher internal noise outperform those with lower internal noise. We tested these predictions using visual identification tasks among participants from the general population with autistic traits measured by the Autism-Spectrum Quotient (AQ). While all participants showed SR in the lab-based experiment, this did not support our model strongly. In the online experiment, significant SR was not found, however participants with higher AQ scores outperformed those with lower AQ scores at low stimulus noise levels, which is consistent with our modeling. In conclusion, our study is the first to investigate the link between SR and superior performance by those with ASD-related traits, and reports limited evidence to support the high neural noise/SR hypothesis.