Stochastic resonance and sensory information processing: a tutorial and review of application.

By F. Moss, Lawrence M. Ward, W. Sannita. Clinical Neurophysiology, 2004

987 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f464671f5da61c99b16c93d05f8fb4606d0b845d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Michael A. Nitsche
2. Walter Paulus
3. C. J. Stagg
4. Felipe Fregni
5. David Liebetanz
6. Leonardo G. Cohen
7. Andrea Antal
8. Marom Bikson

In Molecular Biology

1. Derek Abbott
2. C. J. Stagg
3. Michael A. Nitsche

In Statistical and Nonlinear Physics

1. Derek Abbott
2. Christoph S. Herrmann
3. Mark D. McDonnell
4. Lawrence M. Ward

Citations in the corpus

• What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology (2009, background): “A recent summary of progress for sensory neurons was published in [35].”
• A technical guide to tDCS, and related non-invasive brain stimulation tools (2015, background): “Stochastic resonance (Moss et al., 2004) has also been suggested as a possible mechanism to mediate the effects of tRNS.”
• Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI (2024, background): “Unlike images that are of high signal-to-noise ratio, EEG signals are of low signal-to-noise ratio and have characteristics of apparent stochasticity, nonstationarity, and nonlinearity nature, which make it hard to reconstruct the original signals well (Moss et al., 2004).”
• Hair flow sensors: from bio-inspiration to bio-mimicking—a review (2012, background): “This phenomenon is called stochastic resonance (for a review, see Moss et al 2004).”
• The benefits of noise in neural systems: bridging theory and experiment (2011, background): “Theoretical work on stochastic resonance, whether classical or otherwise, has rarely diverged from the statistical physics discours”
• Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, background): “We did not allow secondary submissions from participants that might enhance the images for human perception, such as those based on noise dithering or inspired by stochastic resonance [48], [65].”
• Increasing Human Brain Excitability by Transcranial High-Frequency Random Noise Stimulation (2008, background): “The authors hypothesized that in PD patients the input noise ameliorated the impaired neuronal transmission, and the noise itself enhanced weak neuronal signal detection in the sensory system; the phenomenon of stochastic resonance, as shown in several experimental studies (e.g., Moss et al., 2004).”
• Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model (2010, neutral): “, 2008), perceptual performance (Moss et al., 2004) and computational models of the brain (Deco et al.”
• Sensory stimulation enhances visual working memory capacity (2024, unknown): “Thispositiveeffectofwhitenoise is explainedby the statistical phenomenon of stochastic resonance 185 , where random noise added to the neural system can aid signal detection and improve performance.”