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Stochastic Resonance

Learn about Stochastic Resonance, where adding just the right amount of noise enhances signal detection, with examples from nature and neuroscience. Understand the causes, nonlinearities, and mathematical definition of SR. Compare optimal detectors and explore noise impacts.

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Stochastic Resonance

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  1. Stochastic Resonance • Adding Noise to a signal can help in its detection. • Just the right amount of noise must be added (resonance) • SR in Nature • Periodic ice age prediction • Crayfish warnings of approaching bass - a periodic fin motion • Dogfish spit noise to better detection. • Neurons

  2. Stochastic Resonance • What Causes SR? • Non-Additive Noise • Nonlinearities • Mathematical Definition • There is none

  3. Example: Miyamoto Resonance Add noise to image and threshold. Vary noise. 0 15 30 60 90 120 180 240

  4. dx/dt x 1/s ax-bx3 + +  sin(t) (t) Most Studied System

  5. Numerics (Mataim et al. 1998)

  6. Numerics (Mataim et al. 1998)

  7. Numerics (Mataim et al. 1998)

  8. Comparison of NP Optimal Detector • Coherent NP Correlator vs. Stochastic Resonance • Assumptions • Compared NP on input and after SR nonlinearity • Detector COHERENT • Noise = White Gaussian Galdi, Pierro, Pinto (Phys Review E, June 1998).

  9. Comparison of NP Optimal Detectors • Coherent NP Correlator vs. Stochastic Resonance (4 dB) Galdi, Pierro, Pinto (Phys Review E, June 1998).

  10. Noncoherent • Noncoherent SR sign detector Galdi, Pierro, Pinto (Phys Review E, June 1998).

  11. Noncoherent Results • Noncoherent correlator is about 3dB worse than coherent. • Noncoherent SR is better than noncoherent correlator for low SNR Galdi, Pierro, Pinto (Phys Review E, June 1998).

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