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Denoising

3 results

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Predictive dynamics improve noise robustness in a deep network model of the human auditory system

Ching Fang, Erica Shook, Justin Buck, Guillermo Horga · COSYNE 2023

The human auditory system is robust to many types of corrupting noise. However, the neural mechanism that drives this robustness is unclear. Experimental evidence suggests that top-down predictions play an important role in processing stimuli in noisy contexts. Feedback connections are abundant in the auditory cortex, and have been hypothesized to carry predictive signals but are often omitted in large scale models. We evaluate the potential role of predictive dynamics in auditory robustness by introducing these dynamics into a large-scale model of the human auditory system. Specifically, we a

ePoster · Computational Neuroscience

Representation transfer and signal denoising through topographic modularity

Barna Zajzon · Morrison lab, Forschungszentrum Jülich, Germany

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the featur

Thu, Nov 4, 2021 · 16:30 UTC

Seminar · Computational Neuroscience