Jason M Samonds

Visual perception is an interactive process involving prediction, identification, classification, and decision or reaction. My interest is in understanding how the visual cortex takes local information and forms a global percept. Specifically, how does the cortex integrate distributed signals in order to identify correlations and patterns among those local cues? The problem is compounded by the fact that patterns and objects are typically embedded within an environment that also contains structure and correlation. We use knowledge from natural scene statistics, psychophysical data, and computer vision algorithms to formulate hypotheses, and we employ rigorous statistical analyses on simultaneous recordings and imaging from multiple neurons distributed across the cortical network. Our results provide us with clues on how the brain is able to segment and identify objects, as well as reveal properties of the underlying cortical mechanisms. 

Natural image and
receptive field statistics
predict saccade sizes.
Samonds JM, Geisler WS, Priebe NJ.
Nature Neurosci 21:1591–1599, 2018.
Motion discrimination and the motion aftereffect in mouse vision.
Samonds JM, Lieberman S, Priebe NJ.
eNeuro 5(6) e0065-18.2018 1–12, 2018.

FEATURED ARTICLES

Mice discriminate
stereoscopic surfaces
without fixating in depth.
Samonds JM, Choi V, Priebe NJ.
J Neurosci 39(41):8024-8037, 2019.

Cortical Inference

© 2018 by Jason M Samonds. Proudly created with Wix.com

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