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Neural Remapping Produces Visual Stability: A Computational Model

Student: Bocharova Daria

Supervisor: W.Joseph MacInnes

Faculty: Institute for Cognitive Neuroscience

Educational Programme: Cognitive Sciences and Technologies: From Neuron to Cognition (Master)

Year of Graduation: 2019

Each second we perform around three saccades, but still percept the world as stable. This work is dedicated to one of the most interesting problems of visual stability: saccadic suppression of displacement (SSD). The research suggests, that humans fail to detect target movements, if they occurred during a saccade performance. We discussed the roles of corollary discharge and remapping in visual stability and in SSD. We used machine learning techniques to model human answers to the SSD task. A previously taken dataset was used as a computational model input. We took the efference copy and retinal signal inputs from it. We obtained information from each initial (first) saccade of each trial and then we took intended saccades as the input. We trained the models on them. All ten models showed very low classification accuracy. However, the Mann-Whitney U test revealed that these models performed significantly about chance, though only by several percent.

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