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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms
A resource of encoding models for in silico neuroscience
Alessandro Thomas Gifford1, Domenic Bersch2, Daniel Janini1, Gemma Roig2, Radoslaw Martin Cichy1; 1Freie Universität Berlin, 2Johann Wolfgang Goethe Universität Frankfurt am Main
Presenter: Alessandro Thomas Gifford
_In silico_ neural responses to sensory stimuli generated by encoding models increasingly resemble _in vivo_ responses from real brains, enabling the novel research paradigm of in silico neuroscience. Within this paradigm, experimentation is carried out on massive amounts of in silico neural responses, empowering broader hypothesis testing and exploration than is possible from in vivo data, which is expensive and time consuming to collect. To catalyze in silico neuroscience, we introduce the Brain Encoding Response Generator (BERG), a resource of multiple pre-trained encoding models of the brain and a Python package to easily generate in silico neural responses to arbitrary stimuli (https://gifale95.github.io/BERG/). We show that BERG’s encoding models accurately predict neural responses to visual stimuli, and that these in silico responses capture key spatial and temporal organizing principles of visual processing in the brain. Together, this opens the doors to using in silico neural responses for brain research, which we envision will speed up neuroscientific discovery.
Topic Area: Methods, Tools, Theory & Neural Coding