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Poster Session B: Wednesday, August 13, 1:00 – 4:00 pm, de Brug & E‑Hall

Catalyzing in silico neuroscience with a toolkit of accurate encoding models of the brain

Alessandro Thomas Gifford¹, Domenic Bersch², Daniel Janini¹, Gemma Roig², Radoslaw Martin Cichy¹; ¹Freie Universität Berlin, ²Johann Wolfgang Goethe Universität Frankfurt am Main

Presenter: Domenic Bersch

_In silico_ neural responses generated from encoding models increasingly resemble _in vivo_ responses recorded from real brains, enabling the novel research paradigm of in silico neuroscience. In silico neuroscience scales beyond what is possible with in vivo data, allowing to explore and test scientific hypotheses across vastly larger solution spaces. To catalyze this emerging research paradigm, here we introduce the Brain Encoding Response Generator (BERG), a resource consisting of multiple pre-trained encoding models of the brain and a Python package to generate accurate in silico neural responses to massive amounts of arbitrary stimuli with a few lines of simple code (https://github.com/gifale95/BERG). We show that BERG’s encoding models accurately predict neural responses to visual stimuli, and that these in silico responses reproduce key neural signatures of visual processing. This opens the doors to using in silico neural responses for scientific discovery, which we envision will lead to a more efficient and reproducible science.

Topic Area: Methods & Computational Tools

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