About

The functioning of the brain largely remains unsolved, partly because collecting in vivo neural data is slow and expensive, creating a bottleneck for brain experimentation and discovery. The emerging paradigm of in silico neuroscience addresses this limitation by leveraging encoding models of the brain, algorithms that predict neural responses to massive amounts of sensory stimuli in a fast and economical fashion. The scalability of in silico neural responses – which are used as stand-ins for in vivo neural responses during experimentation and data analysis – allows researchers to test more scientific hypotheses and to upscale exploratory research. Crucially, novel findings from large-scale in silico experimentation are eventually validated in vivo, but with targeted small-scale data collection, therefore optimizing research resources and allowing for faster neuroscientific discovery.

To catalyze this emerging research paradigm, we introduce the Brain Encoding Response Generator (BERG), a resource consisting of deep-learning-based encoding models of the brain and a Python package to easily generate accurate in silico neural responses to stimuli. BERG includes a growing, well documented library of encoding models spanning different neural recording modalities (e.g., fMRI, M/EEG, animal electrophysiology), species (e.g., human, macaque, mouse), datasets, subjects, brain areas, and sensory stimulation (e.g., visual, linguistic), thus enabling researchers to address a wide range of research questions through in silico neuroscience.

Beyond BERG's native models, BERG is also integrated with BrainScore, giving you access to hundreds of vision and language encoding models of the brain.

We envision that BERG will empower in silico neuroscience, ultimately accelerating scientific discovery. We warmly welcome models, ideas, and collaboration from the vision science community.