About

In silico neural responses to stimuli generated by encoding models increasingly resemble in vivo responses recorded from real brains, enabling the novel research paradigm of in silico neuroscience. The fast and economical generation of in silico neural responses allows researchers to test more scientific hypotheses and to explore across larger solution spaces than possible in vivo. Crucially, novel findings from large-scale in silico experimentation are then validated through targeted small-scale in vivo data collection, thereby optimizing research resources.

To empower this emerging research paradigm, we introduce the Brain Encoding Response Generator (BERG), a resource consisting of diverse pre-trained encoding models of the brain and a Python package to easily generate in silico neural responses to arbitrary stimuli with just a few lines of code. BERG enables researchers to efficiently address a wide range of research questions through in silico neuroscience by providing a growing, well documented library of encoding models trained on different neural recording modalities, species, datasets, subjects, and brain areas.

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.