About

The functioning of the brain in health and disease is still largely a mystery, partly because the process of collecting in vivo neural data is slow and expensive thus providing a bottleneck for brain experimentation and discovery. In silico neuroscience is an emerging research paradigm that addresses this limitation through experimentation on large amounts of in silico neural responses generated by encoding models in a fast and economical fashion. Compared to in vivo experiments on real brains, the unprecedented amount of in-silico-generated neural responses 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 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.

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.