Neuroscience datasets

The goal of behavioral neuroscience is to understand the relationship between neural activity and behavior. One traditional approach for studying this relationship has been to build models that either translate from the neural activity to behavior (decoding) or from behavior to neural activity (encoding). Recent advances in self-supervised learning now allow for modeling both modalities jointly. In this work, we introduce a multimodal masked modeling approach that masks portions of both behavior and neural activity, using the unmasked data to make predictions across both modalities. We evaluat

Andreas Tolias discusses foundation models trained on large neuroscience datasets and their potential to generalize across experimental settings.

The shift towards open science enables the exploration of an increasing variety of neuroscience datasets. The diversity of data formats, ways to organize and describe data, however, pose a significant challenge for reproducing and reusing scientific outputs. To address this, we developed an efficient data curation workflow for sharing FAIR and integrated neuroscience data. This workflow is well documented and continuously refined to meet researchers' needs.The aim of the data curation process is to build the best possible metadata representation of the data for discoverability and reuse. Accor