Learning spatio-temporal visual features by a large scale neural network model

iments. Thus, if data can be made readily available, it can have a profound effect on advancing our understanding of the brain, since more researchers will be able to build and test algorithms with empirical data. Towards this end, we created a web portal and search engine to facilitate data sharing. Our system allows users to search for, upload, and download data. To advance BMI research by combining behavioral and neurophysiological data, we allow users to upload files and videos describing their experiments, and we use a uniform file format for neurophysiological data, which allows us to develop software that can be used to process and extract important features. We are currently working on a time-alignment tool to combine behavioral information with neurophysiological data, which is a feature that other neuroscience databases do not have. Additionally, we have a web-based data previewer that allows users to preview data before they download it. Taken together, these features are made to connect data with the people that need it. Through licensing the data with a Creative Commons license, we allow users who upload data to specify required citations of their work, while also allowing people who download data to freely use it. In future work we will provide a data-driven search tool that will allow users to search for data by inputting some of their own data.