This application proposes to use theory-driven experimental design, with advanced techniques for neural recording, data analysis, and computational modeling, to investigate the neural mechanisms, circuits, and representations underlying the perceptual process of causal inference in space and time. The multidisciplinary nature of the proposed work requires close collaboration among consortium members. The Data Science Core will facilitate this collaboration and provide the tools necessary to handle and analyze the large-scale neural data collected in the proposed experiments. To achieve these goals, the Data Science Core will rely on existing infrastructure, open standards, and open-source software as much as possible.
Aim 1 will establish a unified data standard, and data exchange and storage infrastructure, using the architecture established by the International Brain Laboratory, which stores metadata in a relational, searchable database, and experimental and processed data on a separate file server. Github will enable joint development, exchange, and documentation of the code underlying data preprocessing, processing, and analysis. To relate data to models, voltages recorded experimentally must be transformed into standardized spike times and counts, without artifacts or confounds.
Aim 2 will develop a principled, transparent, and reproducible pipeline for this preprocessing and apply it to all neurophysiological data generated in Projects B and C. The first stage will eliminate electrical and behavioral artifacts and convert voltages into spike times and local field potentials. The second stage will use a statistical model of neural activity to identify and label potential outliers. This pipeline will produce annotated and cleaned data in a standardized format that can be used to perform reliable analyses, model fitting, and hypothesis tests.
Aim 3 will combine cutting-edge methods and convert them to software tools that can be reliably applied to new data. Most of this effort will be applied to variants of latent- state discovery techniques that jointly fit the influence of stimuli, model-driven hypothesized latent states, and unobserved latent states such as slow fluctuations. The central work of this aim is to implement those tools, help the team apply them to the data generated by the collaboration, and refine them for public use.
Aim 4 is to share the experimental data with the wider research community by uploading the relevant portions of the data to public and freely accessible repositories. Code, documentation, and use cases will be made public on Github. The use of standard data structures, open standards, and open-source software will ensure barrier-free access, ease of use, and reproducibility for neuroscience researchers. With the help of a full-time data scientist hired to manage these efforts, the Data Science Core will build on established data storage and analysis standards and methods to produce cleaned and standardized data that our consortium can use to close the loop between theory and experiments. By sharing code, use cases, and data with other researchers, this project will also improve and extend these resources for future use by others.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Program--Cooperative Agreements (U19)
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Special Emphasis Panel (ZNS1)
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University of Rochester
United States
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