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• U19 – brief description overall project goals, competing circuit theories being developed/studied/integrated:

Animals, including humans, interact with their environment via self-generated and continuous actions that enable them to explore and subsequently experience the positive and negative consequences of their actions. As a result of their interactions with the environment, animals alter their future behavior, typically in a manner that maximizes positive and minimizes negative outcomes. Furthermore, how an animal interacts with its environment and the actions that it chooses depend on its current environment, its past experience in that environment, as well as its internal state. Thus, the actions taken by an animal are dynamic and evolving, as necessary for behavioral adaptation. It is thought that both the execution of actions, in particular goal-oriented actions, and the modification of future behavior in response to the outcome of actions, depend on evolutionarily old parts of the brain called the basal ganglia. Within the basal ganglia, cells that produce dopamine have a profound influence on behavior, including human behavior, and their activity appears to encode for features of the environment and animal experience that are important for directing goal-oriented behavior.

Here we bring together a team of experimental and computational neurobiologists to understand how these dopamine- producing cells modulate behavior and basal ganglia circuitry. We will use unifying theories and models to integrate information acquired over many classes of behavior. Completing the proposed work, including the technical advances and biological discoveries, will provide a platform for future analyses of related circuitry and behaviors in many species, including humans.

• Description of how Data Science Core is complying to the criteria of the FAIR principles:

The Data Science Core has dedicated the last year to moving our data to storage via Neurodata Without Borders (NWB). The Core has worked with each lab within the U19 to document their relevant metadata and design NWB schemas for storing both metadata and relevant experimental data. We plan to spend the next year developing more robust scripts for automatically converting labs' data into our NWB schemas. 

• List of Data Types in U19:

Neural Data: Fluorescence Photometry, 2P Calcium Imaging, Extracellular Electrophysiology 

Behavioral Data: MoSeq, 2ABT, new state space models and matrix factorizations 

• Common Data Elements in U19:

Every group stores the above neural and behavioral data in different combinations of custom files and filesystems. The key work of this Data Core has been to standardize and organize each labs' data for better sharing. 

• Data Sharing goals in U19:

Beyond standardization, we've been integrating NWB and Datajoint into our computational packages and pipelines to ensure data integrity and shareability throughout our analyses. 

• Data science tools being used in project:

Neurodata Without Borders, Datajoint, Pytorch, Jax

• Data science tools being developed for project:

1. MoSeq (http://datta.hms.harvard.edu/research/behavioral-analysis/)

2. SSM (https://github.com/lindermanlab/ssm)

3. ndx-complex-bhavior (https://github.com/ndx-complex-behavior)

• Data science approaches to be shared with other U19’s:

See above!

• Data science challenges that could benefit from discussion with other U19’s:

Link to Data/Model Reuse abstract, [LINK] 

2021 Brain PI Meeting

Update:

Link to Poster: https://docs.google.com/presentation/d/1i5oFXOiSqYQ1Jas3SFTCE_JW5e7-FxJiRLV6b-5q9UE/edit?usp=sharing

Demo:

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