Kristofer Bouchard
Lead, Computational Biosciences Group, Scientific Data Division
Computational Biologist Staff Scientist
Divisions
Scientific Data
Secondary Affiliation:
Biological Systems and Engineering
- BioEngineering & BioMedical Sciences
Research Interests
We are an interdisciplinary team that focuses on understanding how distributed neural circuits gives rise to coordinated behaviors and perception. We take a two-pronged approach to this problem by conducting in vivo neuroscience experiments and developing data science tools.
- On the neuroscience side, we investigate functional organization and dynamic coordination in brain by combining in vivo multi-scale electrophysiology and optogenetics in rodents. This multi-modal, multi-scale approach provides the simultaneous breadth of coverage and spatio-temporal resolution required to determine neural computations at the speed-of-thought.
- On the data science side, we develop analysis tools for (neuro)-science, including statistical-machine learning algorithms, inference in dynamic graphical models, and data standards/formats. These interpretable and predictive tools provide enhanced insight into the generative processes that produce data.
Recent Publications
Related News
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The Laboratory Directed Research and Development program at Berkeley Lab produces cutting-edge research for the DOE and the nation. Read about the Biosciences Area–led projects and multi-Area collaborations with Biosciences co-investigators receiving funding this cycle.
ML Opens New Doors in TBI Research
Applying machine learning capabilities developed at Berkeley Lab to an extensive dataset created by the TRACK-TBI collaboration led to a six-fold improvement in the precision with which TBI patient outcomes can be predicted.
A Common Data Format for Neurophysiology
A Berkeley Lab team developed a novel software architecture called Neurodata Without Borders (NWB) to serve as a standardized language for neurophysiology data and data-descriptors. The resulting data is findable, accessible, interoperable, and reusable (FAIR), and enables neuroscientists to effectively describe and communicate about their experiments and share data.
