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BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain (R01 Clinical Trial Not Allowed)
This NIH BRAIN Initiative opportunity supports the development of theories, computational models, and analytical methods for large-scale, complex brain data. Priority is given to novel capabilities for integrating and interpreting physiological, anatomical, connectivity, behavioral, and cell-type-specific data emerging from BRAIN Initiative research.
Four questions about brain and behaviour
Tinbergen encouraged ethologists to address animal behaviour by answering four questions, covering physiology, adaptation, phylogeny, and development. This broad approach has implications for neuroscience and psychology, yet, questions about phylogeny are rarely considered in these fields. Here I describe how phylogeny can shed light on our understanding of brain structure and function. Further, I show that we now have or are developing the data and analytical methods necessary to study the natural history of the human mind.
High-dimensional geometry of visual cortex
Interpreting high-dimensional datasets requires new computational and analytical methods. We developed such methods to extract and analyze neural activity from 20,000 neurons recorded simultaneously in awake, behaving mice. The neural activity was not low-dimensional as commonly thought, but instead was high-dimensional and obeyed a power-law scaling across its eigenvalues. We developed a theory that proposes that neural responses to external stimuli maximize information capacity while maintaining a smooth neural code. We then observed power-law eigenvalue scaling in many real-world datasets, and therefore developed a nonlinear manifold embedding algorithm called Rastermap that can capture such high-dimensional structure.