DRUCKMANN, SHAUL Dissecting distributed representations by advanced population activity analysis methods and modeling EB028171
The tool we are developing aims to distill simultaneous recordings from neural populations (e.g., from two brain areas) into a spatio-temporal profile of strength of influence. This is the first year of the TMM grant and accordingly we are very much in the development phase. Our approach defines influence by the ability to predict unexpected deviations in the dynamics of one brain area, the modeled area, from the just-preceding activity in another brain area, the influencing area. In more detail, we first predict the dynamics of the modeled area from its own past history. We then detect deviations from the predicted dynamics and determine whether these deviations can be themselves reliably predicted from the state of the influencing area. There are numerous variants of this general description, such as using linear vs. non-linear predictive models, or modeling the full activity space our inferred subsets. We will complement it with non-dynamical approaches that optimize over both populations to find components of maximal correlation such as canonical correlation analysis Our current use involves electrophysiology data, though we would like to extend our approach in the future to calcium recordings. In terms of data requirements: signal-to-noise ratios vary a lot between tasks and brain areas making it hard to define a specific minimal number of neurons but this method is meant for population recordings, e.g., more than ten neurons per population to be modeled. As it is a sub-single trial method, repetitions are required, e.g., 50 repetitions of a task condition, or extended recordings in a non task-based structure.
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