Multi-Scale Multi-Area Interaction in Cortical Networks

The use case Multi-Scale Multi-Area Interaction in Cortical Networks employs parallelized data mining strategies paired with statistical Monte-Carlo approaches to evaluate signatures of correlated activity hidden in the high-dimensional ensemble dynamics recorded simultaneously from visual and motor brain areas in order to link neuronal interactions to behavior. There are two challenges to be tackled by this use case. Multi-dimensional correlation analysis methods of activity due to the combinatorial complexity, strong undersampling of the system, and non-stationarities that prohibit the use of analytic statistical tests lead to increased computational demands. In addition, the heterogeneity and complex structure of the various data streams, including rich metadata, require suitable informatics tools and protocols for the acquisition of metadata and provenance tracking of the analysis workflows.

This use case is contributed by the Institute of Neuroscience and Medicine - Computational and Systems Neuroscience (INM-6).

Detecting spatio-temporal spike patterns in massively parallel activity data from recording arrays implanted in motor cortical brain areas. Patterns were specific to behavioral parameters of the motor task, and exhibited a preferred spatial orientation on the cortical Networks.
Copyright: Torre, E., Quaglio, P., Denker, M., Brochier, T., Riehle, A., and Grun, S. (2016). Synchronous spike patterns in macaque motor cortex during an instructed delay reach-to-grasp task. Journal of Neuroscience 36, 8329–8340