Our main goal is to unravel communication dynamics in the brain, as they relate to various sensory-motor actions and to the learning process. The sensory-motor system operates through the concerted interaction of multiple closed-loops feedback systems. While some broad level knowledge is available about single neuron properties and general high-level operations, we lack understanding of functional aspects of neural dynamics, of inter-neuronal interactions and of the modular interaction and integration of brain regions contributing to motor activity. Two photon calcium imaging has revolutionized experimental capabilities to measure large-scale neuronal activity, but poses a significant challenge in terms of massive dynamical data analysis. We intend to confront these challenges face-on, to significantly boost the quality and relevance of experimental data collected during the process of animal learning and execution of motor functions. Our goal is to build an end-to-end modular platform to organize automatically (in a data agnostic way) the dynamical observation space into dynamic scenarios corresponding to contextual groups of neuronal dynamics and to specific motor activity in different related trials. Our plan follows a path from low-level processing of raw calcium imaging data through mid-level organization of extracted neuronal time-traces and finally to high-level inference and prediction of behavior.
We aim to extend prior geometric dynamics analysis methods for nonlinear empirical modeling to the complexity of the large-scale neuronal data. Our methodology leads to the determination of low-dimensional intrinsic dynamical sub-processes that provides a coherent explanation of the observed data, and to testable experimental predictions. Unlike conventional neuronal data processing postulating a-priori specific structural models, we rely only on general data-agnostic coherence assumptions. These settings remove bias due to a- priori modeling and enable developing tools that are independent of the acquisition modality, simplifying data fusion (such as neuronal and behavioral observations). Our initial experimental setup is two photon calcium imaging measurements of a head-fixed mouse, performing a motor reach task in multiple repetitive trials. The brain imaging data is synchronized with acquired high- resolution behavioral video. As we show in this proposal, we already have preliminary results demonstrating the power of our empirical analytics methods.
In this project, we introduce a methodology for unravelling the complexity of neuronal activity in the brain of a mouse, while performing various tasks. Our main goal is to develop and provide an end-to-end system of modular mathematical tools to automatically analyze, co-organize and model massive amounts of high- resolution spatiotemporal neuronal activity with motor functions. Our methodology reveals various multiscale dynamical processes, relating measured neuronal activity to functional behavior, and has already uncovered activity patterns, dysfunction, and recovery stages.
