Data-driven analysis for neuronal dynamic modeling

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PI: Mishne, Gal


Institution: UC San Diego

Title: Data-driven analysis for neuronal dynamic modeling

Grant #: EB026936 

Abstract: Our main goal is to unravel dynamics in the brain, as they relate to various sensory-motor actions and to the learning process. 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. 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).
To accomplish these aims we are developing a framework to analyze time-series from neuro-imaging data acquired over multiple weeks as an animal learns a task. This framework will address imaging challenges (extracting neuronal structures from videos, alignment of videos across days, handling multiple acquisition modalities) as well as unsupervised analysis of the activity of hundreds of neurons jointly with the observed motor behavior, across multiple time-scales: from immediate execution of behavior through a single trial to multiple days of learning. Our end-to-end modeling and analysis framework for multi-trial neuronal activity across multiple modalities and spatiotemporal scales includes: 1) low-level processing of raw calcium imaging data , 2) mid-level organization of extracted interconnected neuronal time-traces, 3) high-level analysis of evolving network of neurons and behavior over long term learning.



Link to Data/Model Reuse abstract, [Link]


Neural Manifolds

Functional imaging analysis

Behavior Analysis

Multiway analysis

Analyzing multiway tensor data with manifold embeddings and multiway clustering



2023 Brain PI Meeting Posters:

2021 Brain PI Meeting Update: 

* Calcium imaging analysis: 

* Multiway analysis of tensor data. 

* Low distortion embedding of manifold data: LDLE: Low Distortion Local Eigenmaps

Link to Poster:…

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