Revealing multiscale structures of neuronal networks

Gal Mishne

(Yale University)

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Date: February 13, 2018

License: CC BY-NC-ND 2.5


Experimental advances in neuroscience enable the acquisition of increasingly large-scale, high-dimensional and high-resolution neuronal and behavioral datasets, however addressing the full spatiotemporal complexity of these datasets poses significant challenges for data analysis and modeling. I present a new geometric analysis framework, and demonstrate its application to the analysis of calcium imaging from the primary motor cortex in a learning mammal. To extract neuronal regions of interest, we develop Local Selective Spectral Clustering, a new method for identifying high-dimensional overlapping clusters while disregarding noisy clutter. We demonstrate the capability of this method to extract hundreds of detailed somatic and dendritic structures with demixed and denoised time-traces. Next, we propose to represent and analyze the extracted time-traces as a rank-3 tensor of neurons, time-frames and trials. We introduce a nonlinear data-driven method for tensor analysis and organization, which infers the coupled multi-scale structure of the data. In analyzing neuronal activity from the motor cortex we identify in an unsupervised manner: functional subsets of neurons, activity patterns associated with particular behaviors, and long-term temporal trends. This general framework can be applied to other biomedical datasets, in neuroscience and beyond, such as fMRI, EEG and BMI.

Joint work with Ronen Talmon, Ron Meir, Jackie Schiller, Maria Lavzin, Uri Dubin and Ronald Coifm

Created: Friday, February 16th, 2018