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Activity Number: 256 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #305041
Title: Testing Global Dynamics in C. Elegans
Author(s): Anastasia Dmitrienko* and John Cunningham and Sean Bittner
Companies: Columbia University and Columbia University and Columbia University
Keywords: linear dynamical systems; machine learning; computational neuroscience; global brain dynamics

We explore evidence of global dynamics in C. elegans by testing hypotheses enabled by novel maximum entropy sampling methods. The C. elegans worm is a model organism studied by neuroscientists due to its small number of 302 neurons and its similarity to more complex organisms.

According to Kato et. al, simultaneously recording all the activity of the neurons in C. elegans demonstrated that most active neurons share information by engaging in coordinated, dynamical network activity that corresponds to the sequential assembly of motor commands. We use the tensor maximum entropy (TME) method as a framework to test the novelty of these population-level findings against simpler features of times and neurons. TME samples surrogate datasets from a probability distribution that maximizes Shannon entropy with the correct average primary features. Thus, the higher-order structures of the surrogate datasets are determined completely by the primary features.

Our testing across times and neurons using TME suggests that the surrogate datasets have the same higher-order structure and dynamics as the original data, challenging the statistical significance of the conclusions reached by Kato.

Authors who are presenting talks have a * after their name.

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