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Activity Number: 286 - Learning Networks from Point Processes: Neuronal Connectivity Networks and Beyond
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #309203
Title: A Universal Nonparametric Event Detection Framework for Neuropixels Data
Author(s): Shizhe Chen* and Hao Chen and Xinyi Deng
Companies: University of California, Davis and University of California, Davis and Columbia University
Keywords: change-points detection; Neuropixel; nonparametric

Neuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as ``change-points". The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics

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

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