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Activity Number:
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440
- SLDS CSpeed 8
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #318967
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Title:
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Adaptive Smoothing Dimension Reduction Methods for Neural Firing Rate Data
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Author(s):
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Angel Garcia de la Garza* and Britton Sauerbrei and Jeff Goldsmith
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Companies:
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Columbia University and Janelia Research Campus HHMI and Columbia University, Department of Biostatistics
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Keywords:
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Adaptive Smoothing;
Functional Data Analysis;
Neuroscience;
Dimension Reduction;
Principal Component Analysis
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Abstract:
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Recent advances have allowed high-resolution observations of firing rates for a collection of individual neurons; these observations can provide insights into patterns of brain activation during the execution of tasks. Our data come from an experiment in which mice performed a reaching motion following an auditory cue, and contain measurements on firing rates from neuron activation in the motor cortex before and after the cue. In this setting, steep increases in firing rates after the cue are expected. Our dimension reduction technique adequately models these sharp changes over time and correctly captures these activation patterns. Initial results suggest different patterns of activation, representing the involvement of different motor cortex functions at different times in the reaching motion.
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Authors who are presenting talks have a * after their name.
