Decoding of Chemical Information from Populations of Olfactory Neurons
Schedule
Wednesday, May 6, 2020
17:30 UTC
Pedro Herrero-Vidal
New York University
Host: NERV
Recording
Open recording
Event Information
Recording
Available
Host
NERV
Duration
70 minutes
Abstract
Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system of the mouse for technological applications. Pedro and co-researchers have successfully extracted odor identity and concentration from olfactory receptor neuron low-dimensional activity trajectories. They have further developed a novel method to identify a shared latent space that allowed decoding of odor information across animals.
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