New methods for identifying latent manifold structure from neural data | ASIC

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=p_WW3HSpc6w


Duration: 1:02:14
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For slides and more information on the paper, visit https://ai.science/e/learning-a-latent-manifold-of-odor-representations-from-neural-responses--2020-05-19

Speaker: Anqi Wu; Discussion Moderator: Rober Boshra

Motivation

Numerous studies in neuroscience posit that large-scale neural activity reflects noisy high-dimensional observations of some underlying, low-dimensional signals of interest. Discovering such low-dimensional signals or structures can help shed light on how information is encoded at the population level, and provide significant scientific insight into the brain. In this talk, Dr. Anqi Wu will present her work on developing Bayesian methods to identify such latent manifold structures .

Full abstract: Numerous studies in neuroscience posit that large-scale neural activity reflects noisy high-dimensional observations of some underlying, low-dimensional signals of interest. Discovering such low-dimensional signals or structures can help shed light on how information is encoded at the population level, and provide significant scientific insight into the brain. In this talk, I will present my work on developing Bayesian methods to identify such latent manifold structures, which are referred to as latent manifold tuning models. Firstly, I will describe the latent manifold tuning model to discover low-dimensional latent dynamics in multi-neuron spike train data with an application to hippocampal place cells. Secondly, I will present a similar latent model to learn interpretable latent embeddings in calcium imaging data with an application to olfactory neurons. We show that the models are able to reveal the underlying signals of neural populations as well as uncovering interesting topography of neurons where there is a lack of knowledge and understanding about the brain.



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Tags:
machine learning
deep learning
interpretability
embeddings