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  A flow-based latent state generative model of neural population responses to natural images

Bashiri, M., Walker, E., Lurz, K.-K., Jagadish, A., Muhammad, T., Ding, Z., et al. (2022). A flow-based latent state generative model of neural population responses to natural images. In M. Ranzato, A. Beygelzimer, P. Liang, J. Vaughan, & Y. Dauphin (Eds.), Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021) (pp. 15801-15815). Red Hook, NY, USA: Curran.

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 Creators:
Bashiri, M, Author
Walker, EY, Author
Lurz, K-K, Author
Jagadish, AK1, Author                 
Muhammad, T, Author
Ding, Z, Author
Ding, Z, Author
Tolias, AS, Author           
Sinz, FH, Author
Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              

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 Abstract: We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations. To this end, we combine (1) state-of-the-art deep networks for stimulus-driven activity and (2) a flexible, normalizing flow-based generative model to capture the stimulus-conditioned variability including noise correlations. This allows us to train the model end-to-end without the need for sophisticated probabilistic approximations associated with many latent state models for stimulus-conditioned fluctuations. We train the model on the responses of thousands of neurons from multiple areas of the mouse visual cortex to natural images. We show that our model outperforms previous state-of-the-art models in predicting the distribution of neural population responses to novel stimuli, including shared stimulus-conditioned variability. Furthermore, it successfully learns known latent factors of the population responses that are related to behavioral variables such as pupil dilation, and other factors that vary systematically with brain area or retinotopic location. Overall, our model accurately accounts for two critical sources of neural variability while avoiding several complexities associated with many existing latent state models. It thus provides a useful tool for uncovering the interplay between different factors that contribute to variability in neural activity.

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 Dates: 2021-102022-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1101/2021.09.09.459570
 Degree: -

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Title: Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021)
Place of Event: -
Start-/End Date: 2021-12-06 - 2021-12-14

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Title: Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Source Genre: Proceedings
 Creator(s):
Ranzato, M, Editor
Beygelzimer, A, Editor
Liang, PS, Editor
Vaughan, JW, Editor
Dauphin, Y, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 15801 - 15815 Identifier: ISBN: 978-1-7138-4539-3