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A flow-based latent state generative model of neural population responses to natural images
- Source :
- Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
- Publication Year :
- 2022
-
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.
- Subjects :
- Computational Neuroscience
education.field_of_study
Computer science
business.industry
Population
Pattern recognition
Quantitative Biology::Genomics
Sensory processing and perception
Identification (information)
Generative model
Visual cortex
medicine.anatomical_structure
Flow (mathematics)
Pupillary response
medicine
Computer Science::Programming Languages
State (computer science)
Noise (video)
Artificial intelligence
Mathematics::Representation Theory
business
education
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
- Accession number :
- edsair.doi.dedup.....1d875ffbb63bb6dbe2858b13d1ad95cf