Unveiling secrets of brain function with generative modeling: Motion perception in primates & Cortical network organization in mice

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This Dissertation is comprised of two main projects, addressing questions in neuroscience through applications of generative modeling. Project #1 (Chapter 4) is concerned with how neurons in the brain encode, or represent, features of the external world. A key challenge here is building artificial systems that represent the world similarly to biological neurons. In Chapter 4, I address this by combining Helmholtz's “Perception as Unconscious Inference”---paralleled by modern generative models like variational autoencoders (VAE)---with the hierarchical structure of the visual cortex. This combination results in the development of a hierarchical VAE model, which I subsequently test for its ability to mimic neurons from the primate visual cortex in response to motion stimuli. Results show that the hierarchical VAE perceives motion similar to the primate brain. I also evaluate the model's capability to identify causal factors of retinal motion inputs, such as object motion. I find that hierarchical latent structure enhances the linear decodability of data generative factors and does so in a disentangled and sparse manner. A comparison with alternative models indicates the critical role of both hierarchy and probabilistic inference. Collectively, these results suggest that hierarchical inference underlines the brain's understanding of the world, and hierarchical VAEs can effectively model this understanding.

Project #2 (Chapter 5) is about how spontaneous fluctuations in the brain are spatiotemporally structured and reflect brain states such as resting. The correlation structure of spontaneous brain activity has been used to identify large-scale functional brain networks, in both humans and rodents. The majority of studies in this domain use functional MRI (fMRI), and assume a disjoint network structure, meaning that each brain region belongs to one and only one community. In Chapter 5, I apply a generative algorithm to a simultaneous fMRI and wide-field calcium imaging dataset and demonstrate that the mouse cortex can be decomposed into overlapping communities. Examining the overlap extent shows that around half of the mouse cortical regions belong to multiple communities. Comparative analyses reveal that calcium-derived network structure reproduces many aspects of fMRI-derived network structure. Still, there are important differences as well, suggesting that the inferred network topologies are ultimately different across imaging modalities. In conclusion, wide-field calcium imaging unveils overlapping functional organization in the mouse cortex, reflecting several but not all properties observed in fMRI signals.