SUBNETVIS · Identifying subtype specific networks involved in sensory representation in mouse primary visual cortex

To produce relevant behaviors, the brain integrates and processes sensory information. Neurons in the primary visual cortex extract sensory information by responding preferentially to certain visual features. This feature preference, or tuning, is thought to arise from structured connections established between cortical neurons. Accordingly, it was found that connected neurons in the cerebral cortex share similar tuning properties. Interestingly, the neurons populating the cerebral cortex correspond to numerous neuronal subpopulations, involved in different functions. However, the functional involvement of this large neuronal diversity in cortical computation has been so far studied for a few broad neuronal subpopulations, leaving the fine subpopulations mainly unexplored. Do these poorly studied neuronal subpopulations share similar tuning properties? Is the structured connectivity giving rise to tuning subpopulation specific? I will use a new technique referred as in situ transcriptomics to study the tuning properties and locomotor modulation of the diverse neuronal subpopulations in the mouse primary visual cortex. This technique provides high throughput identification of neuronal subpopulations on fixed tissue based on the transcriptomic signature of neurons. I will thus determine the identity of in vivo recorded neurons a posteriori and decipher the relationships between cell identity and responses to visual stimuli. Combining this approach with single cell initiated monosynaptic tracing, I will then explore the link between subpopulation specific connectivity and tuning properties. This project will greatly contribute to the understanding of how cortical neuronal subpopulations interact to encode sensory information. I will perform these experiments in the Cortical Processing Laboratory at University College London, led by Professors Kenneth Harris and Matteo Carandini.