Friday, October 11, 2024
10/11/2024
PROJECT ABSTRACT Sociality is a fundamental part of our rich life as social animals. Notably, abnormalities in social cognition are a hallmark of various psychiatric disorders such as schizophrenia and autism. Understanding the social brain, the neural basis of social behavior, is an essential step toward addressing these issues. One of the building blocks of social cognition is social memory, which comprises familiarity, the sense that an individual was previously encountered, and recollection, the detailed storage and recall of social episodes. However, these two processes have conflicting computational requirements: while familiarity requires abstraction and generalization, recollection involves the creation of specialized representations to efficiently memorize many complex, multi- sensory experiences. The question of how the brain implements these two processes to achieve social memory is still open. One hypothesis is that these two processes are simultaneously implemented, and used in synergy to optimize memory, by the hippocampal-cortical network. Here, I will test this hypothesis using artificial neural networks and neural recordings from mice and human subjects exposed to social stimuli. In the first aim, I will analyze data from the entorhinal cortex (EC), the main input region to the hippocampus, of mice interacting with familiar and novel conspecifics and compare it to previous hippocampal data to reveal the specific roles of these regions during social cognition. In the second aim, I will develop a modular computational model inspired by the connectivity structure of the hippocampus with the EC and the neocortex, and use it to test how specialization and abstraction can be used in synergy to achieve efficient memory. This model will be trained to reproduce the mouse data and used to make testable predictions on how social representations in hippocampal and cortical regions change during learning. Finally, in the third aim, I will extend the analysis to human cognition by first analyzing previous single-cell recordings of human patients exposed to familiar and novel faces. I will then co-design a new experiment to reveal the coordinated change of the neural code in simultaneously-recorded hippocampal and cortical regions and directly test how the synergy between abstraction and specialization can improve memory. Given my quantitative training and extensive experience in collaborating with experimentalists, I am in a unique position to conduct this work at the interface between computational modeling and cross-species data analysis. The K99 phase of this work will take place at the Zuckerman Institute at Columbia University, a vibrant and immer- sive scientific hub hosting world-class theoreticians and experimental neuroscientists. Thanks to the mentorship of Drs. Fusi, Siegelbaum, and Rutishauser, all of whom are leaders in their fields, this project will allow me to complete my training and start an independent group in interdisciplinary computational neuroscience.
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