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The Taub Faculty of Computer Science Events and Talks

Geometry-based Dynamic Connectivity Analysis of Biological Neural Networks
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Avigail Cohen-Rimon (M.Sc. Thesis Seminar)
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Wednesday, 28.09.2022, 11:00
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Zoom Lecture: 96390522948
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Advisor: Prof. Ronen Talmon and Prof. Jackie Schiller
Learning in organisms is one of their most fundamental but intricate processes. Understanding learning is a longstanding problem in neuroscience as well as in artificial neural networks. In this work, we focus on studying in biological networks during motor learning through the lens of the connectivity of neurons in the primary motor cortex (M1). For this purpose, we analyze the neural activity recorded from awake and behaving mice using two-photon calcium imaging. These imaging methods enabled us to acquire longitudinal neuronal activity with cellular resolution in many hundreds of cells in one recording session. The recordings from each animal are acquired during several days, following the same neurons, while the animal learns a complex hand-reach task. Our analysis is based on a representation of the dynamic network underlying the neural activity as a sequence of graphs, where each graph describes the neuronal connectivity at a certain point in time during the learning process. In this talk, I will first present the computational methods we developed for measuring and quantifying the similarity between graphs in terms of their connectivity patterns. Then, I will show the application of these methods to neural activity. Based on our analysis, we found that the connectivity of the neural network in M1 smoothly converges to a steady connectivity state as the learning process progresses, which is characterized by the formation of functional subsets of neurons that operate in synchrony. Moreover, we show that blocking the dopamine transmission from the ventral tegmental area (VTA) to M1 disturbs the network convergence and hampers motor learning. This indicates that the transition of the network requires plasticity in the connectivity within the network.