Cell assemblies, defined as groups of neurons forming temporal spike coordination, are thought to be fundamental units supporting major cognitive functions. However, detecting cell assemblies is challenging since they can occur at a range of time scales and with a range of precisions, from synchronous spikes to co-variations in firing rate. In this dissertation, we use a recently published cell assembly detection (CAD) algorithm that is capable of detecting assemblies at a range of time scales and precisions. We first showed that the CAD method can be applied to sparser spike train data than what have previously been reported. This allows us to apply the method to calcium imaging data of neuronal activity in the CA1 region of the hippocampus, a brain region critical for encoding and generalizing contextual memories, during contextual fear conditioning training and tests. We found that CA1 hippocampus plays a role in encoding and retrieving contextual memories. In particular, there exists a group of neurons whose exploratory activities predict the animal’s ability to distinguish different contexts. Moreover, the mechanisms for processing contextual information are different between two genetically distinct strains of mice that are included in the experiments. Lastly, as inspired by experimental findings, we extend the CAD method to extract multiscale assemblies whose activities happen at different time scales.
Mathematics, Applied, Neuroscience
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Truong, Phan Minh Duc, "CELL ASSEMBLY DETECTION IN LOW FIRING-RATE SPIKE TRAIN DATA" (2020). Mathematics Theses and Dissertations. 8.