Browsing by Author "Baker, Casey Michelle"

The brain encodes diverse cognitive functions through the coordinated activity of interacting neural circuits. Neural ensembles are groups of coactive neurons in these circuits that respond to similar stimuli. Neural ensembles are found throughout the brain and have been associated with many cognitive processes including memory, motor control, and perception. However, a key goal of systems neuroscience is to establish a functional link between neural activity and behavior and these previous studies established only a correlation between ensembles and behavior. Demonstrating a functional link between ensembles and behavior requires precise manipulation of ensemble activity. Manipulating ensemble activity allows neuroscientists to determine the patterns of neural activity that are necessary and sufficient to drive behavior. Additionally, recording and analyzing the activity of hundreds to thousands of neurons simultaneously allows neuroscientists to elucidate the patterns of neural activity underlying behavior. In this dissertation, we developed novel computational tools to help scientists selectively activate ensembles and analyze large-scale neural activity with single-cell resolution.One method to precisely activate cortical ensembles while limiting off-target effects is to stimulate pattern completion neurons. Pattern completion neurons are subsets of neurons in an ensemble that, when activated, can trigger the activation of the rest of the ensemble. However, scientists currently lack methods to reliably identify pattern completion neurons. The first project in this dissertation used computational modeling to identify characteristics of pattern completion neurons in cortical ensembles. We developed a realistic spiking model of layer 2/3 of the mouse visual cortex. We then identified ensembles in the network and quantified the pattern completion capability of different neuron pairs in an ensemble. We analyzed the relationship between structural and dynamic parameters and pattern completion capability. We found that multiple graph theory parameters, and degree in particular, could predict the pattern completion capability of a neuron pair. Additionally, we found that neurons that fired earlier in an ensemble recall event were more likely to have pattern completion properties than neurons that fired later. Lastly, we showed that we can measure this temporal latency in vivo with modern calcium indicators. The later projects in this dissertation used deep learning to improve calcium imaging analysis. First, we developed a semi-supervised pipeline for neuron segmentation to reduce the burden of manual labeling. We compensated for the low number of ground truth labels in two ways. First, we augmented the training data with pseudolabels generated with ensemble learning. Next, we used domain-specific knowledge to predict optimal hyperparameters from the limited ground truth labels. Our pipeline achieved state-of-the-art accuracy when trained on only 25% the number of manual labels as supervised methods. Lastly, we developed a spatiotemporal deep learning pipeline to predict the underlying electrical activity from calcium imaging videos. Calcium imaging provides only an indirect measurement of spiking neural activity, and various spike inference pipelines have attempted to accurately recover spiking timing and rate. Our pipeline improved the detection of single-spike events and improved spike rate prediction throughout the video. This improved performance will help scientists reconstruct neural circuits and study single-cell responses to stimuli. Overall, the tools developed in this dissertation will help systems neuroscience researchers establish a causal link between neural activity and behavior and will help determine the precise patterns of neural activity underlying these behaviors.