Friday, April 19, 2024
4/19/2024
Abstract: Elucidating the neural circuitry involved in creating a learned association is crucial to understanding the biology of both typical learning and maladaptive learning. Despite decades of research into learning and memory, the mechanisms whereby newly learned associations are encoded in sensory cortices remain unclear. New functional imaging methods in neuroscience research allow for the tracking of the same groups of neurons over long time periods with single-neuron resolution, permitting us to ‘visualize’ how these memories are acquired, consolidated, and forgotten in real-time. This research will probe the neural mechanisms whereby memories are encoded in sensory association cortex with unparalleled specificity using two-photon calcium imaging, optogenetics, and advanced behavioral tracking. By utilizing visual cued fear conditioning and chronic in vivo two-photon calcium imaging in visual association cortex (VisCtx) in transgenic GCaMP6-expressing mice, we have a powerful model to track hundreds of neurons across learning processes in higher-order brain regions. Ultimately this enables us to track a neuronal ensemble representing a memory as it forms. We hypothesize that a distinct ensemble of neurons in VisCtx that represents a predicted shock-outcome will emerge during learning. In addition to local neuronal ensemble activity being important for learning and memory, sleep and long-range circuit activity is also critical to encoding long-term learned associations in the brain. Therefore, we will track neuronal ensembles representing a learned association across vigilance states, to better understand the role for sleep behavior in forming a long-term memory trace. We propose that an ensemble representing a learned association will be consolidated through reactivation during sleep post-learning. Furthermore, we propose to incorporate imaging amygdala projections to cortex to integrate circuit dynamics into our understanding of long-term storage of memory in cortex. We hypothesize that establishing a predicted-outcome responsive neuronal ensemble will be dependent on amygdala feedback to cortex after learning. Our approach allows for novel investigation of the naturalistic emergence of a learned association, including elucidating some of the circuits and behaviors that promote encoding of long-term memory in cortical circuits.
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