Characterizing the underlying population code to understand the functional organization of the hippocampus and the lateral hypothalamus

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Project Summary/Abstract Recent advancements in neural recording/imaging technologies and computational methods have generated a renewed interest in studying coordinated population activity. Understanding the population code can help us better understand the complex mechanisms behind substance use disorders (SUD). One leading idea is that high-dimensional neural activity, such as simultaneous recordings from hundreds to thousands of cells, can lie on low-dimensional manifolds, such that a handful of latent variables can accurately describe the activity of all recorded neurons. The lateral hypothalamus (LH) is a brain area well-known for its functional diversity – individual cells respond with great heterogeneity to a wide range of appetitive behaviors, and LH stimulation can evoke a variety of actions ranging from feeding to social interaction. This project proposes to use the latest nonlinear dimensionality reduction techniques to extract the low-dimensional manifolds representing population activity patterns that geometrically organize the heterogeneous single neuron activity. These manifolds can then be used to achieve this project’s main goal – differentiating LH neural population encoding of natural reward-seeking behaviors and maladaptive drug-seeking behaviors. In addition, novel computational modeling methods will be used to perform unsupervised detection of internal neural states that guide animals switching between these two reward-seeking behaviors. Finally, state-of-the-art cellular-resolution simultaneous stimulation and imaging microscopy will be used to casually perturb animal behavior and/or neural activity patterns by activating sequences of neurons along trajectories on the low-dimensional manifolds. Importantly, Aims 1 and 2 support these goals by offering training in the use of the necessary computational and instrumentation techniques. Ultimately, results obtained from this project will advance our understanding of the neural mechanisms separating harmful drug-seeking behaviors and useful natural reward-seeking behaviors, such that SUD treatments with more precise targets can be developed that minimize unwanted side effects.