Developing and validating computational models of working memory in the human prefrontal cortex

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We perceive the world around us in the form of a continuous stream that enters our mind through the lens of our senses. At every moment we can only see, hear and feel bits and pieces of our surroundings, yet we constantly demonstrate behaviors that reach far past the simple reflexive reactions. Memory is inarguably an essential component of our ability to bind information over long time spans. The human brain is able to memorize experiences through several types of memory systems that differ depending on the temporal and contextual requirements of the behavior. One of the critical memory systems is working memory - the ability to store and manipulate information over short periods. The long-term goal of research in my group is to specify how the human brain maintains and organizes the previous experiences in memory to make them readily accessible for rapid decision making. My group uses a combination of experimental and computational techniques to build mechanistic models of the neural computations underlying memory in the human brain. To do this, we focus on 1) developing benchmarks from human brains responses to quantify the progress towards building such models; 2) developing computational models with the goal of maximizing their scores against the neural benchmarks. The overall goal of this proposal is to apply our modeling approach towards understanding the neural computations underlying working memory in the human prefrontal cortex. Specifically 1) we use neuroimaging techniques to characterize the brain response patterns during various memory-dependent tasks. We then use this data to construct benchmarks from neural responses in the prefrontal cortex; 2) we form scientific hypotheses in the form of computational models by making specific choices of model architecture, learning objective, and environment composition. 3) we validate the correctness of these hypotheses by validating them against the neural benchmarks. Computational models are becoming increasingly important in understanding physiological systems. These mechanistic models allow us to encapsulate our integral knowledge of a system into a framework that may not be verbally expressive. While current theories of the prefrontal cortex highlight its role in tracking goals and ways to achieve them, they do not offer mechanistic explanations as to how such information may be represented in the population of neuronal responses. This application proposes a computational framework for developing mechanistic models of the neural computations underlying working memory in the prefrontal cortex and tackles the structural, developmental, and ecological aspects of such models. The resulting mechanistic models with known connectivity are invaluable for understanding the neural computations underlying working memory in the prefrontal cortex.