# A Control Theoretic Approach to Addressing Hippocampal Function > **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $526,277 ## Abstract Understanding the interplay between sensory input, endogenous neural dynamics, and behavioral output is key to understanding how the brain works at the level of neural computation. Hippocampal place cells are an ideal system to investigate this closed-loop interaction, as they combine input from self-motion cues and external landmarks to continuously update a dynamic neural network that creates an internal representation of location on a cognitive map. The normal functioning of this system is critical for such cognitive processes as flexible navigation, imagining the future, and autobiographical (episodic) memory. Animals can localize themselves by keeping track of their movements and continuously updating their location on their cognitive map based on these movements—a process called “path integration.” In order for this updating to coincide with the animal's actual displacement in the world, the relationship between physical movement and position updating on the map must be fine-tuned. Previous work in a novel virtual- reality environment demonstrated that external landmarks serve as a teaching signal to guide this fine- tuning process through visual experience—a process called “path integration gain recalibration.” The present proposal seeks an in-depth understanding of the neurophysiological mechanisms that drive this previously unknown degree of plasticity in path integration dynamics, and how these mechanisms support navigation. Attractor neural network models provide a compelling framework for understanding the internal neural dynamic mechanisms underlying path integration. However, present versions of these models do not capture the path-integration plasticity. Moreover, studying the nonlinear dynamics of how visual inputs interact with the attractor network can provide important insights into how the brain creates stable spatial representations that allow flexible solutions to spatial behavioral tasks. Principles and analytical techniques of control-systems engineering will be applied to experiments that record the activity of spatially tuned neurons from the hippocampus and related brain regions of rats to address a series of both hypothesis-driven and discovery-driven aims: in Aims 1 and 2 we will investigate neurophysiological mechanisms and behavioral consequences of the plasticity of the path integration computation and in Aims 3 and 4 we will investigate the signals (such as error signals) that underlie the interactions between landmarks and path integration in the hippocampus that likely drive this plasticity. These experiments will provide fundamental insight into how the brain computes representations of the spatiotemporal context of an organism's experience, which is thought to constitute the framework that the brain uses to organize, interrelate, and bind the various aspects of an experience and store them as a coherent memory. Such insight will help understand how neurodegenerative disorders, such as Alzheimer's Disease and... ## Key facts - **NIH application ID:** 10812762 - **Project number:** 2R01NS102537-06A1 - **Recipient organization:** JOHNS HOPKINS UNIVERSITY - **Principal Investigator:** Noah John Cowan - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $526,277 - **Award type:** 2 - **Project period:** 2017-08-01 → 2028-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10812762 ## Citation > US National Institutes of Health, RePORTER application 10812762, A Control Theoretic Approach to Addressing Hippocampal Function (2R01NS102537-06A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10812762. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*