Event Location: GHC 4405

Abstract: Understanding how neural circuits in the brain enable complex, flexible behavior is now a tangible endeavor. This has in large part been made possible by the ability to record from neurons in mammals, such as rats and monkeys, as the animals are performing a behavioral task. One can then ask how a neuron’s firing is related to the animal’s behavior (i.e., determine the neuron’s behavioral correlate) and investigate the relationship between behavior and neural activity. Technologies developed over the last 15 years have enabled the precise placement of electrodes in the brain and the recording of 100 or more neurons simultaneously during behavior. The ability to record from many neurons at fine time scales has allowed researchers to analyze sequences of activity (neuron A’s firing with respect to neuron B’s firing). Sequences are important because the learning of connections between a pair of neurons depends on the relative spike timing of the neuron pair. In addition, recording from populations of neurons rather than single neurons allows us to see how information is distributed and represented across an entire brain region or even multiple brain regions.

In this proposal I concentrate on the hippocampus, a brain region known to play a crucial role in episodic memory and spatial navigation, which has also been implicated in self-projection and imagination. The rodent hippocampus has been an extremely informative model system because its neurons are densely packed and relatively easy to access with electrodes, and because the firing of hippocampal pyramidal cells has a clear behavioral correlate: the animal’s location in an environment. At any given time as an animal moves through an environment, the population of pyramidal cells in the hippocampus is representing the animal’s physical location.

However, the hippocampal representation of location is dynamic. Roughly every 125 ms during the animal’s movement (the period of the theta rhythm, a 5-10 Hz brain oscillation), the location represented by the hippocampus begins slightly behind the animal and smoothly advances to ahead of the animal. Furthermore, at choice points in a maze, as an animal demonstrates tentative behaviors suggestive of considering its options, the hippocampal representation of location sweeps forward from the animal’s current location down the possible paths it might take. This has been hypothesized to be a mechanism for evaluating the outcome of potential choices. At other times, while the animal is paused and inattentive but awake, the hippocampus replays sequences of neural activity representing behavioral experiences in the order they were experienced (forward replay) and in the reverse order that they were experienced (backward replay). The neural mechanisms supporting backward replay are currently unknown. Replay is thought to be important for consolidating recent memories into long-term memory and for learning general knowledge structures (i.e. cognitive maps) of the environment.

In this proposal, I investigate two main questions. In Specific Aim 1, I explore the nature of hippocampal representations on a task that manipulates, over the course of a recording session, the animal’s confidence about the correct choice to make at a decision point in a maze. How do hippocampal representations change when there is more or less certainty about the correct behavior? Are they more prospective (representing locations further ahead of the animal) or more retrospective? Are changes in hippocampal representations present all over the maze or are they specific to the choice point? Do the forward sweeping representations correlate with the direction the animal is facing, suggesting that the animal’s attention and/or environmental sensory cues potentially drive the sweep? Addressing these questions will help us understand how hippocampal representations contribute to navigation in an uncertain world.

In Specific Aim 2, I will investigate how the hippocampal network may be capable of constructing a cognitive map of the environment, while also supporting episodic memory. To address this question, I will construct a computational model of hippocampal place cells to test my hypothesis that the recently discovered theta phase gradient across the hippocampus can be exploited to learn a cognitive map of the environment, allowing for the expression of forward, backward, and novel path sequences during replay.

Committee:David S. Touretzky, Chair

Tai Sing Lee

Reid Simmons

George Stetten

A. David Redish, University of Minnesota