Neural Mechanisms of Navigational Decision Making - Project Summary:
A fundamental mammalian behavior is the ability to successfully navigate through our environment to reach a
goal (e.g. a left turn decision towards your favorite restaurant). What seems like a simple behavior is the result
of complex brain computations like the representation of space, decision-making and memory. The
Hippocampus (Hipp) and the medial entorhinal cortex (MEC) are critical brain regions for memory and the
representation of our environment. Hipp neurons are active in specific locations in an environment, exhibit
context dependent activity, and can code for elapsed time. MEC neural activity has been mainly studied during
open-field foraging behavior and shown to represent spatial and navigational variables like position, head-
direction, and speed. Contrary to the Hipp, the role of MEC in more complex behavior, such as goal-directed
navigation, is unknown. We hypothesize that MEC neurons are part of the circuitry critical for decision-making
during goal-directed navigation. This hypothesis is tested through a series of goal-directed spatial decision
making tasks while recording MEC neurons in rodents. First, we predict that MEC neurons represent
behaviorally relevant task features during a cue-based spatial decision making task. We designed a task in
which rats make spatial decisions (go left, go right) towards a food reward based on a visually presented cue.
The spiking activity of MEC neurons is recorded and related to the animal's behavior and features of the
environment (e.g., cue, rewards, animal's position). In our preliminary data we demonstrate that animals can
learn the task, recorded isolated cells with spatial and task-related coding. Second, we predict that MEC
population activity represents decision related information. A decision delay is introduced to test this
hypothesis, in which the animal needs to remember the cue before making the spatial decision. We expect to
accurately predict the upcoming decision through neural population decoding analyses of the activity during
the delay period. Our preliminary results demonstrate that animals can perform this complex task. Third, we
predict that Hipp/MEC dynamically interact in service of spatial decisions. We use minimally invasive
multichannel probes simultaneously recording Hipp/MEC through task learning, and predict that hippocampal
events (sharp-wave ripples) will reinstate neural population patterns in the MEC related to the upcoming
spatial decision. Further, we predict that Hipp/MEC interactions are indicative of behavioral performance
through learning. In our preliminary data we demonstrate the use of these probes, and the stability of
recordings over 7 weeks. Through a combination of novel behavioral and electrophysiological techniques, this
project will elucidate the role of MEC in spatial-decisions. Furthermore, understanding how MEC and Hipp
support these complex cognitive behaviors would fundamentally influence models of brain function in the
healthy, mentally ill, and diseased brain, potentially leading to prevention and cure.
