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Shortcutting from self-motion signals reveals a cognitive map in mice
Jiayun Xu1, Mauricio Girardi-Schappo2, Jean-Claude Beique1,3,4
1Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada.
Elife
|November 11, 2024
Summary
Mice learned to find hidden food in a maze without landmarks, using a target estimation vector (TEV) to guide their path. They demonstrated cognitive map formation by taking shortcuts between learned food locations.
Area of Science:
- Neuroscience
- Animal Behavior
- Cognitive Science
Background:
- Animals rely on spatial learning to navigate environments.
- Understanding navigation mechanisms is key to understanding spatial memory.
Purpose of the Study:
- To investigate spatial learning and cognitive map formation in mice.
- To quantify how mice estimate target locations in an environment without landmarks.
Main Methods:
- Mice were trained to find food in a 100-hole maze.
- Trajectory and active hole check data were analyzed.
- A quantitative framework, the target estimation vector (TEV), was developed to model mouse navigation.
Main Results:
- Mice efficiently navigated to the food source without landmarks.
- The computed TEV accurately predicted mouse routes and search patterns.
- Evidence suggests distinct learning dynamics for direction and distance estimation.
- Mice demonstrated cognitive map formation by taking shortcuts between two learned food sites.
Conclusions:
- The target estimation vector (TEV) provides a framework for understanding spatial estimation.
- TEV may be linked to the function of hippocampal place cells.
- Mice can generate and utilize cognitive maps for efficient navigation.
