A goal-directed spatial navigation model using forward trajectory planning based on grid cells
Article first published online: 7 MAR 2012
Published 2012. This article is a U.S. Government work and is in the public domain in the USA.
European Journal of Neuroscience
Volume 35, Issue 6, pages 916–931, March 2012
How to Cite
Erdem, U. M. and Hasselmo, M. (2012), A goal-directed spatial navigation model using forward trajectory planning based on grid cells. European Journal of Neuroscience, 35: 916–931. doi: 10.1111/j.1460-9568.2012.08015.x
- Issue published online: 19 MAR 2012
- Article first published online: 7 MAR 2012
- Received 18 May 2011, revised 8 December 2011, accepted 21 December 2011
- grid cell;
- place cell;
- prefrontal cortex
A goal-directed navigation model is proposed based on forward linear look-ahead probe of trajectories in a network of head direction cells, grid cells, place cells and prefrontal cortex (PFC) cells. The model allows selection of new goal-directed trajectories. In a novel environment, the virtual rat incrementally creates a map composed of place cells and PFC cells by random exploration. After exploration, the rat retrieves memory of the goal location, picks its next movement direction by forward linear look-ahead probe of trajectories in several candidate directions while stationary in one location, and finds the one activating PFC cells with the highest reward signal. Each probe direction involves activation of a static pattern of head direction cells to drive an interference model of grid cells to update their phases in a specific direction. The updating of grid cell spiking drives place cells along the probed look-ahead trajectory similar to the forward replay during waking seen in place cell recordings. Directions are probed until the look-ahead trajectory activates the reward signal and the corresponding direction is used to guide goal-finding behavior. We report simulation results in several mazes with and without barriers. Navigation with barriers requires a PFC map topology based on the temporal vicinity of visited place cells and a reward signal diffusion process. The interaction of the forward linear look-ahead trajectory probes with the reward diffusion allows discovery of never-before experienced shortcuts towards a goal location.