Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6863267 | Neural Networks | 2015 | 13 Pages |
Abstract
There has been extensive research in recent years on the multi-scale nature of hippocampal place cells and entorhinal grid cells encoding which led to many speculations on their role in spatial cognition. In this paper we focus on the multi-scale nature of place cells and how they contribute to faster learning during goal-oriented navigation when compared to a spatial cognition system composed of single scale place cells. The task consists of a circular arena with a fixed goal location, in which a robot is trained to find the shortest path to the goal after a number of learning trials. Synaptic connections are modified using a reinforcement learning paradigm adapted to the place cells multi-scale architecture. The model is evaluated in both simulation and physical robots. We find that larger scale and combined multi-scale representations favor goal-oriented navigation task learning.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M. Llofriu, G. Tejera, M. Contreras, T. Pelc, J.M. Fellous, A. Weitzenfeld,