As neuroscience gradually uncovers how the brain represents and computes with high-level spatial information, the endeavor of constructing biologically-inspired robot controllers using these spatial representations has become viable. Grid cells are particularly interesting in this regard, as they are thought to provide a general coordinate system of space. Artificial neural network models of grid cells show the ability to perform path integration, but important for a robot is also the ability to calculate the direction from the current location, as indicated by the path integrator, to a remembered goal. This paper presents a neural system that integrates networks of path integrating grid cells with a grid cell decoding mechanism. The decoding mechanism detects differences between multi-scale grid cell representations of the present location and the goal, in order to calculate a goal-direction signal for the robot. The model successfully guides a simulated agent to its goal, showing promise for implementing the system on a real robot in the future.
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The author is grateful to Keith Downing and Trygve Solstad for helpful discussions, feedback and advice.
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Edvardsen, V. Goal-directed navigation based on path integration and decoding of grid cells in an artificial neural network. Nat Comput 18, 13–27 (2019). https://doi.org/10.1007/s11047-016-9575-0
- Goal-directed navigation
- Path integration
- Continuous attractor networks
- Grid cells
- Entorhinal cortex