A biologically inspired hierarchical goal directed navigation model

- We propose a hierarchical goal directed navigation model based on head direction cells, grid cells, and place cells using look-ahead linear trajectory probes.
- We significantly extend our previous work by representing the spatial environment at multiple scales using a hierarchy of place cell maps.
- Our hierarchical goal directed navigation model can extend the linear look-ahead probe range arbitrarily while maintaining its duration constant.
- Keeping probe duration constant while extending probe range stabilizes the amount of accumulated noise in the model.
- We conduct simulated experiments to show our model's efficacy.

We propose an extended version of our previous goal directed navigation model based on forward planning of trajectories in a network of head direction cells, persistent spiking cells, grid cells, and place cells. In our original work the animat incrementally creates a place cell map by random exploration of a novel environment. After the exploration phase, the animat decides on its next movement direction towards a goal by probing linear look-ahead trajectories in several candidate directions while stationary and picking the one activating place cells representing the goal location. In this work we present several improvements over our previous model. We improve the range of linear look-ahead probes significantly by imposing a hierarchical structure on the place cell map consistent with the experimental findings of differences in the firing field size and spacing of grid cells recorded at different positions along the dorsal to ventral axis of entorhinal cortex. The new model represents the environment at different scales by populations of simulated hippocampal place cells with different firing field sizes. Among other advantages this model allows simultaneous constant duration linear look-ahead probes at different scales while significantly extending each probe range. The extension of the linear look-ahead probe range while keeping its duration constant also limits the degrading effects of noise accumulation in the network. We show the extended model's performance using an animat in a large open field environment.