6 Grid cells and place cells on Khepera
7.1 Future work
Our recommendation for the future work falls into two categories for modelling and experiments on a mobile robot, hi the first category, our model of spatial representation can be further examined by inclusion of sensory cells and border cells. Although the anatomical location of sensory cells is still unknown, the fusion of sensory information in the model can be achieved and evaluated with two separate approaches. Firstly, the sensory information can be combined with the output of place cells in the DG to produce place cells that represent both location and context information. In this way, the hippocampus seems to be responsible for the episodic memory and spatial representation at the same time. Secondly, place cells in CA1-CA3 can be associated with sensory information. With this approach, when the field of a place cell representing a location with accumulated error, sensory information can correct
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it. Moreover, in the same way, place cells can give projections to the grid cells to correct their path integration eiTor too.
Although the dual oscillator model explains some of the behavioural properties of grid cells, it does not explain the relational positioning of grid cells, i.e. when a correction to the path integration happens without an attractor network, only a location of the associated grid cells gets changed which is in contrary with biological evidence. Therefore, a combination of the dual oscillator model and attractor networks could result in correcting the location of more than one grid cell in the presence of external stimuli. In particular, this has been observed during the cue card experiments when all grid cells in the medial entorhinal cortex change their base orientation.
In the second category, the mobile robot platform provides a good opportunity for testing these hypotheses in the presence of environmental and internal path integration error. We have implemented grid cells and place cells on the robot. These experiments which can be expanded with both path integration and path correction mechanisms. Our implementation of head direction cells can be expanded by a ring attractor network which can also be bound to visual information in the environment. The output of this network can be fed to the grid cell network to create a representation of the grid cells. The robot’s proximity sensors can be used to model border cells with a Gaussian function on the output of these proximity sensors on the robot. Our final suggestion is to use a combination of proximity sensors to detect obstacles in the environment and use them as a representation of sensory information in the area and evaluate its effect on path integration.
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