Grid Cell Inspired Scalar Encoder

A direct correlation has been documented in animals that hide their food in multiple hard to later find locations, and the spatial mapping region of London taxi drivers:

http://www.pnas.org/content/97/8/4398

From what I have over the years found in research papers this neurogenesis is something that must be accounted for in neuroscientific models.

I now think the difference is due to a biological (would still qualify as a) RAM having the advantage of not being burned out like a digital RAM would be from activation of more than one memory location at a time. Also, the biological circuit would need at least a small amount of pyramidal cell activation, as opposed to a digital circuit where the first address is all zeros, totally inactive.

Digital RAM requires exact powers of 2, while this biological circuit would require a little less. The range of 1.4 to 1.7 should work very well. When at a given place it seems we are at the same time aware of the place itā€™s in such as the city or country and nearby places itā€™s associated with. The way grids move when the head angle changes makes sense too.

Being as selective as a digital RAM would for us be a disadvantage. This would be like only being able to draw maps where only one place can be shown, itā€™s not even a map. Normally boundary/border/barrier cells and place cells only become active when an animal is in the vicinity, which is something that I have not yet accounted for in the model Iā€™m working on that currently maps everything in its environment, which works fine for a small arena but very large areas would become unnecessarily overwhelming.

I can only find supporting evidence and feel close to another ā€œEureka!!ā€ moment. In either case I have to thank Eric Collins for having starting this very exciting thread.

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may be it will be good to randomize the bits i.e. randomly remap/permute the bits

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