# Grid Cell Inspired Scalar Encoder

**URL:** <https://discourse.numenta.org/t/grid-cell-inspired-scalar-encoder/4242>\
**Category:** Engineering\
**Tags:** encoders, grid-cells\
**Created:** [July 23, 2018, 5:57pm UTC](https://discourse.numenta.org/t/grid-cell-inspired-scalar-encoder/4242 "2018-07-23T17:57:30Z")\
**Posts on this page:** 1\
**Showing post:** 21

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**Author:** ![Gary\_Gaulin](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/gary_gaulin/32/2248_2.png) [@Gary\_Gaulin](https://discourse.numenta.org/u/Gary_Gaulin)\
**Post date:** [August 12, 2018, 8:27pm UTC](https://discourse.numenta.org/t/grid-cell-inspired-scalar-encoder/4242/21 "2018-08-12T20:27:52Z")

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> [@Casey](#):
>
> I’m not sure if neurogenesis happens in CA1 except with injury. I haven’t researched that, though. There’s a lot more neurogenesis in the dentate gyrus, at least, and more with exercise (maybe for more place fields with more exploration). I might be remembering wrong.

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](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.

> [@Casey](#):
>
> That mostly makes sense, and RAM is a good comparison, but it can’t work exactly like RAM. A single bit can’t make a difference since there is noise and multiple place cells respond because place fields overlap.  
> I’m not sure about this, but I think basket cells target densely (more than 50% like would be needed in binary). Lateral competition like in the spatial pooler could work. If there are some 1s which should be 0s for the cell to fire, other cells will outcompete it.  
> Although, there’s tons of redundancy maybe (thousands of grid cells rather than tens), so thinking of it as an exact binary number where every bit matters might be right.

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 it’s in such as the city or country and nearby places it’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, it’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 I’m working on](https://discourse.numenta.org/t/oscillatory-thousand-brains-minds-eye-for-htm/3726) 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 “Eureka!!” moment. In either case I have to thank Eric Collins for having starting this very exciting thread.

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