HTM School 14: Grid Cells

[Bitking]

@dillon.bender

From an actual implementation perspective, do the grid cell modules have a fixed size on their input space once they are initialized? How would they handle being ported to a larger environment with new locations? Do they have a mechanism for dynamically adjusting to different sized environments? For example, what exactly would happen if the room that your mouse cursor moves within suddenly became larger or smaller? How would the grid cells react?

I think this does well as an answer to your questions!

Hippocampal Remapping and Its Entorhinal Origin

[dillon.bender]

@Bitking

@dillon.bender

From an actual implementation perspective, do the grid cell modules have a fixed size on their input space once they are initialized? How would they handle being ported to a larger environment with new locations? Do they have a mechanism for dynamically adjusting to different sized environments? For example, what exactly would happen if the room that your mouse cursor moves within suddenly became larger or smaller? How would the grid cells react?

I think this does well as an answer to your questions!

Thanks for that! This was a very interesting read! I learned very much about how the hippocampus and entorhinal cortex work together to represent location.

However, even after trying to pay close attention to the most interesting and salient information, I’m still unsure how to answer my own questions. The paper was mostly about hippocampal place field locations and place cell remapping, so I still don’t know much about grid cell dynamics or their relationship with sensory input.

[dillon.bender]

One thing I noticed was the huge emphasis on firing rate remapping and rate encoding. Such as this snippet

“the hippocampus can simultaneously convey information related to the position of an animal and to the cues present in the environment. During rate remapping, the integrity of the spatial code is preserved because place fields are stable, but the precise firing rate of neurons varies to encode information not related to the current position of an animal.”

This sounds like an important feature of the brain.

[Bitking]

Did you get anything out of figure 3?

Section 3 does call out related papers - I have not looked at them but it may be a good point to dig deeper.

That said - I picked up on that you are looking to see how the senses end up forming these patterns in the EC.

Let me make it clear that nobody knows this.

My twitter feed is full of ads for post-doc positions to research this very question. Whoever does figure this out will prolly score on a Nobel prize for it.

[dillon.bender]

@Bitking

That said - I picked up on that you are looking to see how the senses end up forming these patterns in the EC.

Right I think. I just had these questions after watching Matt’s demo where you can see the grid cells firing when the mouse’s location enters their firing fields. I started to think about how those modules can create the hexagonal grid pattern to encode an environment as large and diverse as the world. I guess I would just need more info about the input to EC.

[Bitking]

I just saw this paper today in my twitter feed. It does not directly answer the question of how the senses are encoded in the hippocampus but it does offer more insight into the general topic of coding and retrieval in the medial temporal area:

Reactivated spatial context guides episodic recall - Nora A. Herweg, Ashwini D, Sharan, Michael R.Sperling, Armin Brandt, Andreas Schulze-Bonhage, Michael J. Kahana

399972.full.pdf

[bkaz]

And here is another on time representation in lateral EC: https://neurosciencenews.com/time-perception-9771/

[dillon.bender]

I think I can better articulate my inquiry after thinking this over for a few days.

So, the goal of these grid cells is to uniquely represent each location in an environment by having the hexagonal lattice “cover over” all possible locations of that environment with equally spaced place fields.

Assuming that is an accurate premise, then the question is: how would I initialize these place fields for a given set of grid cells (in modules), assuming this is the very first environment to be learned, so no previous place fields exist? And then how would they grow to represent the environment as it is explored?

AFAICT, you can’t create the grid before you know the extent of the environment, so it has to start from nothing and then be learned through movement, right?

I don’t expect the answer to this to be biologically accurate really, since I highly doubt this is known. Who knows.

I figured I might as well pose this questioning here while I try to hack up my own (probably dumb) solution.

[Bitking]

We have been talking about the spatial component of memory and how that may play out in the hippocampus.
This paper adds some interesting fuel to that fire:

How is experience transformed into memory?

With code:

ContextLab/sherlock-topic-model-paper

and the dataset to feed into the process:
https://dataspace.princeton.edu/jspui/handle/88435/dsp01nz8062179

[Bitking]

We talk about place, direction, edge cells as normal spatial functions of the hippocampus/entorhinal cortex.

For a while now I have been saying that the entire personal experience is coded in this system - not just spatial dimensions. Here is how another major chunk of your experience shows up in this coding:

Interaction of taste and place coding in the hippocampus

[keaton]

Hello, I am currently trying to do research on Grid cells and am attempting to train a model with grid cell data. I was wondering if anyone knows of any grid cell data sets, specifically images of grid cells (brain scans). If so this would we really helpful as I can’t seem to find any

[Bitking]

You might find what you need here:

We have started the process of uploading raw data from studies of entorhinal cortex and related regions for open access. So far key data from Sargolini et al (2006) and Hafting et al (2008) have been uploaded but more will come. Our intention is to make all raw data from all published studies available. The data contain a lot more interesting information than what has been published and we encourage users to dig further. Please do not hesitate to contact us for updates and information (edvard.moser@ntnu.no).

https://www.ntnu.edu/kavli/research/grid-cell-data

And some background:
https://www.ntnu.edu/kavli/discovering-grid-cells