Comment by doodlebugging | Hacker News Reader

Comment by doodlebugging | Hacker News Reader

After reading this article I have decided that the researchers earlier conclusions about how the rats keep track of their environment is suffering from a serious defect introduced by the researchers at the start.

Their earlier work involved studying rat and bat navigation in unnatural environments - a "2D" space which was really a 3D space constrained to be along a single level. This forced or even allowed the rat brains to dumb things down to a situation where they had no need or opportunity to consider things outside that unnaturally limited space and so their brains naturally optimized for that simplistic situation.

You have dumbed down their environment to the point where you have established a minimal ability that they need to master in order to function and navigate that environment.

Then when you discover that their memory encodes navigational information from this dumbed-down, unnatural environment in a regularized grid, this should be no surprise. They are minimizing the energy required to thrive in that environment.

Once you allow them to navigate in a more natural environment you should expect to see this regular grid disappear since their options for reaching a destination are no longer constrained to a single path along most of the route to their destination. Their brains will have to incorporate many more clues to guide their route selection and those clues could be time-varying as in the case where they are tracking the source of an odor as they search for food so they will need to monitor air flow and direction, odor intensity, potential obstacles between themselves and the source, alternate routes which present themselves as they move through space, threat detection from predators or bait traps, etc. In short, there so many variables that will need to be evaluated once you remove the flat plane constraints that led to their grid discovery that it should be no surprise to discover that a more complex environment uses different optimizations, some of which are encoded in a regularized notation and others, probably situational events on the path-picking decision tree, are more random.

I think it's important to see all of these situations. The very fact that the brain optimizes its maps based on constrained environments is awesome. Also, the technology had to be built to do the previous 2d studies, which then bootstrapped the 3d studies. Knowing that under certain circumstances the neural maps had a very "clean architecture" may have saved a lot of time and effort once these more random 3d structures were being analyzed.

I agree. This is awesome work and is a great starting point. The regular gridding to me indicates that the rats learned the routes and found them easy and maybe even boring so after a point, they were not challenged intellectually in the navigation. Once their environment was modified, the rats began to engage more of their brain to learn and manage the choices that each situation required.

I just thought it was funny that the researchers were surprised to see such a large difference in how rats handle complexity when we know going in that rats are pretty intelligent. Those rats probably know each researcher, have favorites, remember routes, and get bored with those tasks like we would be. It becomes muscle memory for them so they go through the motions to get the treat they know is waiting.

> you should expect to see this regular grid disappear since their options for reaching a destination are no longer constrained to a single path along most of the route to their destination.

That's a post hoc rationalization. All those complicating factors can exist in 2D too. Are you predicting that the 2D hexagonal structure will also be lost in a 2D environment with odors/etc., and this conclusion about it being 2D vs 3D is false?

>That's a post hoc rationalization.

Very true. Everything I typed is in fact a post hoc rationalization. Sadly enough, I had to look that up since Latin is not my first language. LOL. Have a chuckle at my expense. I promise I don't mind at all.

> Are you predicting that the 2D hexagonal structure will also be lost in a 2D environment with odors/etc., and this conclusion about it being 2D vs 3D is false?

I'm no rat expert and I don't play one at my desk.

I am saying that the discovery that navigating in a true 3D situation, a much more complex situation than navigation of a planar structure with a floor and ceiling and no opportunity to take the stairs to the next level, involves an order of magnitude more complexity and that as a result they should not have expected to see the grid structure preserved and echoed in the brain as a spherical 3D hexagonal grid. There were indications that the grid stored contextual information along with positional information and that changing one of those things warped the grid in ways they didn't understand (2D case).

This in itself should've been a clue that adding another dimension or degree of freedom of movement could have its own very different footprint.

Ruthless problem reduction and minimization is good experimental design. "Let's, like, let all the variables vary, man" is a recipe for failing to learn anything at all, rather than learning one small piece of a bigger puzzle.

It is a lot like programming. Start off defining the algorithm to handle the general case and refine it until it works with no issues. Then add in edge cases so that you tune it to handle more real-world situations. That is the way to build a solid, fail-safe code base. If you truly understand the problem you can eventually code enough to handle any real-world scenario. Don't be surprised if the code looks hairy and nothing like the original though.

In their case, they started with a generalized "2D" simple case and concluded after study that a real-world scenario would look very similar or would be a neat permutation of their original grid-optimized discovery. They found instead that they had discovered that the rat brains optimized their navigation to the difficulty of the task and that once you add enough difficulty, the brains spread the computational resources in a different optimization in order to handle the newfound complexity. Patterns were still detectable but they did not follow the original hexagonal grid optimization discovered in the initial tests.

This is, as you say, a process of learning one small piece of a bigger puzzle. In this case, they found that the complexity of the puzzle is higher than the initial guess they made after collating and analyzing all the data from the simple experiments. Useful things were learned but I was attempting to note that they have apparently made assumptions based on a very simple case that turned out not to be entirely accurate and that they appear to be surprised by that instead of accepting that the simplicity of the tasks used to generate the data may have biased the results. No doubt they will continue to add complexity and learn new things, and win more awards for their pioneering work.

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