If you take a matrix of covariance or similarity between neurons based on firing pattern, and try to reduce it to the sum of a weighted set of vectors, the number of vectors you would need to accurately model the system gives you the dimensionality of the space.
This isn’t about the 3 dimensional structure the neurons occupy, but about their operational degrees of freedom.
Think about how a CNC machine works, you can have CNC with more than 3 axis, for example a 4 axis CNC machine can move left/right up/down backwards/forwards and also have another axis which can rotate in a given plane.
From a more mathematical perspective just think about the number of parameters in a system (excluding reduction) each parameter would be a dimension.
Appreciate the attempt, but in this example the 4th axis is not independent since the motion along that axis can be achieved, with some complexity, by the motion along the other axes. Granted this is not very useful for a machinist because it will be very tedious to machine a part this way compared to the dedicated 4th rotating axis, but mathematically it is redundant.
I have found it easiest to think of a logical dimensions or configurations when thinking of higher dimensions. Physically it can be a row of bulbs (lighted or not) wherein N bulbs (dimensions) can represent 2^n states in total. The 2 here can be increased by having bulbs that can light up in many colours.
I roughly understand what the article says about dimensional space (Reading higher mathematics books on the way to my meagre college course way back when, helps me a little, even if it is all half-remembered and a bit wrong -- this understanding is sufficient enough to satisfy me), however the poster above me doesn't, and clearly asked for a definition a 5 year old layman could understand.
The comment I am replying to, your comment in the tree, and the one next to you, does not seem to match that request in any sense.
Now, simplified definitions are an art, but Feynman managed it with Quantum Electrodynamics -- so it is not impossible to do it for complex subjects. And it seems to me the less you understand a subject, the less simple and more confusing your explanation will be, such as the explanations given by the other posters here. (fyi: I do not understand enough to properly convey my understanding clearly -- which is why I have not attempted to do so)
This isn't a matter of having an incomplete understanding, thanks for the offhanded aspersion though. The fundamental problem is that the concept of manifolds in state space isn't really something that has a non-tortured real world analogy, which is a prerequisite for a five year old to understand. It's probably possible to express more simply with a video demonstrating the covariance structure of a data set visually, then showing how that results from a small set of vectors, but I've read enough textbooks to be confident that a simple, concise explanation eludes words.
