Quote:Original post by Kring
This information processing model was pretty much discovered by Hawkins, and only became general knowledge after the publication of his book in 2004.

If you're referring specifically to HTM, then you are, of course, correct. If you're referring to heirarchical decomposition and bidirectional parameter adjustment for classification (as I was), then I've definitely seen it before. Specifically, in heirarchical Markov field models for clustering and classification (with some application to MRI), as well as heirarchical Bayesian models for modelling of the cortex from MRI images). I'll grant that the application of an architecture such as this to time dependent classification is probably novel.

Quote:Original post by Timkin
If you're referring specifically to HTM, then you are, of course, correct. If you're referring to heirarchical decomposition and bidirectional parameter adjustment for classification (as I was), then I've definitely seen it before. Specifically, in heirarchical Markov field models for clustering and classification (with some application to MRI), as well as heirarchical Bayesian models for modelling of the cortex from MRI images). I'll grant that the application of an architecture such as this to time dependent classification is probably novel.

Yes, Hawkins did say something similar in a recent article I read.

This is interesting.

It really makes me wonder how the hell human beings are so good at it. What exactly allows me to make identifications of objects within pictures so easily and accurately? What are the heuristics I use to do that? It's so automatic that I can't even break it down!

Quote:Original post by Kevinator
It really makes me wonder how the hell human beings are so good at it. What exactly allows me to make identifications of objects within pictures so easily and accurately? What are the heuristics I use to do that?

One popular theory as to how the brain performs recognition/understanding is that it proposes many possible, closely related explanations of what it 'sees' (and here I use 'see' to mean all sensory information). These are then tested against subsequent information and the poorly performing hypotheses are quickly culled until (hopefully) only one hypothesis remains, which is accepted as truth. This processing happens on timescales of the order of milliseconds, but can last longer. We know that in the visual system, for example, detecting certain information in our environment leads to set sequences of eye movements to vary the information we gather (which we don't even realise happens as our brain hides the variation in the images we see).

In certain cortical regions the hypotheses are seen to be recorded as oscillations of given frequencies of spike trains in closed neuronal loops, with variations on represented by a narrow band of alternate frequency signals. The information that combines in these signals is feature information from the sensory space; for example, texture, lines (and their orientation), shadow, movement velocity, etc. The hypotheses are ways of combining this information into a plausible description of the environment, grounded in what we have already observed. If we've never seen it before and it is difficult to cull the hypotheses, we can have difficulty working out what it is we are seeing. There are classic visual illusions used in psychology to assess this indecisive behaviour of the brain with regards to vision and one could reasonably assume such illusions could be created for our other sensory systems.

A good example of this kind of recognition occurs in the olfactory system, which was shown (back in the mid '80s iirc) to encode smell information as low dimensional attractors in the phase space of certain neuronal clusters. If you recognised a smell/taste the oscillations in the olfactory neurons would settle onto a unique attractor. If you didn't, after repeated exposure (which equates to reinforcement of the stimulus) you learned a new attractor and hence a new smell/flavour.


I could go on about this stuff all day, but I won't. If you're interested, there is heaps of information out there in both lay and professional publications. It makes for great Sunday afternoon reading! ;)

Cheers,

Timkin