Re: face-selective region

"feedbackdroids" <feedbackdroids@xxxxxxxxx> wrote:
Curt Welch wrote:
"feedbackdroids" <feedbackdroids@xxxxxxxxx> wrote:
ref Science mag, issue of 6 Feb 2006, "A cortical region consisting
entirely of face-selective cells" ... joint Bremen-Harvard study ...
high-points ...

- area in macaque cortex initially id'ed by fMRI.

- 97% of visually responsive cells in area were strongly face
selective, 302 of 310 cells in single unit recordings. Cells showed
weaker responses other roundish stimuli.

- supports modular architecture of brain operation.

Jeff Hawkins' book talks about this same "face-selective" region of the
cortex as well.

I looked in Hawkins' book again. He talks about the IT region regards
faces in general, but no specific reference given. Weakest point of the
book is so few references, but then his purpose is to develop a general
framework for an intelligent system, not to do a broad review of
relevant neuroscience.

What was important about the Science article cited above is that they
mention the existence of "several" cortical regions with face-selective
cells, located around the frontal and temporal regions, but that, the
particular region studied was unique in having essentially only face
cells. Notice the word "... entirely ..." in the title of the paper.
So, the actual cortex is a lot more complex than Hawkins' simplified
model, but we should expect that.

Well, yes, we should expect his simplified model to be too simplified for a
full and complete description of the brain (he even says so in the book),
but the evidence you mention above fits exactly with his predictions so I
don't really know why you think it's an indication of addition complexity.

I imagine the summary is that some cortical regions have very-specific
functions, while in others you find more heterogeneity, with cell
responsiveness covering a wider range of stimuli. This should be a good
approach where you need to do 2 things: (a) compute specific functions
or operations, and (b) combine together the results of many such
operations.

All Jeff's model seems to say (as I read it) is that the entire cortex is a
invariant pattern recognizer. This means that every micro column in the
cortex (both motor and sensory cortex) will become active and stay active
whenever the external condition it's responsible for recognizing is
present. His model makes little to know predictions about how those
pattern recognizers will group together to form similar cortical regions -
other than that similar patterns are more likely to be located close
together in the cortex. All this means is that 1) there should be face
recognizing columns in the cortex and 2) you would expect to find face
recognizing columns close to other face columns.

I actually agree with most of Jeff's ideas, except he seems to have missed
what I see as the most important half of the problem. His ideas seem to
align with my old INET concept - an unsupervised learning system for
identifying the current state of the environment. But he makes no mention
of what was the ONET side of my approach - which is the part which takes
the environment state as identified by the INET, and maps it to actual
behavior, using a reinforcement learning algorithm. As far as I can tell,
his theory fails to answer the single most important and most fundamental
AI problem - how does the brain decide what behavior to produce.

And I've now moved on to a solving both problems in each node instead of
having to use two networks to do it. My pulse sorting net attempts to
solve both sides of the problem at once.

His predictive memory approach explains in fairly detailed speculation, how
the brain "knows" what is going on in the environment and how it "knows"
how the world works, but totally fails to say anything about how the brain
uses that knowledge to make behavior decisions. So, for example, his
theory might explain how a mouse or a human, would recognize part of a
hallway and know before turning the corner, that the cheese is probably
right around the corner, but no where does he seem to answer why the brain
would try to get the cheese, instead of trying to run away from it. Or,
even if it for some reason "wanted" the cheese, now the brain would manage
to learn the correct sequence of outputs to get the cheese into it's
stomach. There's where you need a reinforcement learning system.

I've not finished the book so maybe he talks about this later but I seem to
already be past his technical description of his theory.

So, it looks like he's on what I see as the right track, but has only so
far, addressed half of the problem.

And of course, what I like about his theory is that he, like I, believe
there is some fundamental learning algorithm at work for the entire cortex,
and that specialized sections of the cortex form more because of what data
is sent to it, then the way the DNA built it. The DNA just decides what
data gets sent to which part of the cortex, (i.e., where each sensory
signal is connected to the cortex and maybe how different sections of the
cortex are bulk-wired to other sections of the cortex) then the function
formed in each section is a result of the characteristics of the data that
shows up there. So the DNA defines the network topology, and the generic
learning hardware which is the function of the cortex, auto configures
itself to understand the type of data it is dealing with.

--
Curt Welch http://CurtWelch.Com/
curt@xxxxxxxx http://NewsReader.Com/
.

Relevant Pages

  • Re: face-selective region
    ... entirely of face-selective cells" ... ... area in macaque cortex initially id'ed by fMRI. ... weaker responses other roundish stimuli. ... I looked in Hawkins' book again. ...
    (comp.ai.philosophy)
  • Re: face-selective region
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  • Re: face-selective region
    ... cortex will become active and stay active ... using a reinforcement learning algorithm. ... AI problem - how does the brain decide what behavior to produce. ... Rememer H had the hippocampus at the "top" of his hierarchy, ...
    (comp.ai.philosophy)