This is also from PG on the
cortical mapping of sensory axes:
PG: A first thing to note is
that the cortex abounds in topographic maps, whereby neighborhood
relations at the sensory periphery are preserved in the arrangement of
neurons in various “deeper” CNS regions.
For example, one finds
“retinotopic” maps in the lateral geniculate nuclei which are
arranged in six layers, each layer arranged in a topographic representation of
the retina;
there are
“somatotopic” maps representing sensory positions on the body;
and there are
“tonotopic” maps where the orderly mapping of neurons with sound frequencies
is preserved from the cochlea to diverse areas of the auditory cortex.
Another interesting aspect of
these maps is that most of them preserve the modularity of the
senses, in the way that distinct types of receptor neurons are sensitive to
different features of our environment and these features are kept distinct in
the maps higher up in the projection system. On this point, Stein and Meredith
write:
“At each successive
level in the central nervous system the visual, somatosensory, and
auditory representations occupy spatially distinct regions that
are defined functionally and anatomically (i.e., cytoarchitectonically). At the
cortical level, and in most regions of the thalamus, the domain of an
individual sensory modality consists of distinct maps. The map (or
maps) of a single sensory modality in, for example, primary
sensory cortex is distinguished from the map in extraprimary cortex it
abuts by mirror-image reversals in receptive field progressions,
significant changes in
receptive field properties, differences in afferent/efferent
organization, and/or by specialization for different submodality
characteristics.
In cortex the interposition of
“association” areas further segregates
the representations of the different sensory modalities.”
Further support for my comparison can be gained from Gallistel, who in his
excellent book on learning mechanisms in biological systems, devotes an entire
chapter to “Vector spaces in the nervous system.” He writes:
“The
purpose of this chapter is to review neurophysiological data supporting the
hypothesis that the nervous system does in fact quite generally employ vectors
to represent properties of both proximal and distal stimuli. The values of
these representational vectors are physically expressed by the locations of
neural activity in anatomical spaces of whose dimensions correspond to descriptive
dimensions of the stimulus. The term vector space, which refers to the space
defined by a system of coordinates, has a surprisingly literal interpretation
in the nervous system. The functional architecture of many structures that process
higher-level sensory inputs is such that anatomical dimensions of the structure
correspond to descriptive dimensions of the stimulus. There is reason to
think that this correspondence is not fortuitous; rather, it is a foundation
for the nervous system’s capacity to adapt its output to the structure of
the world that generates its inputs.”
So, as is well known, different
cortices are concerned with different sensory capabilities. But the
mapping of sensory data to analogous points on the related sensory cortex,
independently of any thought process, must be considered in designing
agents.
Sincerely,
Rich Cooper,
Rich Cooper,
Chief Technology Officer,
MetaSemantics Corporation
MetaSemantics AT
EnglishLogicKernel DOT com
( 9 4 9 ) 5 2 5-5 7 1 2
http://www.EnglishLogicKernel.com
From:
ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of Rich Cooper
Sent: Wednesday, May 27, 2015 12:52 PM
To: '[ontolog-forum] '
Subject: Re: [ontolog-forum] Fruit fly emotions mimic human emotions -
ontology discovery possible?
Here is another spiffy quote from PG:
PG: Conceptual spaces can also
provide a better way of representing learning in general and concept formation
in particular than what can be achieved on the symbolic level.
For Gaerdenfor, the phrase "conceptual spaces"
refers to "intrinsic" senses scattered across an array of (cortical
columns)", if I may so freely interpret his intent against his
terminology. He cites examples like the Cochlea's scattering by frequency
of harmonics, visual color limits to RGB cones and rods, muscular extension,
and so forth, each representing a scattering dimension. He uses this finiteness
of scattering along the neocortex (again I assisted his interpretation) to
conclude that induction over domains of infinities is biologically
illegitimate:
Many of the problems of
induction that are created by the symbolic approach dissolve into thin air when
analysed on the conceptual level. Similarly, the problem of how transducers
work becomes a non-problem since no transducers are needed for the information
represented in conceptual spaces.
RC: He seems to say that specialized processing in
neural compartments (noncortical brain areas) is very limited, so the
dimensions are scattered across dimensions of each of the cortices, and the
brain does the rest. I suppose that is partly the connectionist image.
But it also shows that the designation of any symbol used
by the patient is localized by name or description in the appropriate cortical
column(s) so it can be referenced linguistically.
PG: The theory of conceptual
spaces may also indicate a direction where a solution to the frame problem can
be ferreted out. The starting point is to separate the information to be
represented into domains. The combinatorial explosion of symbolic
representations of a changing world is a result of not keeping symbolic
information about different domains separated.
RC: There is some truth to that, but also lots of work
left to do being stated. Basically, database models use the primitive
domains (integer, real, Boolean, char, string, ...) and don't build more
object-oriented domains that might be more intuitively understandable by the
salient crowd.
But to do so leaves you with only a snapshot of the data
model at that point in time, while the actual data model varies with new or
updated requirements changes.
Since the need for those more detailed views of the
database are merely conceptual, it doesn't help the bankers of the system
justify funding such work. So that level of detail is usually not
completed.
So, if we (Tonto) can map the fruit fly sensor qualities
and their neurons onto their cortical locations (assuming they have cortices
big enough to do so), perhaps we can even relate that to some primitive
functionality in the brain which corresponds to humans. If so, then we
(Tonto) have that fly model of related phenomenon to work on.
Sincerely,
Rich
Cooper,
Rich Cooper,
Chief Technology Officer,
MetaSemantics Corporation
MetaSemantics AT EnglishLogicKernel DOT com
( 9 4 9 ) 5 2 5-5 7 1 2
http://www.EnglishLogicKernel.com
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Monday, May 25, 2015 1:30 PM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Fruit fly emotions mimic human emotions - ontology
discovery possible?
Tom,
I am well aware of those debates and of the intensity on
all sides:
> This is the “vs.” I am referring to, and
in spite of your “should”,
> the facts on the current ground is that there is
this debate. Indeed,
> the article by Fodor and Lepore and the reply by
David Chalmers, both
> of which you recently provided links to, make it
quite clear how
> intense the “vs.” remains.
The most cutthroat debates are among philosophers and theologians
-- primarily because they're searching for certainty, and they have no way of
knowing when they're wrong.
That was the point of my talk at the Mexican AI
conference in November:
http://www.jfsowa.com/talks/micai.pdf
Why has AI failed? And how can
it succeed?
That wrangling led to single-paradigm systems, which are
very strong on one type of problem and useless for anything else.
> Here's Gardenfors, on my "vs.":
> “Within cognitive science, there are currently
two dominating
> approaches to the problem of modeling
representations.” From the point
> of view of the symbolic approach (which I and others
call the “mental
> representation” approach), “cognition is
seen as essentially being
> computation, involving symbol manipulation.”
I presented a guest lecture at Lund at PG's invitation,
so I won't be too harsh on him. Peter did good work on belief revision,
which I strongly recommend. He's the G of the AGM axioms. But that
quotation is an extremely oversimplified and misleading summary of AI and
cognitive science.
Marvin Minsky's _Society of Mind_ is a good antidote to
that kind of partisanship. See the reference in Slide 13 of micai.pdf:
http://web.media.mit.edu/~push/CognitiveDiversity.pdf
That was a strong influence on my "Flexible Modular
System" (FMF):
http://www.jfsowa.com/pubs/arch.pdf
A quotation from arch.pdf
> The lack of progress in building general-purpose
intelligent systems
> could be explained by several different hypotheses:
>
> * Simulating human intelligence on a digital
computer is impossible.
>
> * The ideal architecture for true AI has not
yet been found.
>
> * Human intelligence is so flexible that no
fixed architecture can do
> more than simulate a single aspect
of what is humanly possible.
>
> Many people have presented strong, but not
completely convincing
> arguments for the first hypothesis. In the
search for an ideal
> architecture, others have implemented a variety of
at best partially
> successful designs. The purpose of this paper is to
explore the third
> hypothesis: propose a flexible modular
framework that can be tailored
> to an open-ended variety of architectures for
different kinds of
> applications.
For examples that show how the FMF works, see "Two
paradigms are better than one, and multiple paradigms are even better":
http://www.jfsowa.com/pubs/paradigm.pdf
Fundamental principle: Neuroscientists are the
first to emphasize that
*nobody* really knows how the brain works. For
philosophers to engage in endless wrangling about the virtues of one half-baked
theory or another is fundamentally misguided.
Recommendation in micai.pdf: Implement various
theories. Test them alone and in different combinations. See what
works. Collaborate!
John
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