Connectionists: how the brain works? (UNCLASSIFIED)

Tue Apr 8 01:30:48 EDT 2014

Hi John,
ART evaluates distance between the contending representation and the
current input through vigilance.  If they are too far apart, a poor
vigilance signal will be triggered.
The best resonance will be achieved when they have the least amount of
distance.
If in your model, K-nearest neighbors is used without a neural equivalent,
then your model is not quite in the spirit of a connectionist model.
For example, Bayesian networks do a great job emulating brain behavior,
modeling the integration of priors. and has been invaluable to model
cognitive studies.  However they assume a statistical configuration of
connections and distributions which is not quite known how to emulate with
neurons.  Thus pure Bayesian models are also questionable in terms of
connectionist modeling.  But some connectionist models can emulate some
statistical models for example see section 2.4  in Thomas & McClelland's
chapter in Sun's 2008 book (
http://www.psyc.bbk.ac.uk/people/academic/thomas_m/TM_Cambridge_sub.pdf).
I am not suggesting Hodgkin-Huxley level detailed neuron models, however
connectionist models should have their connections explicitly defined.
Sincerely,
-Tsvi

On Mon, Apr 7, 2014 at 10:58 AM, Juyang Weng <weng at cse.msu.edu> wrote:

>  Tsvi,
>
> Note that ART uses a vigilance value to pick up the first "acceptable"
> match in its sequential bottom-up and top-down search.
> I believe that was Steve meant when he mentioned vigilance.
>
> Why do you think "ART as a neural way to implement a K-nearest neighbor
> algorithm"?
> If not all the neighbors have sequentially participated,
> how can ART find the nearest neighbor, let alone K-nearest neighbor?
>
> Our DN uses an explicit k-nearest mechanism to find the k-nearest
> neighbors in every network update,
> to avoid the problems of slow resonance in existing models of spiking
> neuronal networks.
> The explicit k-nearest mechanism itself is not meant to be biologically
> plausible,
> but it gives a computational advantage for software simulation of large
> networks
> at a speed slower than 1000 network updates per second.
>
> I guess that more detailed molecular simulations of individual neuronal
> spikes (such as using the Hodgkin-Huxley model of
> a neuron, using the NEURON software, <http://www.neuron.yale.edu/neuron/>or like the
> Blue Brain project <http://bluebrain.epfl.ch/> directed by respected Dr.
> Henry Markram)
> are very useful for showing some detailed molecular, synaptic, and
> neuronal properties.
> However, they miss necessary brain-system-level mechanisms so much that it
> is difficult for them
> to show major brain-scale functions
> (such as learning to recognize objects and detection of natural objects
> directly from natural cluttered scenes).
>
> According to my understanding, if one uses a detailed neuronal model for
> each of a variety of neuronal types and
> connects those simulated neurons of different types according to a diagram
> of Brodmann areas,
> his simulation is NOT going to lead to any major brain function.
> He still needs brain-system-level knowledge such as that taught in the BMI
> 871 course.
>
> -John
>
> On 4/7/14 8:07 AM, Tsvi Achler wrote:
>
>  Dear Steve, John
> I think such discussions are great to spark interests in feedback (output
> back to input) such models which I feel should be given much more
> attention.
> In this vein it may be better to discuss more of the details here than to
> suggest to read a reference.
>
>  Basically I see ART as a neural way to implement a K-nearest neighbor
> algorithm.  Clearly the way ART overcomes the neural hurdles is immense
> especially in figuring out how to coordinate neurons.  However it is also
> important to summarize such methods in algorithmic terms  which I attempt
> to do here (and please comment/correct).
> Instar learning is used to find the best weights for quick feedforward
> recognition without too much resonance (otherwise more resonance will be
> needed).  Outstar learning is used to find the expectation of the patterns.
>  The resonance mechanism evaluates distances between the "neighbors"
> evaluating how close differing outputs are to the input pattern (using the
> expectation).  By choosing one winner the network is equivalent to a
> 1-nearest neighbor model.  If you open it up to more winners (eg k winners)
> as you suggest  then it becomes a k-nearest neighbor mechanism.
>
>  Clearly I focused here on the main ART modules and did not discuss other
> additions.  But I want to just focus on the main idea at this point.
> Sincerely,
> -Tsvi
>
>
> On Sun, Apr 6, 2014 at 1:30 PM, Stephen Grossberg <steve at cns.bu.edu>wrote:
>
>> Dear John,
>>
>>  Thanks for your questions. I reply below.
>>
>>   On Apr 5, 2014, at 10:51 AM, Juyang Weng wrote:
>>
>>   Dear Steve,
>>
>> This is one of my long-time questions that I did not have a chance to ask
>> you when I met you many times before.
>> But they may be useful for some people on this list.
>> Please accept my apology of my question implies any false impression that
>> I did not intend.
>>
>> (1) Your statement below seems to have confirmed my understanding:
>> Your top-down process in ART in the late 1990's is basically for finding
>> an acceptable match
>> between the input feature vector and the stored feature vectors
>> represented by neurons (not meant for the nearest match).
>>
>>
>>  ART has developed a lot since the 1990s. A non-technical but fairly
>> comprehensive review article was published in 2012 in *Neural Networks*and can be found at
>> http://cns.bu.edu/~steve/ART.pdf.
>>
>>  I do not think about the top-down process in ART in quite the way that
>> you state above. My reason for this is summarized by the acronym CLEARS for
>> the processes of Consciousness, Learning, Expectation, Attention,
>> Resonance, and Synchrony. All the CLEARS processes come into this story,
>> and ART top-down mechanisms contribute to all of them. For me, the most
>> fundamental issues concern how ART dynamically self-stabilizes the memories
>> that are learned within the model's bottom-up adaptive filters and top-down
>> expectations.
>>
>>  In particular, during learning, a big enough mismatch can lead to
>> hypothesis testing and search for a new, or previously learned, category
>> that leads to an acceptable match. The criterion for what is "big enough
>> mismatch" or "acceptable match" is regulated by a vigilance parameter that
>> can itself vary in a state-dependent way.
>>
>>  After learning occurs, a bottom-up input pattern typically directly
>> selects the best-matching category, without any hypothesis testing or
>> search. And even if there is a reset due to a large initial mismatch with a
>> previously active category, a single reset event may lead directly to a
>> matching category that can directly resonate with the data.
>>
>>  I should note that all of the foundational predictions of ART now have
>> substantial bodies of psychological and neurobiological data to support
>> them. See the review article if you would like to read about them.
>>
>>   The currently active neuron is the one being examined by the top down
>> process
>>
>>
>>  I'm not sure what you mean by "being examined", but perhaps my comment
>> above may deal with it.
>>
>>  I should comment, though, about your use of the word "currently active
>> neuron". I assume that you mean at the category level.
>>
>>  In this regard, there are two ART's. The first aspect of ART is as a
>> cognitive and neural theory whose scope, which includes perceptual,
>> cognitive, and adaptively timed cognitive-emotional dynamics, among other
>> processes, is illustrated by the above referenced 2012 review article in *Neural
>> Networks*. In the biological theory, there is no general commitment to
>> just one "currently active neuron". One always considers the neuronal
>> population, or populations, that represent a learned category. Sometimes,
>> but not always, a winner-take-all category is chosen.
>>
>>  The 2012 review article illustrates some of the large data bases of
>> psychological and neurobiological data that have been explained in a
>> principled way, quantitatively simulated, and successfully predicted by ART
>> over a period of decades. ART-like processing is, however, certainly not
>> the only kind of computation that may be needed to understand how the brain
>> works. The paradigm called Complementary Computing that I introduced awhile
>> ago makes precise the sense in which ART may be just one kind of dynamics
>> supported by advanced brains. This is also summarized in the review article.
>>
>>  The second aspect of ART is as a series of algorithms that
>> mathematically characterize key ART design principles and mechanisms in a
>> focused setting, and provide algorithms for large-scale applications in
>> engineering and technology. ARTMAP, fuzzy ARTMAP, and distributed ARTMAP
>> are among these, all of them developed with Gail Carpenter. Some of these
>> algorithms use winner-take-all categories to enable the proof of
>> mathematical theorems that characterize how underlying design principles
>> work. In contrast, the distributed ARTMAP family of algorithms, developed
>> by Gail Carpenter and her colleagues, allows for distributed category
>> representations without losing the benefits of fast, incremental,
>> self-stabilizing learning and prediction in response to a large
>> non-stationary databases that can include many unexpected events.
>>
>>  See, e.g.,
>> http://techlab.bu.edu/members/gail/articles/115_dART_NN_1997_.pdf and
>> http://techlab.bu.edu/members/gail/articles/155_Fusion2008_CarpenterRavindran.pdf
>> .
>>
>>  I should note that FAST learning is a technical concept: it means that
>> each adaptive weight can converge to its new equilibrium value on EACH
>> learning trial. That is why ART algorithms can often successfully carry out
>> one-trial incremental learning of a data base. This is not true of many
>> other algorithms, such as back propagation, simulated annealing, and the
>> like, which all experience catastrophic forgetting if they try to do fast
>> learning. Almost all other learning algorithms need to be run using slow
>> learning, that allows only a small increment in the values of adaptive
>> weights on each learning trial, to avoid massive memory instabilities, and
>> work best in response to stationary data. Such algorithms often fail to
>> detect important rare cases, among other limitations. ART can provably
>> learn in either the fast or slow mode in response to non-stationary data.
>>
>>   in a sequential fashion: one neuron after another, until an acceptable
>> neuron is found.
>>
>> (2) The input to the ART in the late 1990's is for a single feature
>> vector as a monolithic input.
>> By monolithic, I mean that all neurons take the entire input feature
>> vector as input.
>> I raise this point here because neuron in ART in the late 1990's does not
>> have an explicit local sensory receptive field (SRF),
>> i.e., are fully connected from all components of the input vector.   A
>> local SRF means that each neuron is only connected to a small region
>> in an input image.
>>
>>
>>  Various ART algorithms for technology do use fully connected networks.
>> They represent a single-channel case, which is often sufficient in
>> applications and which simplifies mathematical proofs. However, the
>> single-channel case is, as its name suggests, not a necessary constraint on
>> ART design.
>>
>>  In addition, many ART biological models do not restrict themselves to
>> the single-channel case, and do have receptive fields. These include the
>> LAMINART family of models that predict functional roles for many identified
>> cell types in the laminar circuits of cerebral cortex. These models
>> illustrate how variations of a shared laminar circuit design can carry out
>> very different intelligent functions, such as 3D vision (e.g., 3D
>> LAMINART), speech and language (e.g., cARTWORD), and cognitive information
>> processing (e.g., LIST PARSE). They are all summarized in the 2012 review
>> article, with the archival articles themselves on my web page
>> http://cns.bu.edu/~steve.
>>
>>  The existence of these laminar variations-on-a-theme provides an
>> existence proof for the exciting goal of designing a family of chips whose
>> specializations can realize all aspects of higher intelligence, and which
>> can be consistently connected because they all share a similar underlying
>> design. Work on achieving this goal can productively occupy lots of
>> creative modelers and technologists for many years to come.
>>
>>  I hope that the above replies provide some relevant information, as
>> well as pointers for finding more.
>>
>>  Best,
>>
>>  Steve
>>
>>
>>
>>
>> My apology again if my understanding above has errors although I have
>> examined the above two points carefully
>> through multiple your papers.
>>
>> Best regards,
>>
>> -John
>>
>>  Juyang (John) Weng, Professor
>> Department of Computer Science and Engineering
>> MSU Cognitive Science Program and MSU Neuroscience Program
>> 428 S Shaw Ln Rm 3115
>> Michigan State University
>> East Lansing, MI 48824 USA
>> Tel: 517-353-4388
>> Fax: 517-432-1061
>> Email: weng at cse.msu.edu
>> URL: http://www.cse.msu.edu/~weng/
>> ----------------------------------------------
>>
>>
>>
>>      Stephen Grossberg
>> Wang Professor of Cognitive and Neural Systems
>> Professor of Mathematics, Psychology, and Biomedical Engineering
>>  Director, Center for Adaptive Systems
>> http://www.cns.bu.edu/about/cas.html
>>  http://cns.bu.edu/~steve
>> steve at bu.edu
>>
>>
>>
>>
>>
>
> --
> --
> Juyang (John) Weng, Professor
> Department of Computer Science and Engineering
> MSU Cognitive Science Program and MSU Neuroscience Program
> 428 S Shaw Ln Rm 3115
> Michigan State University
> East Lansing, MI 48824 USA
> Tel: 517-353-4388
>
> Fax: 517-432-1061
> Email: weng at cse.msu.edu
> URL: http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20140407/557ed5db/attachment.html>