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    Tavi:<br>
    <br>
    Let me explain a little more detail:<br>
    <br>
    There are two large categories of biological neurons, excitatory and
    inhibitory.   Both are developed through mainly signal statistics, <br>
    not specified primarily by the genomes.   Not all people agree with
    my this point, but please tolerate my this view for now.   <br>
    I gave a more detailed discussion on this view in my NAI book. <br>
    <br>
    The main effect of inhibitory connections is to reduce the number of
    firing neurons (David Field called it sparse coding), with the help
    of <br>
    excitatory connections.  This sparse coding is important because
    those do not fire are long term memory of the area at this point of
    time.<br>
    My this view is different from David Field.  He wrote that sparse
    coding is for the current representations.  I think sparse coding is
    <br>
    necessary for long-term memory. Not all people agree with my this
    point, but please tolerate my this view for now.   <br>
    <br>
    However, this reduction requires very fast parallel neuronal updates
    to avoid uncontrollable large-magnitude oscillations.  <br>
    Even with the fast biological parallel neuronal updates, we still
    see slow but small-magnitude oscillations such as the <br>
    well-known theta waves and alpha waves.   My view is that such slow
    but small-magnitude oscillations are side effects of <br>
    excitatory and inhibitory connections that form many loops, not
    something really desirable for the brain operation (sorry, <br>
    Paul Werbos).  Not all people agree with my this point, but please
    tolerate my this view for now. <br>
    <br>
    Therefore, as far as I understand, all computer simulations for
    spiking neurons are not showing major brain functions<br>
    because they have to deal with the slow oscillations that are very
    different from the brain's, e.g., as Dr. Henry Markram reported<br>
    (40Hz?). <br>
    <br>
    The above discussion again shows the power and necessity of an
    overarching brain theory like that in my NAI book.  <br>
    Those who only simulate biological neurons using superficial
    biological phenomena are not going to demonstrate <br>
    any major brain functions.  They can talk about signal statistics
    from their simulations, but signal statistics are far from brain
    functions. <br>
    <br>
    -John<br>
    <br>
    <div class="moz-cite-prefix">On 4/8/14 1:30 AM, Tsvi Achler wrote:<br>
    </div>
    <blockquote
cite="mid:CANdH7hnH7S0k1Nk3essSy9xmk5Qv9V8+zK2HsBu9FJVbbMKzQA@mail.gmail.com"
      type="cite">
      <div dir="ltr">Hi John,
        <div>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.</div>
        <div>The best resonance will be achieved when they have the
          least amount of distance.</div>
        <div>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.</div>
        <div>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 (<a moz-do-not-send="true"
href="http://www.psyc.bbk.ac.uk/people/academic/thomas_m/TM_Cambridge_sub.pdf">http://www.psyc.bbk.ac.uk/people/academic/thomas_m/TM_Cambridge_sub.pdf</a>).</div>
        <div>I am not suggesting <span
style="color:rgb(0,0,0);font-family:arial,sans-serif;font-size:13.333333969116211px">Hodgkin-Huxley</span> level
          detailed neuron models, however connectionist models should
          have their connections explicitly defined. </div>
        <div>Sincerely,</div>
        <div>-Tsvi</div>
        <div><br>
        </div>
      </div>
      <div class="gmail_extra"><br>
        <br>
        <div class="gmail_quote">On Mon, Apr 7, 2014 at 10:58 AM, Juyang
          Weng <span dir="ltr"><<a moz-do-not-send="true"
              href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>></span>
          wrote:<br>
          <blockquote class="gmail_quote" style="margin:0 0 0
            .8ex;border-left:1px #ccc solid;padding-left:1ex">
            <div bgcolor="#FFFFFF" text="#000000"> Tsvi,<br>
              <br>
              Note that ART uses a vigilance value to pick up the first
              "acceptable" match in its sequential bottom-up and
              top-down search.<br>
              I believe that was Steve meant when he mentioned
              vigilance.    <br>
              <br>
              Why do you think "ART as a neural way to implement a
              K-nearest neighbor algorithm"?  <br>
              If not all the neighbors have sequentially participated,<br>
              how can ART find the nearest neighbor, let alone K-nearest
              neighbor?<br>
              <br>
              Our DN uses an explicit k-nearest mechanism to find the
              k-nearest neighbors in every network update, <br>
              to avoid the problems of slow resonance in existing models
              of spiking neuronal networks.   <br>
              The explicit k-nearest mechanism itself is not meant to be
              biologically plausible, <br>
              but it gives a computational advantage for software
              simulation of large networks <br>
              at a speed slower than 1000 network updates per second.<br>
              <br>
              I guess that more detailed molecular simulations of
              individual neuronal spikes (such as using the
              Hodgkin-Huxley model of<br>
              a neuron, using the <a moz-do-not-send="true"
                href="http://www.neuron.yale.edu/neuron/"
                target="_blank">NEURON software,</a> or like <a
                moz-do-not-send="true" href="http://bluebrain.epfl.ch/"
                target="_blank">the Blue Brain project</a> directed by
              respected Dr. Henry Markram) <br>
              are very useful for showing some detailed molecular,
              synaptic, and neuronal properties.<br>
              However, they miss necessary brain-system-level mechanisms
              so much that it is difficult for them <br>
              to show major brain-scale functions <br>
              (such as learning to recognize objects and detection of
              natural objects directly from natural cluttered scenes). <br>
              <br>
              According to my understanding, if one uses a detailed
              neuronal model for each of a variety of neuronal types and<br>
              connects those simulated neurons of different types
              according to a diagram of Brodmann areas, <br>
              his simulation is NOT going to lead to any major brain
              function.  <br>
              He still needs brain-system-level knowledge such as that
              taught in the BMI 871 course. <br>
              <br>
              -John <br>
              <div>
                <div class="h5"> <br>
                  <div>On 4/7/14 8:07 AM, Tsvi Achler wrote:<br>
                  </div>
                  <blockquote type="cite">
                    <div dir="ltr">
                      <div>Dear Steve, John</div>
                      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.
                      <div>In this vein it may be better to discuss more
                        of the details here than to suggest to read a
                        reference.</div>
                      <div><br>
                      </div>
                      <div>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).</div>
                      <div>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.</div>
                      <div><br>
                      </div>
                      <div>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.</div>
                      <div>Sincerely,</div>
                      <div>-Tsvi</div>
                    </div>
                    <div class="gmail_extra"> <br>
                      <br>
                      <div class="gmail_quote">On Sun, Apr 6, 2014 at
                        1:30 PM, Stephen Grossberg <span dir="ltr"><<a
                            moz-do-not-send="true"
                            href="mailto:steve@cns.bu.edu"
                            target="_blank">steve@cns.bu.edu</a>></span>
                        wrote:<br>
                        <blockquote class="gmail_quote" style="margin:0
                          0 0 .8ex;border-left:1px #ccc
                          solid;padding-left:1ex">
                          <div style="word-wrap:break-word"><font
                              face="Arial" size="5">Dear John,</font>
                            <div><font face="Arial" size="5"><br>
                              </font></div>
                            <div><font face="Arial" size="5">Thanks for
                                your questions. I reply below.</font></div>
                            <div> <font face="Arial" size="5"><br>
                              </font>
                              <div>
                                <div>
                                  <div><font face="Arial" size="5">On
                                      Apr 5, 2014, at 10:51 AM, Juyang
                                      Weng wrote:</font></div>
                                  <font face="Arial" size="5"><br>
                                  </font>
                                  <blockquote type="cite">
                                    <div bgcolor="#FFFFFF"
                                      text="#000000"><font face="Arial"
                                        size="5"> Dear Steve,<br>
                                        <br>
                                        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. <br>
                                        But they may be useful for some
                                        people on this list.   <br>
                                        Please accept my apology of my
                                        question implies any false
                                        impression that I did not
                                        intend.<br>
                                        <br>
                                        (1) Your statement below seems
                                        to have confirmed my
                                        understanding:  <br>
                                        Your top-down process in ART in
                                        the late 1990's is basically for
                                        finding an acceptable match <br>
                                        between the input feature vector
                                        and the stored feature vectors
                                        represented by neurons (not
                                        meant for the nearest match). <br>
                                      </font></div>
                                  </blockquote>
                                  <div><font face="Arial" size="5"><br>
                                    </font></div>
                                </div>
                                <font face="Arial" size="5">ART has
                                  developed a lot since the 1990s. A
                                  non-technical but fairly comprehensive
                                  review article was published in 2012
                                  in <i>Neural Networks</i> and can be
                                  found at <a moz-do-not-send="true"
                                    href="http://cns.bu.edu/%7Esteve/ART.pdf"
                                    target="_blank">http://cns.bu.edu/~steve/ART.pdf</a>.</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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. </font><span
style="font-family:Arial;font-size:x-large">All the CLEARS processes
                                  come into this story, and </span><span
style="font-family:Arial;font-size:x-large">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. </span></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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.</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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. </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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.</font></div>
                              <div>
                                <div><font face="Arial" size="5"><br>
                                  </font>
                                  <blockquote type="cite">
                                    <div bgcolor="#FFFFFF"
                                      text="#000000"><font face="Arial"
                                        size="5"> The currently active
                                        neuron is the one being examined
                                        by the top down process<br>
                                      </font></div>
                                  </blockquote>
                                  <div><font face="Arial" size="5"><br>
                                    </font></div>
                                </div>
                                <font face="Arial" size="5">I'm not sure
                                  what you mean by "being examined", but
                                  perhaps my comment above may deal with
                                  it.</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">I should
                                  comment, though, about your use of the
                                  word "currently active neuron". I
                                  assume that you mean at the category
                                  level. </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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 <i>Neural
                                    Networks</i>. 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. </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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.<br>
                                </font>
                                <div><font face="Arial" size="5"><br>
                                  </font></div>
                                <div><font face="Arial" size="5">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. </font></div>
                                <div><font face="Arial" size="5"><br>
                                  </font></div>
                                <div><font face="Arial" size="5">See,
                                    e.g., <a moz-do-not-send="true"
                                      href="http://techlab.bu.edu/members/gail/articles/115_dART_NN_1997_.pdf"
                                      target="_blank">http://techlab.bu.edu/members/gail/articles/115_dART_NN_1997_.pdf</a>
                                    and <a moz-do-not-send="true"
href="http://techlab.bu.edu/members/gail/articles/155_Fusion2008_CarpenterRavindran.pdf"
                                      target="_blank">http://techlab.bu.edu/members/gail/articles/155_Fusion2008_CarpenterRavindran.pdf</a>.</font></div>
                                <div><font face="Arial" size="5"><br>
                                  </font></div>
                                <div><font face="Arial" size="5">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.</font></div>
                                <div>
                                  <div><font face="Arial" size="5"><br>
                                    </font></div>
                                  <blockquote type="cite">
                                    <div bgcolor="#FFFFFF"
                                      text="#000000"><font face="Arial"
                                        size="5"> in a sequential
                                        fashion: one neuron after
                                        another, until an acceptable
                                        neuron is found.<br>
                                        <br>
                                        (2) The input to the ART in the
                                        late 1990's is for a single
                                        feature vector as a monolithic
                                        input.  <br>
                                        By monolithic, I mean that all
                                        neurons take the entire input
                                        feature vector as input.   <br>
                                        I raise this point here because
                                        neuron in ART in the late 1990's
                                        does not have an explicit local
                                        sensory receptive field (SRF), <br>
                                        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 <br>
                                        in an input image. <br>
                                      </font></div>
                                  </blockquote>
                                  <div><font face="Arial" size="5"><br>
                                    </font></div>
                                </div>
                                <font face="Arial" size="5">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. </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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 <a
                                    moz-do-not-send="true"
                                    href="http://cns.bu.edu/%7Esteve"
                                    target="_blank">http://cns.bu.edu/~steve</a>. </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">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.</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">I hope
                                  that the above replies provide some
                                  relevant information, as well as
                                  pointers for finding more.</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">Best,</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5">Steve</font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div><font face="Arial" size="5"><br>
                                </font></div>
                              <div>
                                <blockquote type="cite">
                                  <div bgcolor="#FFFFFF" text="#000000">
                                    <div> <font face="Arial" size="5"><br>
                                        My apology again if my
                                        understanding above has errors
                                        although I have examined the
                                        above two points carefully <br>
                                        through multiple your papers.<br>
                                        <br>
                                        Best regards,<br>
                                        <br>
                                        -John<br>
                                        <br>
                                      </font></div>
                                    <div>
                                      <pre cols="72"><font face="Arial"><span style="font-size:18px">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: <a moz-do-not-send="true" href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a>
Fax: <a moz-do-not-send="true" href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a>
Email: <a moz-do-not-send="true" href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a>
----------------------------------------------

</span></font></pre>
                                    </div>
                                  </div>
                                </blockquote>
                              </div>
                              <div><font face="Arial" size="5"><br>
                                </font>
                                <div> <font face="Arial" size="5"><span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px"><span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px">
                                        <div
                                          style="word-wrap:break-word">
                                          <span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px">
                                            <div
                                              style="word-wrap:break-word">
                                              <div>
                                                <div>
                                                  <div>
                                                    <div>Stephen
                                                      Grossberg</div>
                                                    <div>Wang Professor
                                                      of Cognitive and
                                                      Neural Systems</div>
                                                    <div>Professor of
                                                      Mathematics,
                                                      Psychology, and
                                                      Biomedical
                                                      Engineering</div>
                                                    <div>
                                                      <div>Director,
                                                        Center for
                                                        Adaptive
                                                        Systems <a
                                                          moz-do-not-send="true"
href="http://www.cns.bu.edu/about/cas.html" target="_blank">http://www.cns.bu.edu/about/cas.html</a></div>
                                                    </div>
                                                    <div><a
                                                        moz-do-not-send="true"
href="http://cns.bu.edu/%7Esteve" target="_blank">http://cns.bu.edu/~steve</a></div>
                                                    <div><a
                                                        moz-do-not-send="true"
href="mailto:steve@bu.edu" target="_blank">steve@bu.edu</a></div>
                                                  </div>
                                                </div>
                                              </div>
                                              <div><br>
                                              </div>
                                            </div>
                                          </span></div>
                                      </span><br>
                                    </span><br>
                                  </font></div>
                                <br>
                              </div>
                            </div>
                          </div>
                        </blockquote>
                      </div>
                      <br>
                    </div>
                  </blockquote>
                  <br>
                </div>
              </div>
              <pre cols="72"><span class="HOEnZb"><font color="#888888">-- 
--
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: <a moz-do-not-send="true" href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a></font></span><div class="">
Fax: <a moz-do-not-send="true" href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a>
Email: <a moz-do-not-send="true" href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a>
----------------------------------------------

</div></pre>
            </div>
          </blockquote>
        </div>
        <br>
      </div>
    </blockquote>
    <br>
    <pre class="moz-signature" cols="72">-- 
--
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: <a class="moz-txt-link-abbreviated" href="mailto:weng@cse.msu.edu">weng@cse.msu.edu</a>
URL: <a class="moz-txt-link-freetext" href="http://www.cse.msu.edu/~weng/">http://www.cse.msu.edu/~weng/</a>
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