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<font size="+1">nicely said.<br>
<br>
</font>
<div class="moz-cite-prefix">On 1/26/14 2:43 PM, Geoffrey Hinton
wrote:<br>
</div>
<blockquote
cite="mid:CAK8NvqqMEReJ2NpM65OXef0A8wZgxg3UChvDBqdVer-E7UyDbg@mail.gmail.com"
type="cite">
<div dir="ltr">
<div>I can no longer resist making one point. <br>
<br>
A lot of the discussion is about telling other people what
they should NOT be doing. I think people should just get on
and do whatever they think might work. Obviously they will
focus on approaches that make use of their particular skills.
We won't know until afterwards which approaches led to major
progress and which were dead ends. Maybe a fruitful approach
is to model every connection in a piece of retina in order to
distinguish between detailed theories of how cells get to be
direction selective. Maybe its building huge and very
artificial neural nets that are much better than other
approaches at some difficult task. Probably its both of these
and many others too. The way to really slow down the expected
rate of progress in understanding how the brain works is to
insist that there is one right approach and nearly all the
money should go to that approach. <br>
<br>
</div>
Geoff<br>
<br>
</div>
<div class="gmail_extra"><br>
<br>
<div class="gmail_quote">On Sat, Jan 25, 2014 at 3:00 PM, Brad
Wyble <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:bwyble@gmail.com" target="_blank">bwyble@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">I am extremely pleased to see such vibrant
discussion here and my thanks to Juyang for getting the
ball rolling.
<div>
<br>
</div>
<div>Jim, I appreciate your comments and I agree in large
measure, but I have always disagreed with you as regards
the necessity of simulating everything down to a lowest
common denominator . Like you, I enjoy drawing lessons
from the history of other disciplines, but unlike you, I
don't think the analogy between neuroscience and physics
is all that clear cut. The two fields deal with vastly
different levels of complexity and therefore I don't
think it should be expected that they will (or should)
follow the same trajectory. </div>
<div><br>
</div>
<div>To take your Purkinje cell example, I imagine that
there are those who view any such model that lacks an
explicit simulation of the RNA as being incomplete. To
such a person, your models would also be unfit for the
literature. So would we then change the standards such
that no model can be published unless it includes an
explicit simulation of the RNA? And why stop there?
Where does it end? In my opinion, we can't make
effective progress in this field if everyone is bound to
the molecular level. </div>
<div><br>
</div>
<div>I really think that neuroscience presents a
fundamental challenge that is not present in physics,
which is that progress can only occur when theory is
developed at different levels of abstraction that
overlap with one another. The challenge is not how to
force everyone to operate at the same level of formal
specificity, but how to allow effective communication
between researchers operating at different levels. </div>
<div><br>
</div>
<div>In aid of meeting this challenge, I think that our
field should take more inspiration from engineering, a
model-based discipline that already has to work
simultaneously at many different scales of complexity
and abstraction. </div>
<div><br>
</div>
<div><br>
</div>
<div>Best, </div>
<div>Brad Wyble</div>
<div>
<div><br>
</div>
<div><br>
</div>
</div>
</div>
<div class="gmail_extra">
<div>
<div class="h5"><br>
<br>
<div class="gmail_quote">On Sat, Jan 25, 2014 at 9:59
AM, james bower <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:bower@uthscsa.edu" target="_blank">bower@uthscsa.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">Thanks for your
comments Thomas, and good luck with your effort.
<div><br>
</div>
<div>
I can’t refrain myself from making the
probably culturist remark that this seems a
very practical approach.</div>
<div><br>
</div>
<div>I have for many years suggested that those
interested in advancing biology in general and
neuroscience in particular to a ‘paradigmatic’
as distinct from a descriptive / folkloric
science, would benefit from understanding this
transition as physics went through it in the
15th and 16th centuries. In many ways, I
think that is where we are today, although
with perhaps the decided disadvantage that we
have a lot of physicists around who, again in
my view, don’t really understand the origins
of their own science. By that, I mean, that
they don’t understand how much of their
current scientific structure, for example the
relatively clean separation between
‘theorists’ and ‘experimentalists’, is
dependent on the foundation build by those
(like Newton) who were both in an earlier
time. Once you have a sold underlying
computational foundation for a science, then
you have the luxury of this kind of
specialization - as there is a framework that
ties it all together. The Higgs effort being
a very visible recent example.</div>
<div><br>
</div>
<div>Neuroscience has nothing of the sort. As I
point out in the article I linked to in my
first posting - while it was first proposed 40
years ago (by Rodolfo Llinas) that the
cerebellar Purkinje cell had active dendrites
(i.e. that there were non
directly-synaptically associated voltage
dependent ion channels in the dendrite that
governed its behavior), and 40 years of
anatomically and physiologically realistic
modeling has been necessary to start to
understand what they do - many cerebellar
modeling efforts today simply ignore these
channels. While that again, to many on this
list, may seem too far buried in the details,
these voltage dependent channels make the
Purkinje cell the computational device that it
is. </div>
<div><br>
</div>
<div>Recently, I was asked to review a
cerebellar modeling paper in which the authors
actually acknowledged that their model lacked
these channels because they would have been
too computationally expensive to include.
Sadly for those authors, I was asked to
review the paper for the usual reason - that
several of our papers were referenced
accordingly. They likely won’t make that
mistake again - as after of course
complementing them on the fact that they were
honest (and knowledgable) enough to have
remarked on the fact that their Purkinje cells
weren’t really Purkinje cells - I had to
reject the paper for the same reason.</div>
<div><br>
</div>
<div>As I said, they likely won’t make that
mistake again - and will very likely get away
with it.</div>
<div><br>
</div>
<div>Imagine a comparable situation in a field
(like physics) which has established a
structural base for its enterprise. “We found
it computational expedient to ignore the
second law of thermodynamics in our
computations - sorry”. BTW, I know that
details are ignored all the time in physics as
one deals with descriptions at different
levels of scale - although even there, the
field clearly would like to have a way to link
across different levels of scale. I would
claim, however, that that is precisely the
“trick’ that biology uses to ‘beat’ the second
law - linking all levels of scale together -
another reason why you can’t ignore the
details in biological models if you really
want to understand how biology works. (too
cryptic a comment perhaps).</div>
<div><br>
</div>
<div>Anyway, my advice would be to consider how
physics made this transition many years ago,
and ask the question how neuroscience (and
biology) can now. Key points I think are:</div>
<div><span style="white-space:pre-wrap"> </span>-
you need to produce students who are REALLY
both experimental and theoretical (like
Newton). (and that doesn’t mean programs that
“import” physicists and give them enough
biology to believe they know what they are
doing, or programs that link experimentalists
to physicists to solve their computational
problems)</div>
<div><span style="white-space:pre-wrap"> </span>-
you need to base the efforts on models (and
therefore mathematics) of sufficient
complexity to capture the physical reality of
the system being studied (as Kepler was forced
to do to make the sun centric model of the
solar system even as close to as accurate as
the previous earth centered system)</div>
<div><span style="white-space:pre-wrap"> </span>-
you need to build a new form of collaboration
and communication that can support the
complexity of those models. Fundamentally, we
continue to use the publication system (short
papers in a journal) that was invented as part
of the transformation for physics way back
then. Our laboratories are also largely
isolated and non-cooperative, more appropriate
for studying simpler things (like those in
physics). Fortunate for us, we have a new
communication tool (the Internet) although, as
can be expected, we are mostly using it to
reimplement old style communication systems
(e-journals) with a few twists (supplemental
materials).</div>
<div><span style="white-space:pre-wrap"> </span>-
funding agencies need to insist that anyone
doing theory needs to be linked to the
experimental side REALLY, and vice versa. I
proposed a number of years ago to NIH that
they would make it into the history books if
they simply required the following monday,
that any submitted experimental grant include
a REAL theoretical and computational component
- Sadly, they interpreted that as meaning that
P.I.s should state "an hypothesis" - which
itself is remarkable, because most of the
‘hypotheses’ I see stated in Federal grants
are actually statements of what the P.I.
believes to be true. Don’t get me started on
human imaging studies. arggg</div>
<div><span style="white-space:pre-wrap"> </span>-
As long as we are talking about what funding
agencies can do, how about the following
structure for grants - all grants need to be
submitted collaboratively by two laboratories
who have different theories (better models)
about how a particular part of the brain
works. The grant should support at set of
experiments, that both parties agree
distinguish between their two points of view.
All results need to be published with joint
authorship. In effect that is how physics
works - given its underlying structure.</div>
<div><span style="white-space:pre-wrap"> </span>-
You need to get rid, as quickly as possible,
the pressure to “translate” neuroscience
research explicitly into clinical significance
- we are not even close to being able to do
that intentionally - and the pressure (which
is essentially a give away to the pharma and
bio-tech industries anyway) is forcing
neurobiologists to link to what is arguably
the least scientific form of research there is
- clinical research. It just has to be the
case that society needs to understand that an
investment in basic research will eventually
result in all the wonderful outcomes for
humans we would all like, but this distortion
now is killing real neuroscience just at a
critical time, when we may finally have the
tools to make the transition to a paradigmatic
science. </div>
<div><span style="white-space:pre-wrap"> </span></div>
<div>As some of you know, I have been all about
trying to do these things for many years -
with the GENESIS project, with the original
CNS graduate program at Caltech, with the CNS
meetings, (even originally with NIPS) and with
the first ‘Methods in Computational
Neuroscience Course" at the Marine Biological
laboratory, whose latest incarnation in Brazil
(LASCON) is actually wrapping up next week,
and of course with my own research and
students. Of course, I have not been alone in
this, but it is remarkable how little impact
all that has had on neuroscience or
neuro-engineering. I have to say, honestly,
that the strong tendency seems to be for these
efforts to snap back to the non-realistic,
non-biologically based modeling and
theoretical efforts.</div>
<div><br>
</div>
<div>Perhaps Canada, in its usual practical and
reasonable way (sorry) can figure out how to
do this right.</div>
<div><br>
</div>
<div>I hope so.</div>
<div><br>
</div>
<div>Jim</div>
<div><br>
</div>
<div>p.s. I have also been proposing recently
that we scuttle the ‘intro neuroscience’
survey courses in our graduate programs
(religious instruction) and instead organize
an introductory course built around the
history of the discovery of the origin of the
axon potential that culminated in the first
(and last) Nobel prize work in computational
neuroscience for the Hodkin Huxley model. The
50th anniversary of that prize was celebrated
last year, and the year before I helped to
organize a meeting celebrating the 60th
anniversary of the publication of the original
papers (which I care much more about anyway).
That meeting was, I believe, the first
meeting in neuroscience ever organized around
a single (mathematical) model or theory - and
in organizing it, I required all the speakers
to show the HH model on their first slide,
indicating which term or feature of the model
their work was related to. Again, a first -
but possible, as this is about the only
“community model’ we have.</div>
<div><br>
</div>
<div>Most Neuroscience textbooks today don’t
include that equation (second order
differential) and present the HH model
primarily as a description of the action
potential. Most theorists regard the HH
model as a prime example of how progress can
be made by ignoring the biological details.
Both views and interpretations are
historically and practically incorrect. In my
opinion, if you can’t handle the math in the
HH model, you shouldn’t be a neurobiologist,
and if you don’t understand the profound
impact of HH’s knowledge and experimental
study of the squid giant axon on the model,
you shouldn’t be a neuro-theorist either.
just saying. :-)</div>
<div>
<div>
<div><br>
</div>
<div><br>
<div>
<div>On Jan 25, 2014, at 6:58 AM, Thomas
Trappenberg <<a
moz-do-not-send="true"
href="mailto:tt@cs.dal.ca"
target="_blank">tt@cs.dal.ca</a>>
wrote:</div>
<br>
<blockquote type="cite">
<div dir="ltr">
<p dir="ltr">James, enjoyed your
writing. </p>
<p dir="ltr">So, what to do? We are
trying to get organized in Canada
and are thinking how we fit in
with your (US) and the European
approaches and big money. My
thought is that our advantage
might be flexibility by not having
a single theme but rather a
general supporting structure for
theory and theory-experimental
interactions. I believe the
ultimate place where we want to be
is to take theoretical proposals
more seriously and try to make
specific experiments for them;
like the Higgs project. (Any other
suggestions? Canadians, see <a
moz-do-not-send="true"
href="http://www.neuroinfocomp.ca/"
target="_blank">http://www.neuroinfocomp.ca</a>
if you are not already on there.)</p>
<p dir="ltr">Also, with regards to
big data, I believe that one very
fascinating thing about the brain
is that it can function with
'small data'.</p>
<p>Cheers, Thomas<br>
</p>
<p dir="ltr"><br>
</p>
<div class="gmail_quote">On
2014-01-25 12:09 AM, "james bower"
<<a moz-do-not-send="true"
href="mailto:bower@uthscsa.edu"
target="_blank">bower@uthscsa.edu</a>>
wrote:<br type="attribution">
<blockquote class="gmail_quote"
style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
<div
style="word-wrap:break-word">
<div>Ivan thanks for the
response,</div>
<div><br>
</div>
<div>Actually, the talks at
the recent Neuroscience
Meeting about the Brain
Project either excluded
modeling altogether - or
declared we in the US could
leave it to the Europeans.
I am not in the least bit
nationalistic - but,
collecting data without
having models (rather than
imaginings) to indicate what
to collect, is simply
foolish, with many examples
from history to demonstrate
the foolishness. In fact,
one of the primary
proponents (and likely
beneficiaries) of this Brain
Project, who gave the big
talk at Neuroscience on the
project (showing lots of
pretty pictures), started
his talk by asking: “what
have we really learned since
Cajal, except that there are
also inhibitory neurons?”
Shocking, not only because
Cajal actually suggested
that there might be
inhibitory neurons - in
fact. To quote “Stupid is
as stupid does”.</div>
<div><br>
</div>
<div>Forbes magazine estimated
that finding the Higgs Boson
cost over $13BB,
conservatively. The Higgs
experiment was absolutely
the opposite of a Big Data
experiment - In fact, can
you imagine the amount of
money and time that would
have been required if one
had simply decided to
collect all data at all
possible energy levels?
The Higgs experiment is all
the more remarkable because
it had the nearly unified
support of the high energy
physics community, not that
there weren’t and aren’t
skeptics, but still,
remarkable that the large
majority could agree on the
undertaking and effort. The
reason is, of course, that
there was a theory - that
dealt with the particulars
and the details - not
generalities. In contrast,
there is a GREAT DEAL of
skepticism (me included)
about the Brain Project -
its politics and its effects
(or lack therefore), within
neuroscience. (of course,
many people are burring
their concerns in favor of
tin cups - hoping).
Neuroscience has had genome
envy for ever - the
connectome is their response
- who says its all in the
connections? (sorry
‘connectionists’) Where is
the theory? Hebb? You
should read Hebb if you
haven’t - rather remarkable
treatise. But very far from
a theory.</div>
<div><br>
</div>
<div>If you want an honest
answer to your question - I
have not seen any good
evidence so far that the
approach works, and I deeply
suspect that the nervous
system is very much NOT like
any machine we have built or
designed to date. I don’t
believe that Newton would
have accomplished what he
did, had he not, first, been
a remarkable
experimentalist, tinkering
with real things. I feel
the same way about
Neuroscience. Having spent
almost 30 years building
realistic models of its
cells and networks (and also
doing experiments, as
described in the article I
linked to) we have made some
small progress - but only by
avoiding abstractions and
paying attention to the
details. OF course, most
experimentalists and even
most modelers have paid
little or no attention. We
have a sociological and
structural problem that, in
my opinion, only the right
kind of models can fix,
coupled with a real
commitment to the biology -
in all its complexity. And,
as the model I linked tries
to make clear - we also have
to all agree to start
working on common “community
models’. But like big horn
sheep, much safer to stand
on your own peak and make a
lot of noise. </div>
<div><br>
</div>
<div>You can predict with
great accuracy the movement
of the planets in the sky
using circles linked to
other circles - nice and
easy math, and very
adaptable model (just add
more circles when you need
more accuracy, and invent
entities like equant points,
etc). Problem is, without
getting into the nasty math
and reality of ellipses- you
can’t possible know anything
about gravity, or the
origins of the solar system,
or its various and eventual
perturbations. </div>
<div><br>
</div>
<div>As I have been saying for
30 years: Beware Ptolemy
and curve fitting.</div>
<div><br>
</div>
<div>The details of reality
matter.</div>
<div><br>
</div>
<div>Jim</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
</div>
<div><br>
</div>
<br>
<div>
<div>On Jan 24, 2014, at
7:02 PM, Ivan Raikov <<a
moz-do-not-send="true"
href="mailto:ivan.g.raikov@gmail.com"
target="_blank">ivan.g.raikov@gmail.com</a>>
wrote:</div>
<br>
<blockquote type="cite">
<div dir="ltr"><br>
<div class="gmail_extra">I
think perhaps the
objection to the Big
Data approach is that
it is applied to the
exclusion of all other
modelling approaches.
While it is true that
complete and detailed
understanding of
neurophysiology and
anatomy is at the
heart of neuroscience,
a lot can be learned
about signal
propagation in
excitable branching
structures using
statistical physics,
and a lot can be
learned about
information
representation and
transmission in the
brain using
mathematical theories
about distributed
communicating
processes. As these
modelling approaches
have been successfully
used in various areas
of science, wouldn't
you agree that they
can also be used to
understand at least
some of the
fundamental properties
of brain structures
and processes? <br>
<br>
</div>
<div class="gmail_extra">
-Ivan Raikov<br>
</div>
<div class="gmail_extra"><br>
<div
class="gmail_quote">On
Sat, Jan 25, 2014 at
8:31 AM, james bower
<span dir="ltr"><<a
moz-do-not-send="true" href="mailto:bower@uthscsa.edu" target="_blank">bower@uthscsa.edu</a>></span>
wrote:<br>
<blockquote
class="gmail_quote"
style="margin:0px
0px 0px
0.8ex;border-left:1px
solid
rgb(204,204,204);padding-left:1ex">[snip]
<br>
</blockquote>
<blockquote
class="gmail_quote"
style="margin:0px
0px 0px
0.8ex;border-left:1px
solid
rgb(204,204,204);padding-left:1ex">
<div
style="word-wrap:break-word">
<div>An enormous
amount of
engineering
and
neuroscience
continues to
think that the
feedforward
pathway is
from the
sensors to the
inside -
rather than
seeing this as
the actual
feedback loop.
Might to some
sound like a
semantic
quibble, but
I assure you
it is not.</div>
<div><br>
</div>
<div>If you
believe as I
do, that the
brain solves
very hard
problems, in
very
sophisticated
ways, that
involve, in
some sense the
construction
of complex
models about
the world and
how it
operates in
the world, and
that those
models are
manifest in
the complex
architecture
of the brain -
then
simplified
solutions are
missing the
point.</div>
<div><br>
</div>
<div>What that
means
inevitably, in
my view, is
that the only
way we will
ever
understand
what
brain-like is,
is to pay
tremendous
attention
experimentally
and in our
models to the
actual
detailed
anatomy and
physiology of
the brains
circuits and
cells.</div>
<br>
</div>
</blockquote>
</div>
</div>
</div>
</blockquote>
</div>
<br>
<div>
<div
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<p class="MsoNormal"> </p>
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<p class="MsoNormal"><span
style="font-size:14pt;font-family:Verdana;color:rgb(149,0,4)">Dr. James
M. Bower Ph.D.</span></p>
<p class="MsoNormal">
<span
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of Computational
Neurobiology</span></p>
<p class="MsoNormal"><span
style="font-family:'Times
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Institute for
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Studies.</span><span
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Lambda Drive</span><span
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<p class="MsoNormal"><span
style="font-size:11pt;font-family:'Times
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Science Center </span><span
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style="font-size:11pt;font-family:'Times
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<div><span
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</div>
<p class="MsoNormal"><b><span
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style="font-family:'Times
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of the attachments to
this e-mail, is
strictly prohibited
and that this e-mail
and all of the
attachments to this
e-mail, if any, must
be</span></p>
<p class="MsoNormal"><span
style="font-family:'Times
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returned to the sender
or destroyed and, in
either case,
this e-mail and all
attachments to this
e-mail must be
immediately deleted
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<div><span
style="font-family:'Times
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</div>
</div>
</div>
<br>
</div>
</blockquote>
</div>
</div>
</blockquote>
</div>
<br>
<div>
<div
style="text-indent:0px;letter-spacing:normal;font-variant:normal;text-align:-webkit-auto;font-style:normal;font-weight:normal;line-height:normal;text-transform:none;white-space:normal;font-family:Helvetica;word-wrap:break-word;word-spacing:0px">
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><span
style="font-size:14pt;font-family:Verdana;color:rgb(149,0,4)">Dr.
James M. Bower Ph.D.</span></p>
<p class="MsoNormal">
<span
style="font-size:11pt;font-family:'Times
New Roman'">Professor of
Computational Neurobiology</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman';color:rgb(66,0,168)">Barshop
Institute for Longevity and Aging
Studies.</span><span
style="font-family:'Times New
Roman'"></span></p>
<p class="MsoNormal"><span
style="font-size:11pt;font-family:'Times
New Roman'">15355 Lambda Drive</span><span
style="font-family:'Times New
Roman'"></span></p>
<p class="MsoNormal"><span
style="font-size:11pt;font-family:'Times
New Roman'">University of Texas
Health Science Center </span><span
style="font-family:'Times New
Roman'"></span></p>
<p class="MsoNormal"><span
style="font-size:11pt;font-family:'Times
New Roman'">San Antonio, Texas
78245</span><span
style="font-family:'Times New
Roman'"></span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'"> </span></p>
<p class="MsoNormal"><b><span
style="font-size:13pt;font-family:'Times
New Roman'">Phone: <a
moz-do-not-send="true"
href="tel:210%20382%200553"
value="+12103820553"
target="_blank">210 382 0553</a></span></b><span
style="font-family:'Times New
Roman'"></span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'">Email: <a
moz-do-not-send="true"
href="mailto:bower@uthscsa.edu"
target="_blank">bower@uthscsa.edu</a></span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'">Web: <a
moz-do-not-send="true"
href="http://www.bower-lab.org"
target="_blank">http://www.bower-lab.org</a></span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'">twitter: superid101</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'">linkedin: Jim Bower</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman'"> </span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman';color:rgb(177,89,19)">CONFIDENTIAL
NOTICE:</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
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contents of this email and any
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recipient. If you have received
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the intended recipient, you are
hereby notified that any
disclosure, copying, distribution
or use of, or the taking of
any action in reliance upon, any
of the information contained in
this e-mail, or</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman';color:rgb(177,89,19)">any
of the attachments to this e-mail,
is strictly prohibited and that
this e-mail and all of the
attachments to this e-mail, if
any, must be</span></p>
<p class="MsoNormal"><span
style="font-family:'Times New
Roman';color:rgb(177,89,19)">immediately
returned to the sender or
destroyed and, in either case,
this e-mail and all attachments to
this e-mail must be immediately
deleted from your computer without
making any copies hereof and any
and all hard copies made must be
destroyed. If you have received
this e-mail in error,
please notify the sender by e-mail
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style="font-family:'Times New
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<p class="MsoNormal"><span
style="font-family:'Times New
Roman'"> </span></p>
</div>
</div>
<br>
</div>
</div>
</div>
</div>
</blockquote>
</div>
<br>
<br clear="all">
<div><br>
</div>
</div>
</div>
<span class="HOEnZb"><font color="#888888">-- <br>
<div dir="ltr">Brad Wyble<br>
Assistant Professor<br>
Psychology Department<br>
Penn State University
<div><br>
</div>
<div><a moz-do-not-send="true"
href="http://wyblelab.com" target="_blank">http://wyblelab.com</a></div>
</div>
</font></span></div>
</blockquote>
</div>
<br>
</div>
</blockquote>
<br>
<pre class="moz-signature" cols="72">--
Hava T. Siegelmann, Ph.D.
Professor
Director, BINDS Lab (Biologically Inspired Neural Dynamical Systems)
Dept. of Computer Science
Program of Neuroscience and Behavior
University of Massachusetts Amherst
Amherst, MA, 01003
Phone - Grant Administrator – Michele Roberts: 413-545-4389
Fax: 413-545-1249
LAB WEBSITE: <a class="moz-txt-link-freetext" href="http://binds.cs.umass.edu/">http://binds.cs.umass.edu/</a>
</pre>
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