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Yes, much of fMRI/MEG can be hyped and misused..like most technologies. But to be fair, there is<BR>
interesting work going on with the connectome and with "big data". And we are just<BR>
at the tipping point of the deluge of this stuff...so if you didn't like brain mapping in the<BR>
last 10 years... time to disconnect! But here's an example of something we thought<BR>
would be a model of this type of research:<BR>
<BR>
An early version of this was an study that Russ Poldrack and I did a few years ago, using 130 <BR>
brains, with 8 different tasks (equal number sub per task) same magnet same enviroment etc.. and <BR>
used a large margin classifier (SVM) to predict the task the subjects were doing using only the<BR>
whole brain (400k voxels) distributed pattern of activity. We were able to decode task state <BR>
with 80% cross validation on held out brains.<BR>
<BR>
If one looks at the representations recovered, it begins to give one a sense of the difficulty<BR>
of matching brain activity with tasks. In some ways we don't really have an epistomology of tasks (cognitive or perceptual) and they don't really match up to the constitute structure of the brain.<BR>
Of course its presumptive that somehow our arbitrary tasks have something to do with brain computation in a factorial (linear!) way.<BR>
<BR>
Here's the paper if you have interest in the details:<BR>
<BR>
<A HREF="http://nwkpsych.rutgers.edu/~jose/psych_sciPHH.pdf">http://nwkpsych.rutgers.edu/~jose/psych_sciPHH.pdf</A><BR>
<BR>
Cheers,<BR>
<BR>
Steve<BR>
<BR>
On Sat, 2014-01-25 at 11:05 -0600, james bower wrote:<BR>
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Hi Jose,
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Ah, neuroimaging - don’t get me started. Not all, but a great deal of neuroimaging has become a modern form of phrenology IMHO, distorting not only neuroscience, but it turns out, increasingly business too. To wit:
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At present I am actually much more concerned (and involved) in the use of brain imaging in what has come to be called "Neuro-marketing”. Many on this list are perhaps not aware, but while we worry about the effect of over interpretation of neuroimaging data within neuroscience, the effect of this kind of data in the business world is growing and not good. Although my guess is that those of you in the United States might have noted the rather large and absurd marketing campaign by Lumosity and the sellers of other ‘brain training” games. A number of neuroscientists are actually now getting in this business.
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As some of you know, almost as long as I have been involved in computational neuroscience, I have also been involved in exploring the use of games for children’s learning. In the game/learning world, the misuse of neuroscience and especially brain imaging has become excessive. It wouldn’t be appropriate to belabor this point on this list - although the use of neuroscience by the NN community does, in my view, often cross over into a kind of neuro-marketing.
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For those that are interested in the more general abuses of neuro-marketing, here is a link to the first ever session I organized in the game development world based on my work as a neurobiologist:
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<A HREF="http://www.youtube.com/watch?v=Joqmf4baaT8&list=PL1G85ERLMItAA0Bgvh0PoZ5iGc6cHEv6f&index=16">http://www.youtube.com/watch?v=Joqmf4baaT8&list=PL1G85ERLMItAA0Bgvh0PoZ5iGc6cHEv6f&index=16</A>
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As set up for that video, you should know that in his keynote address the night before, Jessi Schelll (of Schell Games and CMU) introduced his talk by saying that he was going to tell the audience what neuroscientists have figured out about human brains, going on to claim that they (we) have discovered that human brains come in two forms, goat brains and sheep brains. Of course the talk implied that the goats were in the room and the sheep were out there to be sold to. (although as I noted on the twitter feed at the time, there was an awful lot of ‘baaing’ going on in the audience :-) ).
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Anyway, the second iteration of my campaign to try to bring some balance and sanity to neuro-marketing, will take place at SxSW in Austin in march, in another session I have organized on the subject.
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<A HREF="http://schedule.sxsw.com/2014/events/event_IAP22511">http://schedule.sxsw.com/2014/events/event_IAP22511</A>
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If you happen to be in Austin for SxSW feel free to stop by. :-)
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The larger point, I suppose, is that while we debate these things within our own community, our debate and our claims have unintended consequences in larger society, with companies like Lumosity, in effect marketing to the baby boomers the idea (false) that using “the science of neuroplasticity’ and doing something as simple as playing games “designed by neuroscientists” can revert their brains to teen age form. fRMI and Neuropsychology used extensively as evidence.
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Perhaps society has become so accustomed to outlandish claims and over selling that they won’t hold us accountable.
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Or perhaps they will.
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Jim
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p.s. (always a ps) I have also recently proposed that we declare a moratorium on neuroimaging studies until we at least know how the signal is related to actual neural-activity. Seems rather foolish to base so much speculation and interpretation on a signal we don’t understand. Easy enough to poo poo cell spikers - but to my knowledge, there is no evidence that neural computing is performed through the generation of areas of red, yellow, green and blue. :-)
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On Jan 25, 2014, at 9:43 AM, Stephen José Hanson <<A HREF="mailto:jose@psychology.rutgers.edu">jose@psychology.rutgers.edu</A>> wrote:
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Indeed. Its like we never stopped arguing about this for the last 30 years! Maybe this is a brain principle<BR>
integrated fossilized views of the brain principles.<BR>
<BR>
I actually agree with John.. and disagree with you JIm... surprise surprise...seems like old times..<BR>
<BR>
The most disconcerting thing about the emergence the new new neural network field(s)<BR>
is that the NIH Connectome RFPs contain language about large scale network functions...and<BR>
yet when Program managers are directly asked whether fMRI or any neuroimaging methods<BR>
would be compliant with the RFP.. the answer is "NO".<BR>
<BR>
So once the neuroscience cell spikers get done analyzing 1000 or 10000 or even a 1M neurons<BR>
at a circuit level.. we still won't know why someone makes decisions about the shoes they wear; much<BR>
less any other mental function! Hopefully neuroimaging will be relevant again.<BR>
<BR>
Just saying.<BR>
<BR>
Cheers.<BR>
<BR>
Steve<BR>
PS. Hi Gary! Dijon!<BR>
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<TABLE CELLSPACING="0" CELLPADDING="0" WIDTH="100%">
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Stephen José Hanson<BR>
Director RUBIC (Rutgers Brain Imaging Center)<BR>
Professor of Psychology<BR>
Member of Cognitive Science Center (NB)<BR>
Member EE Graduate Program (NB)<BR>
Member CS Graduate Program (NB)<BR>
Rutgers University
<PRE>
email: <A HREF="mailto:jose@psychology.rutgers.edu">jose@psychology.rutgers.edu</A>
web: <A HREF="http://psychology.rutgers.edu/~jose">psychology.rutgers.edu/~jose</A>
lab: <A HREF="http://www.rumba.rutgers.edu">www.rumba.rutgers.edu</A>
fax: 866-434-7959
voice: 973-353-3313 (RUBIC)
</PRE>
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On Fri, 2014-01-24 at 17:31 -0600, james bower wrote:<BR>
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Well, well - remarkable!!! an actual debate on connectionists - just like the old days - in fact REMARKABLY like the old days.<BR>
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<BR>
Same issues - how ‘brain-like’ is ‘brain-like’ and how much hype is ‘brain-like’ generating by itself. How much do engineers really know about neuroscience, and how much do neurobiologists really know about the brain (both groups tend to claim they know a lot - now and then).<BR>
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<BR>
I went to the NIPS meeting this year for the first time in more than 25 years. Some of the older timers on connectionists may remember that I was one of the founding members of NIPS - and some will also remember that a few years of trying to get some kind of real interaction between neuroscience and then ‘neural networks’ lead me to give up and start, with John Miller, the CNS meetings - focused specifically on computational neuroscience. Another story - <BR>
<BR>
<BR>
At NIPS this year, there was a very large focus on “big data” of course, with "machine learning" largely replaced "Neural Networks" in most talk titles. I was actually a panelist (most had no idea of my early involvement with NIPS) on big data in on-line learning (generated by Ed-X, Kahn, etc) workshop. I was interested, because for 15 years I have also been running Numedeon Inc, whose virtual world for kids, <A HREF="http://whyville.net/">Whyville.net</A> was the first game based immersive worlds, and is still one of the biggest and most innovative. (no MOOCs there).<BR>
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From the panel I made the assertion, as I had, in effect, many years ago, that if you have a big data problem - it is likely you are not taking anything resembling a ‘brain-like’ approach to solving it. The version almost 30 years ago, when everyone was convinced that the relatively simple Hopfield Network could solve all kinds of hard problems, was my assertion that, in fact, simple ‘Neural Networks, or simple Neural Network learning rules were unlikely to work very well, because, almost certainly, you have to build a great deal of knowledge about the nature of the problem into all levels (including the input layer) of your network to get it to work.<BR>
<BR>
<BR>
Now, many years later, everyone seems convinced that you can figure things out by amassing an enormous amount of data and working on it.<BR>
<BR>
<BR>
It has been a slow revolution (may actually not even be at the revolutionary stage yet), BUT it is very likely that the nervous system (like all model based systems) doesn’t collect tons of data to figure out with feedforward processing and filtering, but instead, collects the data it thinks it needs to confirm what it already believes to be true. In other words, it specifically avoids the big data problem at all cost. It is willing to suffer the consequence that occasionally (more and more recently for me), you end up talking to someone for 15 minutes before you realize that they are not the person you thought they were.<BR>
<BR>
<BR>
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.<BR>
<BR>
<BR>
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.<BR>
<BR>
<BR>
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.<BR>
<BR>
<BR>
I saw none of that at NIPS - and in fact, I see less and less of that at the CNS meeting as well.<BR>
<BR>
<BR>
All too easy to simplify, pontificate, and sell.<BR>
<BR>
<BR>
So, I sympathize with Juyang Wang’s frustration.<BR>
<BR>
<BR>
If there is any better evidence that we are still in the dark, it is that we are still having the same debate 30 years later, with the same ruffled feathers, the same bold assertions (mine included) and the same seeming lack of progress.<BR>
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<BR>
If anyone is interested, here is a chapter I recently wrote of the book I edited on “20 years of progress in computational neuroscience (Springer) on the last 40 years trying to understand the workings of a single neuron (The cerebellar Purkinje cell), using models. <A HREF="https://www.dropbox.com/s/5xxut90h65x4ifx/272602_1_En_5_DeltaPDF%20copy.pdf">https://www.dropbox.com/s/5xxut90h65x4ifx/272602_1_En_5_DeltaPDF%20copy.pdf</A><BR>
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Perhaps some sense of how far we have yet to go.<BR>
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Jim Bower<BR>
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On Jan 24, 2014, at 4:00 PM, Ralph Etienne-Cummings <<A HREF="mailto:ralph.etiennecummings@gmail.com">ralph.etiennecummings@gmail.com</A>> wrote:<BR>
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Hey, I am happy when our taxpayer money, of which I contribute way more than I get back, funds any science in all branches of the government. <BR>
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Neuromorphic and brain-like computing is on the rise ... Let's please not shoot ourselves in the foot with in-fighting!!<BR>
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Thanks,<BR>
Ralph's Android<BR>
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On Jan 24, 2014 4:13 PM, "Juyang Weng" <<A HREF="mailto:weng@cse.msu.edu">weng@cse.msu.edu</A>> wrote: <BR>
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Yes, Gary, you are correct politically, not to upset the "emperor" since he is always right and he never falls behind the literature. <BR>
<BR>
But then no clear message can ever get across. Falling behind the literature is still the fact. More, the entire research community that does brain research falls behind badly the literature of necessary disciplines. The current U.S. infrastructure of this research community does not fit at all the brain subject it studies! This is not a joking matter. We need to wake up, please. <BR>
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Azriel Rosenfeld criticized the entire computer vision filed in his invited talk at CVPR during early 1980s: "just doing business as usual" and "more or less the same" . However, the entire computer vision field still has not woken up after 30 years! As another example, I respect your colleague Terry Sejnowski, but I must openly say that I object to his "we need more data" as the key message for the U.S. BRAIN Project. This is another example of "just doing business as usual" and so everybody will not be against you. <BR>
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Several major disciplines are closely related to the brain, but the scientific community is still very much fragmented, not willing to wake up. Some of our government officials only say superficial worlds like "Big Data" because we like to hear. This cost is too high for our taxpayers. <BR>
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-John <BR>
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On 1/24/14 2:19 PM, Gary Cottrell wrote:<BR>
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Hi John - <BR>
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It's great that you have an over-arching theory, but if you want people to read it, it would be better not to disrespect people in your emails. You say you respect Matthew, but then you accuse him of falling behind in the literature because he hasn't read your book. Politeness (and modesty!) will get you much farther than the tone you have taken. <BR>
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g. <BR>
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On Jan 24, 2014, at 6:27 PM, Juyang Weng <<A HREF="mailto:weng@cse.msu.edu">weng@cse.msu.edu</A>> wrote: <BR>
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Dear Matthew:<BR>
<BR>
My apology if my words are direct, so that people with short attention spans can quickly get my points. I do respect you.<BR>
<BR>
You wrote: "to build hardware that works in a more brain-like way than conventional computers do. This is not what is usually meant by research in neural networks."<BR>
<BR>
Your statement is absolutely not true. Your term "brain-like way" is as old as "brain-like computing". Read about the 14 neurocomputers built by 1988 in Robert Hecht-Nielsen, "Neurocomputing: picking the human brain", IEEE Spectrum 25(3), March 1988, pp. 36-41. Hardware will not solve the fundamental problems of the current human severe lack in understanding the brain, no matter how many computers are linked together. Neither will the current "Big Data" fanfare from NSF in U.S.. The IBM's brain project has similar fundamental flaws and the IBM team lacks key experts. <BR>
<BR>
Some of the NSF managers have been turning blind eyes to breakthrough work on brain modeling for over a decade, but they want to waste more taxpayer's money into its "Big Data" fanfare and other "try again" fanfares. It is a scientific shame for NSF in a developed country like U.S. to do that shameful politics without real science, causing another large developing country like China to also echo "Big Data". "Big Data" was called "Large Data", well known in Pattern Recognition for many years. Stop playing shameful politics in science! <BR>
<BR>
You wrote: "Nobody is claiming a `brain-scale theory that bridges the wide gap,' or even close." <BR>
<BR>
To say that, you have not read the book: <A HREF="http://www.brain-mind-institute.org/press.html">Natural and Artificial Intelligence</A>. You are falling behind the literature so bad as some of our NSF project managers. With their lack of knowledge, they did not understand that the "bridge" was in print on their desks and in the literature. <BR>
<BR>
-John<BR>
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On 1/23/14 6:15 PM, Matthew Cook wrote:<BR>
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Dear John, <BR>
<BR>
<BR>
I think all of us on this list are interested in brain-like computing, so I don't understand your negativity on the topic. <BR>
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Many of the speakers are involved in efforts to build hardware that works in a more brain-like way than conventional computers do. This is not what is usually meant by research in neural networks. I suspect the phrase "brain-like computing" is intended as an umbrella term that can cover all of these efforts. <BR>
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<BR>
I think you are reading far more into the announcement than is there. Nobody is claiming a "brain-scale theory that bridges the wide gap," or even close. To the contrary, the announcement is very cautious, saying that intense research is "gradually increasing our understanding" and "beginning to shed light on the human brain". In other words, the research advances slowly, and we are at the beginning. There is certainly no claim that any of the speakers has finished the job. <BR>
<BR>
<BR>
Similarly, the announcement refers to "successful demonstration of some of the underlying principles [of the brain] in software and hardware", which implicitly acknowledges that we do not have all the principles. There is nothing like a claim that anyone has enough principles to "explain highly integrated brain functions". <BR>
<BR>
<BR>
You are concerned that this workshop will avoid the essential issue of the wide gap between neuron-like computing and highly integrated brain functions. What makes you think it will avoid this? We are all interested in filling this gap, and the speakers (well, the ones who I know) all either work on this, or work on supporting people who work on this, or both. <BR>
<BR>
<BR>
This looks like it will be a very nice workshop, with talks from leaders in the field on a variety of topics, and I wish I were able to attend it. <BR>
<BR>
<BR>
Matthew <BR>
<BR>
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On Jan 23, 2014, at 7:08 PM, Juyang Weng wrote: <BR>
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Dear Anders,<BR>
<BR>
Interesting topic about the brain! But Brain-Like Computing is misleading because neural networks have been around for at least 70 years.<BR>
<BR>
I quote: "We are now approaching the point when our knowledge will enable successful demonstrations of some of the underlying principles in software and hardware, i.e. brain-like computing."<BR>
<BR>
What are the underlying principles? I am concerned that projects like "Brain-Like Computing" avoid essential issues: <BR>
the wide gap between neuron-like computing and well-known highly integrated brain functions.<BR>
Continuing this avoidance would again create bad names for "brain-like computing", just such behaviors did for "neural networks".<BR>
<BR>
Henry Markram criticized IBM's brain project which does miss essential brain principles, but has he published such principles?<BR>
Modeling individual neurons more and more precisely will explain highly integrated brain functions? From what I know, definitely not, by far. <BR>
<BR>
Has any of your 10 speakers published any brain-scale theory that bridges the wide gap? Are you aware of any such published theories? <BR>
<BR>
I am sorry for giving a CC to the list, but many on the list said that they like to hear discussions instead of just event announcements. <BR>
<BR>
-John<BR>
<BR>
<BR>
On 1/13/14 12:14 PM, Anders Lansner wrote:<BR>
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<B><FONT SIZE="6">Workshop on Brain-Like Computing, February 5-6 2014</FONT></B>
</H1>
The exciting prospects of developing brain-like information processing is one of the Deans Forum focus areas.<BR>
As a means to encourage progress in this research area a Workshop is arranged February 5th-6th 2014 on KTH campus in Stockholm. <BR>
<BR>
The human brain excels over contemporary computers and robots in processing real-time unstructured information and uncertain data as well as in controlling a complex mechanical platform with multiple degrees of freedom like the human body. Intense experimental research complemented by computational and informatics efforts are gradually increasing our understanding of underlying processes and mechanisms in small animal and mammalian brains and are beginning to shed light on the human brain. We are now approaching the point when our knowledge will enable successful demonstrations of some of the underlying principles in software and hardware, i.e. brain-like computing.<BR>
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This workshop assembles experts, from the partners and also other leading names in the field, to provide an overview of the state-of-the-art in theoretical, software, and hardware aspects of brain-like computing.<BR>
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<B><FONT SIZE="5">List of speakers</FONT></B>
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Giacomo Indiveri<BR>
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ETH Zürich<BR>
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Abigail Morrison<BR>
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Forschungszentrum Jülich<BR>
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Mark Ritter<BR>
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IBM Watson Research Center<BR>
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Guillermo Cecchi<BR>
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IBM Watson Research Center<BR>
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Anders Lansner<BR>
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KTH Royal Institute of Technology<BR>
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Ahmed Hemani<BR>
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KTH Royal Institute of Technology<BR>
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Steve Furber<BR>
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University of Manchester<BR>
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Kazuyuki Aihara<BR>
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University of Tokyo<BR>
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Karlheinz Meier<BR>
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Heidelberg University<BR>
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Andreas Schierwagen<BR>
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Leipzig University<BR>
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<B>For signing up to the Workshop please use the registration form found at </B><B><U><A HREF="http://bit.ly/1dkuBgR">http://bit.ly/1dkuBgR</A></U></B><BR>
<BR>
<B>You need to sign up before January 28th.</B><BR>
<BR>
<B>Web page: <A HREF="http://www.kth.se/en/om/internationellt/university-networks/deans-forum/workshop-on-brain-like-computing-1.442038">http://www.kth.se/en/om/internationellt/university-networks/deans-forum/workshop-on-brain-like-computing-1.442038</A> </B><BR>
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******************************************<BR>
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Anders Lansner<BR>
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Professor in Computer Science, Computational biology<BR>
<BR>
School of Computer Science and Communication<BR>
<BR>
Stockholm University and Royal Institute of Technology (KTH)<BR>
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<A HREF="mailto:ala@kth.se">ala@kth.se</A>, <A HREF="tel:%2B46-70-2166122">+46-70-2166122</A><BR>
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<PRE>
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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 HREF="tel:517-353-4388">517-353-4388</A>
Fax: <A HREF="tel:517-432-1061">517-432-1061</A>
Email: <A HREF="mailto:weng@cse.msu.edu">weng@cse.msu.edu</A>
URL: <A HREF="http://www.cse.msu.edu/%7Eweng/">http://www.cse.msu.edu/~weng/</A>
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--
--
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 HREF="tel:517-353-4388">517-353-4388</A>
Fax: <A HREF="tel:517-432-1061">517-432-1061</A>
Email: <A HREF="mailto:weng@cse.msu.edu">weng@cse.msu.edu</A>
URL: <A HREF="http://www.cse.msu.edu/%7Eweng/">http://www.cse.msu.edu/~weng/</A>
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[I am in Dijon, France on sabbatical this year. To call me, Skype works best (gwcottrell), or dial <A HREF="tel:%2B33%20788319271">+33 788319271</A>] <BR>
<BR>
<BR>
Gary Cottrell <A HREF="tel:858-534-6640">858-534-6640</A> FAX: <A HREF="tel:858-534-7029">858-534-7029</A> <BR>
<BR>
<BR>
My schedule is here: <A HREF="http://tinyurl.com/b7gxpwo">http://tinyurl.com/b7gxpwo</A> <BR>
<BR>
Computer Science and Engineering 0404 <BR>
IF USING FED EX INCLUDE THE FOLLOWING LINE: <BR>
CSE Building, Room 4130<BR>
University of California San Diego<BR>
9500 Gilman Drive # 0404<BR>
La Jolla, Ca. 92093-0404<BR>
<BR>
<BR>
Things may come to those who wait, but only the things left by those who hustle. -- Abraham Lincoln <BR>
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<BR>
"Of course, none of this will be easy. If it was, we would already know everything there was about how the brain works, and presumably my life would be simpler here. It could explain all kinds of things that go on in Washington." -Barack Obama <BR>
<BR>
<BR>
"Probably once or twice a week we are sitting at dinner and Richard says, 'The cortex is hopeless,' and I say, 'That's why I work on the worm.'" Dr. Bargmann said.<BR>
<BR>
"A grapefruit is a lemon that saw an opportunity and took advantage of it." - note written on a door in Amsterdam on Lijnbaansgracht.<BR>
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"Physical reality is great, but it has a lousy search function." -Matt Tong<BR>
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"Only connect!" -E.M. Forster<BR>
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"You always have to believe that tomorrow you might write the matlab program that solves everything - otherwise you never will." -Geoff Hinton <BR>
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"There is nothing objective about objective functions" - Jay McClelland<BR>
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"I am awaiting the day when people remember the fact that discovery does not work by deciding what you want and then discovering it."<BR>
-David Mermin<BR>
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Email: <A HREF="mailto:gary@ucsd.edu">gary@ucsd.edu</A><BR>
Home page: <A HREF="http://www-cse.ucsd.edu/%7Egary/">http://www-cse.ucsd.edu/~gary/</A> <BR>
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<BR>
<PRE>
--
--
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 HREF="tel:517-353-4388">517-353-4388</A>
Fax: <A HREF="tel:517-432-1061">517-432-1061</A>
Email: <A HREF="mailto:weng@cse.msu.edu">weng@cse.msu.edu</A>
URL: <A HREF="http://www.cse.msu.edu/~weng/">http://www.cse.msu.edu/~weng/</A>
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Dr. James M. Bower Ph.D.<BR>
<BR>
Professor of Computational Neurobiology<BR>
<BR>
Barshop Institute for Longevity and Aging Studies.<BR>
<BR>
15355 Lambda Drive<BR>
<BR>
University of Texas Health Science Center <BR>
<BR>
San Antonio, Texas 78245<BR>
<BR>
<BR>
<BR>
<B>Phone: 210 382 0553</B><BR>
<BR>
Email: <A HREF="mailto:bower@uthscsa.edu">bower@uthscsa.edu</A><BR>
<BR>
Web: <A HREF="http://www.bower-lab.org/">http://www.bower-lab.org</A><BR>
<BR>
twitter: superid101<BR>
<BR>
linkedin: Jim Bower<BR>
<BR>
<BR>
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-- <BR>
Stephen José Hanson<BR>
Director RUBIC (Rutgers Brain Imaging Center)<BR>
Professor of Psychology<BR>
Member of Cognitive Science Center (NB)<BR>
Member EE Graduate Program (NB)<BR>
Member CS Graduate Program (NB)<BR>
Rutgers University
<PRE>
email: jose@psychology.rutgers.edu
web: psychology.rutgers.edu/~jose
lab: www.rumba.rutgers.edu
fax: 866-434-7959
voice: 973-353-3313 (RUBIC)
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