Connectionists: Brain-like computing fanfare and big data fanfare

james bower bower at uthscsa.edu
Sat Jan 25 21:07:20 EST 2014


> 
>  
> I *hope* that each team working on new measurement methods knows what they are trying to measure

This is precisely the original point - you CAN’T know the right thing to measure without theory and models.  

Who knew the position of stars apparently shifted when close to the sun? 

 Who knew that the 3.2K (or whatever it was) radiation that those two AT&T engineers were trying to remove with aluminum foil from their microwave dishes was SUPPOSED to be there?

And who knew that the overshoot of the action potential wasn’t an artifact of the amplifier?  

And what about the student who in the early days of GENESIS wrote to us claiming that GENESIS was poorly programmed because if you hyperpolerized the cell, the membrane rebounded into an action potential?  

The history of science is full of ‘who knews’ unfortunately, neuroscience is currently full of “I already knows” for no good reason.  :-)

Its the neuroscience theory behind the measurements that is so murky as to be non existent.


Jim



> and has an independent way of assessing their success. If they don’t, then this will be a lot more like astronomy (e.g., measuring electromagnetic radiation and trying to make inferences) than like genetics. Astronomers do this because they have no alternative, but neuroscientists can and should do better.
>  
> If the gene sequence measurement problem had been much harder, perhaps the various teams would not have been able to reach the end point. In that case, the technology development would resemble the “war on XXXX” in the sense of never reaching an end point. But even in that case, we would have been able to measure the error properties of the sequencing methods, so we could have measured progress and even done science using the imperfect measurements.
>  
> From your description, it sounds like the measurement goals of the HBP are pretty murky.
>  
> --Tom
>  
>  
> --
> Thomas G. Dietterich, Distinguished Professor Voice: 541-737-5559
> School of Electrical Engineering              FAX: 541-737-1300
>   and Computer Science                        URL: eecs.oregonstate.edu/~tgd
> US Mail: 1148 Kelley Engineering Center
> Office: 2067 Kelley Engineering Center
> Oregon State Univ., Corvallis, OR 97331-5501
>  
>  
> From: Bard Ermentrout [mailto:ermentrout at gmail.com] 
> Sent: Saturday, January 25, 2014 2:35 PM
> To: Dietterich, Tom
> Cc: Connectionists
> Subject: Re: Connectionists: Brain-like computing fanfare and big data fanfare
>  
> With the human genome project,there was clear endpoint. One knows when one has sequenced the human genome. With the BRAIN initiative, I would be optimistic about it if someone could tell me when we would know that we were done. But, of course we won't know when we are done. It is like the war on XXXX where you can put in whatever you like for XXXX. Until we know the right questions to ask we will get nowhere. We'll amass a ton of data, but unlike the genome where there is an underlying theory ( we know genes code for proteins and that other parts of the genome are regulatory)  we have no such theory of the nervous system. Everyone agrees s to what the genetic code is, but pick any random assortment of N neuroscientists, and there will be O(N) theories as to the code.
> 
> --Bard Ermentrout 
> 
> On Jan 25, 2014, at 3:39 PM, "Thomas G. Dietterich" <tgd at eecs.oregonstate.edu> wrote:
> 
> I’ve enjoyed this provocative exchange. It reminds me very much of the discussion in the late 80s about whether to invest $3B in sequencing the human genome. The lab scientists who studied individual genes and metabolic pathways complained that this lacked any detailed guiding hypotheses and would just be a waste of money.  The sequencing advocates promised that it would be revolutionary. It turned out that they were right. In fact, the technology was much more effective and much cheaper than they had dared to hope.
>  
> The current BRAIN initiative is investing a similar amount of money in developing new sensing and measurement technologies.  If we look at the history of science, we see many cases in which new instruments led to giant leaps forward: telescopes, microscopes, x-ray crystallography, MRI, interferometry, rapid throughput DNA, RNA, and protein sequencing. Of course every new technology will have biases and artifacts, and part of the process of developing a new technology is understanding these and how to compensate for them.  Often, this advances the science as well.
>  
> Sometimes it turns out that the instruments are only very indirectly capturing the underlying processes. This is where the big data argument gets interesting. If we look at the kind of data collected on the internet, it is almost always of this type.  For many ecommerce applications, it suffices to build a predictive model of customer behavior. And this is the main contribution of applied machine learning. The bigger the data sets, the more accurate these predictive models can become.
>  
> Unfortunately, such models are not very useful in science, because the goal of neuroscience, for example, is not to just to predict human behavior but to understand how that behavior is generated.  If we only have “indirect” measurements, we must fit models where the “real” variables are latent.  Making sound scientific inferences about latent variables is extremely difficult and relies on having very good models of how the latent variables produce the observed signal. Issues of identifiability and bias must be addressed, and there are also very challenging computational problems, because latent variable models typically exhibit many local optima.  Regularization, the favorite tool of machine learning, usually worsens the biases and limits our ability to draw statistical inferences.  If your model is fundamentally not identifiable, then it doesn’t matter how big your data are.
>  
> Every scientific experiment (or expenditure) involves risk, and every scientist must “place bets” on what will work. I’ll place mine on improving our measurement technology, because I think it can get us closer to measuring the critical causal variables.  But I agree with Jim that not enough effort goes into the unglamorous process of figuring out what it is that our instruments are actually measuring. And if we don’t understand that, then our scientific inferences are likely to be wrong.
>  
> --
> Thomas G. Dietterich, Distinguished Professor Voice: 541-737-5559
> School of Electrical Engineering              FAX: 541-737-1300
>   and Computer Science                        URL: eecs.oregonstate.edu/~tgd
> US Mail: 1148 Kelley Engineering Center
> Office: 2067 Kelley Engineering Center
> Oregon State Univ., Corvallis, OR 97331-5501
>  
>  
> From: Connectionists [mailto:connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of james bower
> Sent: Saturday, January 25, 2014 9:05 AM
> To: jose at psychology.rutgers.edu
> Cc: Connectionists
> Subject: Re: Connectionists: Brain-like computing fanfare and big data fanfare
>  
> Hi Jose,
>  
> 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:
>  
> 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.
>  
> 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.  
>  
> 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:
>  
> http://www.youtube.com/watch?v=Joqmf4baaT8&list=PL1G85ERLMItAA0Bgvh0PoZ5iGc6cHEv6f&index=16
>  
> 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  :-)  ).
>  
> 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.
>  
> http://schedule.sxsw.com/2014/events/event_IAP22511
>  
> If you happen to be in Austin for SxSW feel free to stop by.  :-)
>  
> 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.
>  
> Perhaps society has become so accustomed to outlandish claims and over selling that they won’t hold us accountable.
>  
> Or perhaps they will.
>  
> Jim
>  
>  
>  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.  :-)
>  
>  
>  
>  
>  
>  
>  
> On Jan 25, 2014, at 9:43 AM, Stephen José Hanson <jose at psychology.rutgers.edu> wrote:
> 
> 
> 
> Indeed.  Its like we never stopped arguing about this for the last 30 years!  Maybe this is a brain principle
> integrated fossilized views of the brain principles.
> 
> I actually agree with John.. and disagree with you JIm... surprise surprise...seems like old times..
> 
> The most disconcerting thing about the emergence the new new neural network field(s)
> is that the NIH Connectome RFPs contain language about large scale network functions...and
> yet when Program managers are directly asked whether fMRI or any neuroimaging methods
> would be compliant with the RFP.. the answer is "NO".
> 
> So once the neuroscience cell spikers get done analyzing 1000 or 10000 or even a 1M neurons
> at a circuit level.. we still won't know why someone makes decisions about the shoes they wear; much
> less any other mental function!   Hopefully neuroimaging will be relevant again.
> 
> Just saying.
> 
> Cheers.
> 
> Steve
> PS.  Hi Gary!  Dijon!
> 
> Stephen José Hanson
> Director RUBIC (Rutgers Brain Imaging Center)
> Professor of Psychology
> Member of Cognitive Science Center (NB)
> Member EE Graduate Program (NB)
> Member CS Graduate Program (NB)
> Rutgers University
>  
> email: jose at psychology.rutgers.edu
> web: psychology.rutgers.edu/~jose
> lab: www.rumba.rutgers.edu
> fax: 866-434-7959
> voice: 973-353-3313 (RUBIC)
> 
> On Fri, 2014-01-24 at 17:31 -0600, james bower wrote:
> 
> 
> Well, well - remarkable!!!  an actual debate on connectionists - just like the old days - in fact REMARKABLY like the old days.
>  
> 
> 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).
>  
> 
> 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 - 
>  
> 
> 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, Whyville.net was the first game based immersive worlds, and is still one of the biggest and most innovative.  (no MOOCs there).
>  
> 
> 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.
>  
> 
> Now, many years later, everyone seems convinced that you can figure things out by amassing an enormous amount of data and working on it.
>  
> 
> 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.
>  
> 
> 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.
>  
> 
> 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.
>  
> 
> 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.
>  
> 
> I saw none of that at NIPS - and in fact, I see less and less of that at the CNS meeting as well.
>  
> 
> All too easy to simplify, pontificate, and sell.
>  
> 
> So, I sympathize with Juyang Wang’s frustration.
>  
> 
> 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.
>  
> 
> 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.  https://www.dropbox.com/s/5xxut90h65x4ifx/272602_1_En_5_DeltaPDF%20copy.pdf
>  
> 
> Perhaps some sense of how far we have yet to go.
>  
> 
> Jim Bower
>  
> 
>  
> 
>  
> 
>  
> 
>  
> On Jan 24, 2014, at 4:00 PM, Ralph Etienne-Cummings <ralph.etiennecummings at gmail.com> wrote:
> 
> 
> 
> 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.  
> 
> Neuromorphic and brain-like computing is on the rise ... Let's please not shoot ourselves in the foot with in-fighting!!
> 
> Thanks,
> Ralph's Android
> 
> On Jan 24, 2014 4:13 PM, "Juyang Weng" <weng at cse.msu.edu> wrote:
> Yes, Gary, you are correct politically, not to upset the "emperor" since he is always right and he never falls behind the literature.  
> 
> 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. 
> 
> 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.    
> 
> 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. 
> 
> -John  
> 
> On 1/24/14 2:19 PM, Gary Cottrell wrote:
> 
> Hi John -
>  
> 
> 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.
>  
> 
> g.
>  
> On Jan 24, 2014, at 6:27 PM, Juyang Weng <weng at cse.msu.edu> wrote:
>  
> Dear Matthew:
> 
> My apology if my words are direct, so that people with short attention spans can quickly get my points.  I do respect you.
> 
> 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."
> 
> 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.  
> 
> 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!  
> 
> You wrote: "Nobody is claiming a `brain-scale theory that bridges the wide gap,' or even close." 
> 
> To say that, you have not read the book: Natural and Artificial Intelligence.  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.     
> 
> -John
> 
> On 1/23/14 6:15 PM, Matthew Cook wrote:
> 
> Dear John,
>  
> 
> I think all of us on this list are interested in brain-like computing, so I don't understand your negativity on the topic.
>  
> 
> 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.
>  
> 
> 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.
>  
> 
> 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".
>  
> 
> 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.
>  
> 
> 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.
>  
> 
> Matthew
>  
> 
>  
> On Jan 23, 2014, at 7:08 PM, Juyang Weng wrote:
>  
> Dear Anders,
> 
> Interesting topic about the brain!  But Brain-Like Computing is misleading because neural networks have been around for at least 70 years.
> 
> 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."
> 
> What are the underlying principles?  I am concerned that projects like "Brain-Like Computing" avoid essential issues: 
> the wide gap between neuron-like computing and well-known highly integrated brain functions.
> Continuing this avoidance would again create bad names for "brain-like computing", just such behaviors did for "neural networks".
> 
> Henry Markram criticized IBM's brain project which does miss essential brain principles, but has he published such principles?
> Modeling individual neurons more and more precisely will explain highly integrated brain functions? From what I know, definitely not, by far. 
> 
> Has any of your 10 speakers published any brain-scale theory that bridges the wide gap?  Are you aware of any such published theories? 
> 
> 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. 
> 
> -John
> 
> 
> 
> On 1/13/14 12:14 PM, Anders Lansner wrote:
> 
> Workshop on Brain-Like Computing, February 5-6 2014
> 
> The exciting prospects of developing brain-like information processing is one of the Deans Forum focus areas.
> As a means to encourage progress in this research area a Workshop is arranged February 5th-6th 2014 on KTH campus in Stockholm.
> 
> 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.
> 
> 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.
> 
> 
> 
> List of speakers
> 
> Speaker
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> 
> Affiliation
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> 
> Giacomo Indiveri
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> ETH Zürich
> 
> 
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> Abigail Morrison
> 
> 
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> Forschungszentrum Jülich
> 
> 
> 
> Mark Ritter
> 
> 
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> IBM Watson Research Center
> 
> 
> 
> Guillermo Cecchi
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> 
> 
> IBM Watson Research Center
> 
> 
> 
> Anders Lansner
> 
> 
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> KTH Royal Institute of Technology
> 
> 
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> Ahmed Hemani
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> KTH Royal Institute of Technology
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> Steve Furber
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> University of Manchester
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> Kazuyuki Aihara
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> 
> 
> University of Tokyo
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> Karlheinz Meier
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> 
> Heidelberg University
> 
> 
> 
> Andreas Schierwagen
> 
> 
> 
> Leipzig University
> 
> 
> 
> 
>  
> 
> For signing up to the Workshop please use the registration form found at http://bit.ly/1dkuBgR
> 
> You need to sign up before January 28th.
> 
> Web page:http://www.kth.se/en/om/internationellt/university-networks/deans-forum/workshop-on-brain-like-computing-1.442038
> 
>  
> 
>  
> 
>  
> 
> ******************************************
> 
> Anders Lansner
> 
> Professor in Computer Science, Computational biology
> 
> School of Computer Science and Communication
> 
> Stockholm University and Royal Institute of Technology (KTH)
> 
> ala at kth.se, +46-70-2166122
> 
>  
> 
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>  
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>  
> 
> Detta epostmeddelande innehåller inget virus eller annan skadlig kod för avast! Antivirus är aktivt. 
> 
> 
> 
>  
>  
> -- 
> --
> Juyang (John) Weng, Professor
> Department of Computer Science and Engineering
> MSU Cognitive Science Program and MSU Neuroscience Program
> 428 S Shaw Ln Rm 3115
> Michigan State University
> East Lansing, MI 48824 USA
> Tel: 517-353-4388
> Fax: 517-432-1061
> Email: weng at cse.msu.edu
> URL: http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>  
>  
> 
>  
> -- 
> --
> Juyang (John) Weng, Professor
> Department of Computer Science and Engineering
> MSU Cognitive Science Program and MSU Neuroscience Program
> 428 S Shaw Ln Rm 3115
> Michigan State University
> East Lansing, MI 48824 USA
> Tel: 517-353-4388
> Fax: 517-432-1061
> Email: weng at cse.msu.edu
> URL: http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>  
>  
> [I am in Dijon, France on sabbatical this year. To call me, Skype works best (gwcottrell), or dial +33 788319271]
>  
> 
> Gary Cottrell 858-534-6640 FAX: 858-534-7029
>  
> 
> My schedule is here: http://tinyurl.com/b7gxpwo
> 
> Computer Science and Engineering 0404
> IF USING FED EX INCLUDE THE FOLLOWING LINE:      
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> 
> 
> 
> Things may come to those who wait, but only the things left by those who hustle. -- Abraham Lincoln
>  
> 
> "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
>  
> 
> "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.
> 
> "A grapefruit is a lemon that saw an opportunity and took advantage of it." - note written on a door in Amsterdam on Lijnbaansgracht.
> 
> "Physical reality is great, but it has a lousy search function." -Matt Tong
> 
> "Only connect!" -E.M. Forster
> 
> "You always have to believe that tomorrow you might write the matlab program that solves everything - otherwise you never will." -Geoff Hinton
>  
> 
> "There is nothing objective about objective functions" - Jay McClelland
> 
> "I am awaiting the day when people remember the fact that discovery does not work by deciding what you want and then discovering it."
> -David Mermin
> 
> Email: gary at ucsd.edu
> Home page: http://www-cse.ucsd.edu/~gary/
>  
> 
>  
> -- 
> --
> Juyang (John) Weng, Professor
> Department of Computer Science and Engineering
> MSU Cognitive Science Program and MSU Neuroscience Program
> 428 S Shaw Ln Rm 3115
> Michigan State University
> East Lansing, MI 48824 USA
> Tel: 517-353-4388
> Fax: 517-432-1061
> Email: weng at cse.msu.edu
> URL: http://www.cse.msu.edu/~weng/
> ----------------------------------------------
>  
>  
>  
> 
>  
> 
> Dr. James M. Bower Ph.D.
> 
> Professor of Computational Neurobiology
> 
> Barshop Institute for Longevity and Aging Studies.
> 
> 15355 Lambda Drive
> 
> University of Texas Health Science Center 
> 
> San Antonio, Texas  78245
> 
>  
> 
> Phone:  210 382 0553
> 
> Email: bower at uthscsa.edu
> 
> Web: http://www.bower-lab.org
> 
> twitter: superid101
> 
> linkedin: Jim Bower
> 
>  
> 
> CONFIDENTIAL NOTICE:
> 
> The contents of this email and any attachments to it may be privileged or contain privileged and confidential information. This information is only for the viewing or use of the intended recipient. If you have received this e-mail in error or are not 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
> 
> 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
> 
> 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 immediately.
> 
>  
> 
> 
> 
>  
> 
>  
> --
> 
>  
>  
>  
> Dr. James M. Bower Ph.D.
> Professor of Computational Neurobiology
> Barshop Institute for Longevity and Aging Studies.
> 15355 Lambda Drive
> University of Texas Health Science Center 
> San Antonio, Texas  78245
>  
> Phone:  210 382 0553
> Email: bower at uthscsa.edu
> Web: http://www.bower-lab.org
> twitter: superid101
> linkedin: Jim Bower
>  
> CONFIDENTIAL NOTICE:
> The contents of this email and any attachments to it may be privileged or contain privileged and confidential information. This information is only for the viewing or use of the intended recipient. If you have received this e-mail in error or are not 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
> 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
> 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 immediately.

 

 

Dr. James M. Bower Ph.D.

Professor of Computational Neurobiology

Barshop Institute for Longevity and Aging Studies.

15355 Lambda Drive

University of Texas Health Science Center 

San Antonio, Texas  78245

 

Phone:  210 382 0553

Email: bower at uthscsa.edu

Web: http://www.bower-lab.org

twitter: superid101

linkedin: Jim Bower

 

CONFIDENTIAL NOTICE:

The contents of this email and any attachments to it may be privileged or contain privileged and confidential information. This information is only for the viewing or use of the intended recipient. If you have received this e-mail in error or are not 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

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

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 immediately.

 


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