Richard, I tend to agree with you that very little fundamental progress is being made in linking neuron-level dynamics to cognitive-level dynamics.
Though it is not my main area of focus (by any means), I have been developing some specific ideas in this regard, in collaboration with a neuroscientist friend. So I don't think that building such a linkage is impossible, or necessarily even insanely difficult -- but I do think it requires a way of thinking different from what most neuroscientists are used to. The way my friend and I want to proceed is as follows. We have certain cognitive behaviors we would like to see emerge from a small neural network (i.e. a relatively small number of cortical columns), and he has a fairly biologically accurate simulator of neural networks. We then want to see how hard it is to tune the parameters of the neural net simulator to caue it to give rise to the cognitive behaviors. Specifically, we believe that networks of cortical columns can be shown to give rise to first-order probabilistic inference behavior; and that higher-order inference behavior can be achieved by including hippocampus in the mix (according to a specific theory about the role of hippocampus, which I don't want to discuss pre-publication). My point in this message isn't to spout off about my own theory in this regard, which I'd rather not do before writing the paper on it. My point is to suggest the kind of work I think could be helpful in building a bridge btw the neural and cognitive levels. I agree with you that not much work of this nature is going on. I don't think we need to have a realistic sim of the whole brain to begin to crack the problem of the relationship btw neurons and cognition. I think that moderately realistic, biologically sophisticated simulations of relatively small parts of the brain can be shown to display behaviors of obvious cognitive significance, and that this can help us build up a real neurocognitive theory. And, I don't think this is at all necessary for AGI ... though it's damn interesting on its own, and certainly may contain lessons for AGI ;-) ... as you know my work on AGI is not based on human brain emulation, because a) I feel we just don't know enough yet about the human brain, b) I feel human brains are far from optimal as intelligences... -- Ben G On 10/11/06, Richard Loosemore <[EMAIL PROTECTED]> wrote:
Sergio, Your words sound nice in theory, but that is not the way it is happening on the ground. What I tried to say was that neuroscience folks are far too quick to deploy words like "cognition" and "concepts" and "consciousness" (apparently as a way to sound impressive) when in fact their justification for using those words is appalling. They often seem not understand the way those words are used (by cognitive scientists), and their attempts to link the words to their models or observations are, quite frankly, a joke. (I am being blunt, but that is because there are strong feelings on the cognitive science side of this situation.) The point of my presenting that "analogy" quote was to say that THIS is what it looks like to the cognitive scientist who reads what they put out, so you are slipping the point sideways a little when you say that maybe the influence of noise is more important in neurons than it would be in transistors. I was not lampooning it at that level, I was saying that a neuroscientist who started off by claiming great progress in fantastically high-level areas (cognition) is being idiotic when they jump all the way down and suddenly begin to talk about *any* aspect of the neuron level directly, without just cause. My analogy was a person who claimed to be studying high-level software architecture of huge systems, but who then spent most of their time talking about transistors .... and then occasionally popped up to the top level and said (e.g.) "this looks like the concept of "object persistence" in OOP systems!" That person could not claim to be doing just "early science," trying to establish what the right concepts might be, etc (the way you frame it), because there already exists (using my analogy) an entire field of people who know a great deal about terms like objects, object persistence, message passing and so on. It ain't early stage science unless that latter crowd is ignored, as if they did not exist! Now that is the problem. You can get a flavor of some aspects of what I am ranting about ;-) from a special issue of Cognitive Neuropsychology, the lead article of which was: Harley, T. A. (2004). Does cognitive neuropsychology have a future? Cognitive Neuropsychology, 21, 3-16. Trevor Harley and I are currently working on a paper in this area. Richard Loosemore Sergio Navega wrote: > Richard, > > I'm not sure your substitutions of words constitute a valid > criticism of the computational neuroscience field. Sure, I may > concede that some of the categories they introduce may suffer > from some serious flaws. But we cannot forget that we still don't > know how the whole neural system works (as opposed to our very > good understanding of how a bunch of transistors give rise to > the US Air Traffic Control System). The way computational > neuroscientists introduce concepts (and often mix levels of > analyses) should be seen as the way scientific reasoning works > in fields with a lot to discover. The scientific method allows > the construction of such abstract notions, because it has > some chance of being appropriate and if they are not, the method > will eventually discard or substitute them for better notions. > History of science is filled with such things. In transistors, > stochastic behavior is not important. In neurons, it seems an > important factor. But what is the influence of noise in these > systems? What good does it do? We will only discover by > postulating theoretical constructions capable of generating > predictions and letting the evidences guillotine the bad models. > > Sergio Navega. > > > > > > > ----- Original Message ----- From: "Richard Loosemore" <[EMAIL PROTECTED]> > To: <[email protected]> > Sent: Wednesday, October 11, 2006 11:47 AM > Subject: Re: [agi] Fwd: Articles in this week's Science > > >> >> [Warning. The following is short and brutally frank.] >> >> This first article (which overviews the others) is a typical piece of >> neuroscience BS - at least from the point of view of a substantial >> number of people in the cognitive science community. >> >> To get an idea why, let me quote to you three excerpts from the piece, >> but translating them into claims about high level aspects of >> COMPUTING, instead of high level aspects of COGNITION. >> >> Rememember, these neuroscience folks are trying to sell the idea that >> they are making progress towards understanding how the entire brain >> works as a mind, not as a bunch of neurons. They don't claim to be >> doing neurophysiology, they claim to be making a link to cognition, >> thought, consciousness and the like. So, in my edited versions below, >> I have changed references to high level cognition to become references >> to large software systems. With this change, the essential >> vacuousness of the claims sticks out like a sore thumb. >> >> >> >> **************************************************** >> >> "Understanding the dynamics and computations of single [transistors] >> and their role within [computer circuits] is at the center of >> neuroscience. How do single-[transistor] properties contribute to >> information processing and, ultimately, to [the behavior of extremely >> complex software systems]? What level of description is required when >> modeling single [transistors]? Herz et al. (p. 80) review >> single-[transistor] models..........." >> >> "Single [transistors] are part of larger networks. Destexhe and >> Contreras (p. 85) review advances in the computations created by >> stochastic input in [transistors] and networks of [transistors]. They >> emphasize the importance of irregular activity in [transistor] >> computations............" >> >> "On a higher processing level, computational neuroscience based on [an >> approximate circuit diagram (with only 1% of the wires transistors >> actually showing)] of the [Intel Xeon] can help us understand the >> complexities of [the highest-level aspects of the software that runs >> the US Air Traffic Control System]. O'Reilly (p. 91) reviews >> developments in models, of [the software that runs the US Air Traffic >> Control System]. He develops the idea that the [floating point unit in >> an Intel Xeon] represents a synthesis between analog and digital forms >> of computation.........." >> >> **************************************************** >> >> If someone said that there was an entire field that was making >> progress in understanding the issues involved in the design and >> functioning of the largest software/hardware projects on the planet, >> and if the people in this field grabbed an issue of Science to present >> shining examples of their best work, as above, what would you think? >> >> The emperor has no clothes. >> >> >> Richard Loosmore >> >> >> >> >> >> >> >> >> >> >> >> >> Ben Goertzel wrote: >>> There's a special section in this week's Science called "Modeling the >>> Mind" >>> that should be of interest to many denizens of this list. Here are the >>> titles: >>> >>> Of Bytes and Brains >>> >>> Peter Stern and John Travis >>> Science 6 October 2006: 75. >>> <http://www.sciencemag.org/cgi/content/summary/314/5796/75> Summary >| >>> PDF <http://www.sciencemag.org/cgi/reprint/314/5796/75.pdf> >| >>> >>> >>> News >>> >>> >>> An Enterprising Approach to Brain Science >>> >>> Greg Miller >>> Science 6 October 2006: 76-77. >>> <http://www.sciencemag.org/cgi/content/summary/314/5796/76> Summary >| >>> Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/76> >>> >| PDF >>> <http://www.sciencemag.org/cgi/reprint/314/5796/76.pdf> >| >>> >>> Vision's Grand Theorist >>> >>> Ingrid Wickelgren >>> Science 6 October 2006: 78-79. >>> <http://www.sciencemag.org/cgi/content/summary/314/5796/78> Summary >| >>> Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/78> >>> >| PDF >>> <http://www.sciencemag.org/cgi/reprint/314/5796/78.pdf> >| >>> >>> >>> Reviews >>> >>> >>> Modeling Single-Neuron Dynamics and Computations: A Balance of Detail >>> and >>> Abstraction >>> >>> Andreas V. M. Herz, Tim Gollisch, Christian K. Machens, and Dieter >>> Jaeger >>> Science 6 October 2006: 80-85. >>> <http://www.sciencemag.org/cgi/content/abstract/314/5796/80> Abstract >| >>> Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/80> >>> >| PDF >>> <http://www.sciencemag.org/cgi/reprint/314/5796/80.pdf> >| >>> >>> Neuronal Computations with Stochastic Network States >>> >>> Alain Destexhe and Diego Contreras >>> Science 6 October 2006: 85-90. >>> <http://www.sciencemag.org/cgi/content/abstract/314/5796/85> Abstract >| >>> Full Text <http://www.sciencemag.org/cgi/content/full/314/5796/85> >>> >| PDF >>> <http://www.sciencemag.org/cgi/reprint/314/5796/85.pdf> >| >>> >>> Biologically Based Computational Models of High-Level Cognition >>> >>> Randall C. O'Reilly >>> Science 6 October 2006: 91-94 >>> >>> ----- >>> This list is sponsored by AGIRI: http://www.agiri.org/email >>> To unsubscribe or change your options, please go to: >>> http://v2.listbox.com/member/[EMAIL PROTECTED] >>> >>> >> >> ----- >> This list is sponsored by AGIRI: http://www.agiri.org/email >> To unsubscribe or change your options, please go to: >> http://v2.listbox.com/member/[EMAIL PROTECTED] >> >> >> >> -- >> No virus found in this incoming message. >> Checked by AVG Free Edition. >> Version: 7.1.408 / Virus Database: 268.13.2/471 - Release Date: >> 10/10/2006 >> >> > > ----- > This list is sponsored by AGIRI: http://www.agiri.org/email > To unsubscribe or change your options, please go to: > http://v2.listbox.com/member/[EMAIL PROTECTED] > > ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/[EMAIL PROTECTED]
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