On Sun, 02 Nov 2008 17:11:54 -0500, Christopher D. Green wrote: > Mike Palij wrote in response to the following comment by Chris: >>> You may be right that parallel distributed connectionist models (a less >>> tendentious name for "neural networks") >> >> "Tendentious"? Not to argue the point but considering that >> one can trace these models back to McCulloch & Pitts (1943) >> and other models of the nervous system (and even earlier >> connectionist conceptions), couldn't one claim that "neural >> network" has priority in describing these types of models? >> > Origin is not essence, as they say. Yes, McCulloch & Pitts were one > (two, actually) of the first to use these kinds of models (you'll find > something similar slightly earlier in Rashevsky), but there was nothing > particularly "nervous" about them (apart from McCulloch's use of the > term). There are at least as many disanalogies between them and neural > structure as there are analogies, and there are all kinds of ways to > interpret their activity that has nothing whatever to do with neural > modeling (See Green, 1998, 2001).
Okay, a few points: (1) As James Anderson presents in his 1995 text "An Introduction to Neural Networks", it could be argued that William James proposed an early form of neural networks in his 1892 "Psychology: Briefer Course". See Anderson's pages 148-149 and Figure 6.1 which illustrates a kind of two layer association system. Anderson presents this in the context of explaining Hebbian learning rules. One could probably make the argument that such conceptions could be traced back to Herbart who tried to work out the mathematics of excitation and inhibition of ideas in order to explain why some ideas were in consciousness and other were not (but the changing patterns of excitation and inhibition could make ideas change places). I realize, however, that associationism's long history has to be seen as primarily c oncerned with assciations among "ideas" and not "neurons" though this does change in the 20th century (I believe, in part, because of the work of McCulloch & Pitts). (2) Paul Cull's brief biography of Nicolas Rashevsky puts his work and McCulloch and Pitt's work into appropriate historical context (see: Cull, P. (2007). The mathematical biophysics of Nicolas Rashevsky. Biosystems, 88, 178-184 -- available at the ScienceDirect database). In 1933 Rashevsky did provide a model for the behavior of a neuron and how a neural network might operate. McCulloch & Pitts (who also resided in Chicago while Rashevsky was there and published their 1943 paper in the "Bulletin of Mathematical Biophysics" of which Rashevsky was the editor), proposed an alterantive model. Where Rashevsky's model was more concerned with continuous activity, the McCulloch & Pitts (M&P) model was concerned with discrete states and the rules that could operate on them. As James Anderson (see ref above) and others characterized this, M&P seemed to interested in showing how the logical relationships of the "first order predicate calculus" could be modeled by their type of neurons (M&P 1943 represent these networks in form of neurons in their Figure 1 while James Anderson givens a more abstract version including the "truth table" associated with a M&P neuron in his Fig 2.9 on page 49). The lasting importance of the M&P idealized neuron, as both Cull and Anderson point out, is that John von Neuman used the ideas associated with M&P neurons to develop his own ideas for the digital computer, meaning that our contemporary computers can be thought of as the "mutated" offspring of M&P. ;-) (3) James Anderson points out, the M&P neuron had properties generally consistent with what was known about the properties of neurons circa 1940s (see Anderson p49-50). Given that it is an abstraction which is supposed to embody certain mathematical ideas, it does not have to exactly mirror real neurons but be credible representations. In your papers you seem to argue that because artificial neurons do behave "exactly" like real neurons, one can rule them out as credible models, even as crude approximations. I'm sure you'll correct me if this interpretation is wrong. However, given the growth of knowledge about the properties of neurons and the nervous system since 1943, I think one might want to guard against a "presentism" tendency to discount the importance of M&P's contribution. (4) Finally, I acknowledge that the following should be interpreted tentatively subject to further confirmation but I think that it effectively deals with the issue of whether M&P neurons were supposed to view as "real neurons". From Ken Aizawa's entry on the history of connectionism in the University of Wateloo's "Dictionary of Philosophy of Mind": |During the 1930's, Nicolas Rashevsky proposed to use differential |equations and physical concepts, such as energy minimization, to |describe how the behavior of nerves and networks of nerves that |might be related to psychological processes, such as Pavlovian |conditioning. (See, for example, Rashevsky, 1931a, 1931b, 1935). |Rashevsky's work was part of a larger project of developing a mathematical |biophysics that would mirror the methods of mathematical physics. | |Rashevsky was instrumental in bringing together Warren S. McCulloch |and Walter Pitts, who in 1943 published a seminal contribution to many |fields, "A Logical Calculus of Ideas Immanent in Nervous Activity". |This work described how networks of binary threshold neurons might |be described in terms of sentences of first-order logic. Although |popularly remembered for having contributed the idea that networks |might carry out logical inferences, McCulloch and Pitts were themselves |more interested in the description of networks containing closed loops. |*****This interest stemmed in part from McCulloch's work in tracing neural |pathways using strychnine neuronography.***** Neuroscientific findings by |Rafael Lorente de No also increased the interest in closed neural circuits. http://philosophy.uwaterloo.ca/MindDict/connectionismhistory.html >> It might be just me but it seems that J.J. Gibson might be becoming >> increasingly relevant to cognitive psychology. Aren't Gibsonians fond >> of saying "it's not what's inside your head that's important, it's what >> your head is inside of that is". Or something like that. > > I've always thought that J. J. Gibson was important (but, then again, my > own supervisor was one of his students, so I got a heavy dose of > ecological psych when I was a grad student). Several researchers in AI and elsewhere who are interested in the role of perception and action seem to be working in a Gibsonian framework but rarely acknowledge him as a source (e.g., Steven Sloman in his book "Causal models" spending several pages describing perceptual processes that I immediately recognized as being Gibsonian in nature but he only provides a brief reference to Gibson at the end of the chapter). > IMHO, Gibsonians (like > Brooks) overstate the case -- the trick is not to abolish cognition, but > to conceive of it in an ongoing interaction with the environment -- but > they are still on to something important. Ulric Neisser in his attempts to develop an ecological approach to cognition had to ultimately admit that mental representations that Gibson eshewed had to be accepted as being legitimate at some level. I think the question becomes how much information can an organism directly extract (perceive) from stimulus information before a mental representation has to be retrieved. >Now if we could only cash out the fascinating insight of "affordances." :-) This is a frustrating concept but I believe that one way of thinking about affordances is that it involves the perception of functional properties of stimulus or environmental configuration that can be associated with previous stimulus-action sequences. For example, chairs are associated with sitting so what other objects in the environment will "afford" us the ability to sit on them? Of course, there is the recognition that some things may be better things to sit on (e.g., a garbage can) than others (e.g., a road cone ;-). -Mike Palij New York University [EMAIL PROTECTED] > Refs: > Green, C. D. (1998). Are connectionist models theories of cognition? > <http://psycprints.ecs.soton.ac.uk/archive/00000553/> /Psycoloquy > <http://psycprints.ecs.soton.ac.uk/>, 9/ (4). > Green, C. D. (2001). Scientific models, connectionist networks, and > cognitive science <http://www.yorku.ca/christo/papers/models-TP2.htm>. > /Theory and Psychology, 11/, 97-117. --- To make changes to your subscription contact: Bill Southerly ([EMAIL PROTECTED])
