Path: utzoo!attcan!uunet!husc6!uwvax!uwslh!lishka From: lishka@uwslh.UUCP (Fish-Guts) Newsgroups: comp.ai.neural-nets Subject: Re: Neuron Resolution (warning: this baby is long!) Message-ID: <400@uwslh.UUCP> Date: 11 Nov 88 22:15:15 GMT References: <183@enuxha.eas.asu.edu> Reply-To: lishka@uwslh.UUCP (Fish-Guts) Organization: U of Wisconsin-Madison, State Hygiene Lab Lines: 120 In article <183@enuxha.eas.asu.edu> rao@enuxha.eas.asu.edu (Arun Rao) writes: > > Here's the only useful reply I received to my posting (actually, I >received two from Chris Lishka, and this is his second). Hello, this is Chris speaking.... >I got the >book he talks about from the library. It is authoritative (Kuffler >was apparently a leading light in experimental neurophysiology) If I remember my neurobiologists correctly, Kuffler was one of the big names in the study of the human visual system, especially the retina. Hence I found that the Kuffler book was heavy on the visual system; other books are not. >and >has a textbook flavor to it. It gets into more detail than I need, >but is definitely readable. However, there is no mention of the >kind of information I was looking for. I am sorry to hear that. You will probably need to go hunt some actual research papers up that deal with your area more specifically. >The trouble seems to be that >engineers need information that neurophysiologists never think of >obtaining. Actually, this was originally my opinion, *before* I took the neurobiology courses. Afterwards, it dawned on me that engineers (both in software and hardware) are looking for "information" that is too simple and vague. There is too much going on in the nervous system (even in single neurons) to consider just the firing rate variance, or just the variance in the number of synapses onto a single neuron. The types of information that engineers want have probably been considered by neurobiologists many years ago. One of the biggest lessons I learned was there is no such thing as a "typical" neuron. The nervous systems of living creatures (especially humans) are much too varied, and the structures and types of neurons take on many, many different forms. If one considers this, then much of the simple data (i.e. typical firing rates, variance in dendrite trees, variance in the length and width of axons, etc.) is fairly meaningless unless one is looking at a very specific area in the nervous system. And so much goes on in the nervous system that it is really hard to determine whether or not a particular characteristic of a group of neurons is a contrbuting factor in why it works the way it does. Simple data is usually only good for defining very general characteristics about neurons (i.e. the fact that some axons are myelinated allows signals to travel much faster down the axon body). A good example lies in the study of the retina. From what the professors taught us, early on the neurobiology community began to study the structure of the retina because it was thought that it was composed of fairly "typical" neurons. Besides this, it was easier to study the retina because (a) the input source which the visual system interpretted (i.e. light!) was the easiest to measure of all the senses and (b) it is easier to look at retinas in other animals than try and study the inner ear or skin responses. Since the early studies, a great amount of work has been performed on the retina, and much is known about the layered structure, the types of neurons, and the variety of interconnections between retinal neurons (there is still much to learn, though). However, the neurobiologists also discovered that the neurons in the retina tended to be much different from other neurons, and were not "typical" as was once hoped. Also, neurobiologists now tend to believe that the retina serves as a sort of "preprocessing" stage to the visual cortex, which is believed to handle the "higher order" interpretations of the visual inputs (although is is very possible that the retina serves other purposes, such as an Associative Memory for images). The "moral" of the above story is that even though research into retinal neurobiology *has* defined much of what goes on in the visual system, it hasn't shed all that much light on what happens in other areas of the human brain. The many sections of the nervous system can be very different in structure, and vary from massive layers of highly organized neurons in very regular connection patterns to other areas where there are incredibly different types of neurons that are connected in a more "random" fashion. Do not take any area in the nervous system to be a "typical" area; all are fairly specialized to the function that they serve. It is for these reasons that I believe current AI "neural-network" theories are much too simple to be used as models for biological neural-networks. They not not be taken as such, but instead should be respected for what they are: interesting studies into massively connected networks of simple elements. I feel that Connectionism is a wonderful study of the characteristics and power available using massively connected parallel networks. Real nervous systems will share some (but probably not all) of these characteristics. However, current neural networks are not very good models of real biological neural networks, because the Connectionist models are much too simplistic and "generic" to be that effective. Therefore, I would also be careful about taking specific measurements (such as the variance in firing rates, the number of connections in real neural systems, etc.) and applying them to Connectionist models and expecting them to be useful. It seems to me that at the stage that artificial neural-networks are at, only really basic characteristics should be conisdered. I will get down off of my soapbox now! Sorry about the length, but it is a topic I feel somewhat strongly about. I think that both neurobiology and AI (especially Connectionism) are two amazing fields of science, and each has a lot to learn from each other. Each field should be respected for what it is. Here's to many more years of fruitful research and cooperation between the two! >P.S.: Chris Lishka's e-mail address is lishka%uwslh@cs.wisc.edu. This address may or may not work (isn't email great? ;-) See my .signature below for more information. .oO Chris Oo.-- Christopher Lishka ...!{rutgers|ucbvax|...}!uwvax!uwslh!lishka Wisconsin State Lab of Hygiene lishka%uwslh.uucp@cs.wisc.edu Immunology Section (608)262-1617 lishka@uwslh.uucp ---- "...Just because someone is shy and gets straight A's does not mean they won't put wads of gum in your arm pits." - Lynda Barry, "Ernie Pook's Commeek: Gum of Mystery"