neuron-request@HPLABS.HP.COM (Neuron-Digest Moderator Peter Marvit) (12/23/88)
Neuron Digest Thursday, 22 Dec 1988 Volume 4 : Issue 34 Today's Topics: Reinforcement Schemes for Learning Automata Haverford job Re: Some biological questions Re: some biological questions (Neuron Digest V4 #33) Re: Some biological questions SBIR on parallel processing Question for you or your viewers B.P. nets as associative memory nets. Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" ------------------------------------------------------------ Subject: Reinforcement Schemes for Learning Automata From: DMEREDI3@UA1VM.BITNET (Don Meredith) Organization: The Internet Date: 08 Dec 88 19:26:07 +0000 I am in desperate need of some literature on reinforcement schemes for learning automata and weight changing algorithms for neural networks. You can send any information to DMEREDI3@UA1VM or to the following address: Don Meredith 1049 Taylorwood Circle Tuscaloosa, AL 35405 ------------------------------ Date: Mon, 12 Dec 88 10:00 EST From: <J_SCHULL%HVRFORD.BITNET@CUNYVM.CUNY.EDU> Subject: Haverford faculty position FACULTY POSITION IN BIOPHYSICS AT HAVERFORD COLLEGE The Department of Physics invites applications for a special assistant or associate professorship in biophysics for a period of up to 4 years. Applicants must have a Ph.D. and be strongly motivated toward creative research and undergraduate teaching. The successful candidate will assist in developing a new interdisciplinary biophysics program. Substantial startup funds for research are available. The position is part of a major program funded by the Howard Hughes Medical Institute to enhance the biological sciences through interaction between the various science departments. Haverford is a liberal arts college with an international reputation for strong research and instruction in the physical and life sciences. We have active collaborations in the sciences with the University of Pennsylvania, Bryn Mawr College, and Swarthmore, among others. Please send curriculum vitae, list of publications, a statement of research interests, and at least three letters of recommendation by 16 January 1989 to: Professor David Pine, Department of Physics, Haverford College, Haverford, PA 19041. An Equal Opportunity/Affirmative Action Employer. ------------------------------ Subject: Re: Some biological questions From: "Neuron-Digest Moderator Peter Marvit" <neuron-request@hplms2> Date: Tue, 13 Dec 88 17:05:51 -0800 Unfortunately, the answers to most of these questions are not as simple as the questions themselves. The computer, however astonishing even to practitioners of the the art, pales in subtle complexity compared to dynamic living systems. Our knowledge of the nervous system is patchy at best; only the largest neurons have actually been recorded from and only an estimated 100 mm^2 of the 1m^2 cortex has been carefully and systematically mapped. It would not be an exaggeration to say that Neuroscience is pre-Newtonian. Given those caveats, I'll attempt a reasonable first-order. My knowledge is not deep and so detailed supplements with citations are welcome. There is a vast literature extant, and I will commend two books to start with. "Principles of Neural Science," Kandel & Schwartz, eds. (Elsevier 1985) is considered by some to be the "Bible"; while not always the clearest writing, this survey covers a vast amount of material in its 979 pages and provides an excellent overview of all parts of Neuroscience. "From Neuron to Brain", by Kuffler, Nicholls, & Martin (Sinauer, 1984) takes a more cellular approach, with special concentration on the visual system as paradigmatic of the rest of the nervous system; generally quite well written, it offers an excellent introduction to the workings of the neurons themselves. Both books give extensive "Suggested Readings" and bibliographies. Most of the following comes from these two books. > How diverse are the neurons in a small system of neurons (or in > selected regions of the brain)? Current estimates of morphologically distinct neurons number near 10,000; that is, there are about 10,000 different types of neurons. In addition, there are some dozen types of glial cells (relatively recently discovered), even though in population they outnumber neuron 50:1! The diversity of neurons depends on the region and its extent. The cerebellum, one of the best studied structures, is an extremely regular arrangement of only a few types of neurons (Purkinje cells, basket neurons, stellate cells, Golgi cells, granule cells); Purkinje cells provide the sole output of the cerebellum (entirely inhibitory), but each cell has about 150,000 dendrites (external contacts) and receives input from 200,000 contacts! The somatogastric system of the Pacific spiny lobster is one of the best understood of all invertebrate neuronal systems. Researchers in San Diego (amongst others) have been working working to describe its mechanisms. The gastric mill has 12 neurons of 7 types which appears to have rythmic properties not predictable from the individual cells; the pyloric muscles are controlled by 14 neurons of 6 different types in a complex web which has only recently been characterized as to its functional interconnections. The short answer is: incredibly complex! > Can somebody give me a general idea of the complexity of the > chemical reactions that occur in the cell body? (A vague > question I know, but I'm just trying to get an idea how much > is going on in there). The general mechanisms of signal generation and transfer down a neuron's axon are apparently straight forward -- dealing with voltage sensitive ion channels and simple physics. The subtleties of neuronal cell metabolism and ionic pumps are as complex as any other cell, with the added feature of maintaining an electrical potential. A useful first reference is "Molecular Biology of the Cell", Alberts et al, eds. (Garland Publishing, 1983) although many of the finer details are rapidly becoming out of date. > Approximately, how many chemicals/ions have been found in and > around a neuron? Again, the basic constituants of signal transfer involve (nominally) 4 ions: K+, Cl-, Na+, and Ca++. However, over 60 transmitters have been identified... often several acting within a single neuron. Further, neurons are subject to the vast chemical arsenal of the human body -- hormones, peptides, enzymes, amines, proteins, etc. I would guess that one could tally well over 100,000 chemicals and ions which react with cortex neurons. > Is it true that the basic structure of the brain is determined > when you are born? Actually, much is before you are born. The physical structures of the brain is remarkably consistent from person to person (as far as we know), and much of the basic anatomical structure exists before birth. For example, in 95% of people, one hemisphere (the "dominant" or language or left side) is physically larger than the other and this difference can be seen in 6 month old fetuses. However, the brain is enormously plastic in terms of function; even if the entire dominant hemisphere is removed from an infant, it will develop relatively normally (complete with full language!) -- contrary to the extreme deficits one might expect. > How does the shape of the neuron affect its "computation"? Really this is unknown. The number of input and output contacts, the size of the cell body, whether the axon is myelenated, whether is has an axon at all, all are factors in a cell's response. One can make some generalizations, based on a neuron's arborization (see different retinal ganglia for examples), but details are still hazy. The fact is, though, that we don't understand what a "computation" really is at the neuronal level -- at least near the cortex. In short answer, there must be some reason to have 10,000 distinct shapes. > Finally, has anyone determined what role, if any, DNA might > play in the processing performed by a neuron? Again, many clues exist, though mostly for early development. I don't have any citations, but Dr. Jeff Winer at Berkeley agrees with me that DNA probably has little to do with the moment by moment lives of mature neurons. I think the "Selfish Gene" concept of DNA guiding all levels of biological activity from molecular to behavioral must be taken metaphorically; except in certain narrowly prescribed instances, the genes themselves cease to exert direct influence past the initial stages of cell differentiation. > If anyone is interested, this questions were raised during a > reading of the first two chapters of James S. Albus' "Brains, > Behavior, and Robotics". I'm intrigued. I hope others have additional insights. ------------------------------ Subject: Re: some biological questions (Neuron Digest V4 #33) From: Paul Davis <davis%mauve.sdr.slb.com@RELAY.CS.NET> Date: Wed, 14 Dec 88 04:48:00 -0400 > Can somebody give me a general idea of the complexity of the chemical > reactions that occur in the cell body? (A vague question I know, but > I'm just trying to get an idea how much is going on in there). Hmmm, depends on what you mean by complexity. A neuron is a cell pretty much like most others, and as such has an enormously complicated panoply of (bio)chemical systems in action all the time (gene expression, protein synthesis, energy metabolism, ionic regulation (intimately related to their primary function) etc.) In terms of the reaction systems directly related to neuronal function (though its debatable whether one can distinguish between those directly related and those that are not), I would have said that they are not really `very complex' in the sense of say, a comparison with gene regulation or metabolism, but they are still enormously complex in comparison to inorganic chemistry or even simple enzyme-catalysed reactions. If you're thinking of trying to model them, my recommendation (only as an ex-biochem graduate student now in CS) would be to forget the details and concentrate on the feeedback systems implicit in their operation. Alberts et al. "The Molecular Biology of the Cell" (2nd edition) is a highly recommended starting point for grasping the intricacies of this stuff - almost bedtime reading. Paul Schlumberger Cambridge Research Cambridge, England internet: davis%mauve@sdr.slb.com ------------------------------ Subject: Re: Some biological questions From: reinke%uicslsaj.csl.uiuc.edu@uxc.cso.uiuc.edu (Robert Reinke) Date: Thu, 15 Dec 88 08:09:18 -0600 [[Editor's Note: This came in after I wrote my response. He repeats some of my reply, but had additional and valuable insights. -PM]] In response to Chip Roberson's posting: These are interesting questions, but hard to answer simply. Biological systems are *not* simple, and easy generalizations are almost always wrong in some cases. I have tried to give succint answers to your questions, but have not included references. The level of detail you're looking for can best be found in an introductory neurobiology text. If you are really interested in these questions, I suggest you read such a text (or better, take an intro course). > How diverse are the neurons in a small system of neurons (or in > selected regions of the brain)? There are certainly hundreds, if not thousands, of anatomically distinct types of neurons in the vertebrate nervous system. Some regions of the nervous system, however, do use only a few types. The classic example (and one reason it is much studied) is the cerebellum, which contains only five types, connected in a very ordered way. Another example is the retina, which also contains five distinct cell types. > Can somebody give me a general idea of the complexity of the chemical > reactions that occur in the cell body? (A vague question I know, but > I'm just trying to get an idea how much is going on in there). I assume you are interested only in the electrical reactions. The basic mechanisms have to do with ionic channels in the cell membrane. A grossly simplified outline is that communication between neurons (usually) occurs through release of neurotransmitters (there are quite a few different ones known) that interact with membrane proteins, which in turn activate or inactivate ionic channels in the membrane. Changing the flow of ions in and out of the cell changes the electrical potential across the membrane. This potential difference may (at other places) activate voltage sensitive channels, allowing propogation of the potential down the cell. This is really a tremendous simplification. There are a wide variety of different mechanisms known; the only constant is that electrical characteristics of neurons are based on the activation and inactivation of ionic channels in the membrane. There also seem to be more complex effects involving chemical intermediaries inside the cell that *permanently* change the channel characteristics. > Approximately, how many chemicals/ions have been found in and around a > neuron? Again, generalizations are tough here. Four ions seem in most cases to be the ones involved in electrical action: K+, Na+, Cl-, Ca++. But, there are also the neurotransmitters (common: acetylcholine, gamma-aminobutyric acid (an amino acid), serotonin, substance P (a peptide), epinephrin,...) and any other messengers (e.g., hormones) that can interact with membrane proteins and therefore affect the electrical properties of the cell. > Is it true that the basic structure of the brain is determined when > you are born? Yes and no. Yes, neurons during development form up in "fixed" patterns. For example, it has recently been shown (see articles by Goodman in the Nov. 1988 issue of Science) that neurons form bundles (fasciculate) based on proteins on the surface of other neurons. It is also known that in some cases developing axons seek out "guidepost cells" to find their way to the appropriate place. On the other hand, it is well known that formation of connections can be affected by the environment. The classic example is connections between the eye and the visual cortex in the cat: it has been shown that if one eye of a kitten is sutured shut during a critical period (a week approx. 3 weeks after birth), the sutured eye will be effectively blind after the sutures are removed, even though the retina is fine. The problem seems to lie in the visual cortex, where it has been shown that the sutured eye lacks the normal connections. > How does the shape of the neuron affect its "computation"? I'm not even going to try to answer this: there are too many factors. Clearly, it is not just the shape of the neuron, but where on that shape other neurons synapse that determine how it reacts. > Finally, has anyone determined what role, if any, DNA might play in > the processing performed by a neuron? Not much is known (at least to me), though some experiments have shown that during imprinting in chicks there seems to be an increase in RNA synthesis in part of the brain, followed by an increase in protein synthesis. This seems to implicate DNA in learning, but care must be taken with these results, as it is hard to distinguish effects due to learning from effects due to general stimulation of the organism. There is also work (by Agranoff) that shows inhibition of protein synthesis effects learning in goldfish in interesting ways. ------------------------------ Subject: SBIR on parallel processing From: ohare@itd.nrl.navy.mil (John O'Hare) Date: Wed, 14 Dec 88 09:03:04 -0500 1. Researchers in small businesses (less than 500 people) might be interested in participating in a research program on acoustic classification with parallel-processing networks. Awards are $50K for a 6-month definition phase; and in later competition, up to $250K for each of two years in the work phase. Close date is 6 Jan 89. 2. The topic is #N89-003 (pg. 87) in the DoD program solicitation entitled FY-89 Small Business Innovation Research (SBIR) Program. The general contact is: Mr. Bob Wrenn, SBIR Coordinator, OSD/SADBU, US Dept of Defense, Pentagon, Rm. 2A340,Washington, DC. 20301-3061. Phone: (202) 697-1481. ------------------------------ Subject: Question for you or your viewers From: pluto%cs@ucsd.edu (Mark E. P. Plutowski) Date: Wed, 14 Dec 88 13:16:34 -0800 Re: Radial basis functions and similar items Last spring I created unit-assemblies, which I then used as nodes. One is, according to the terminology used in your recent articles, a spherically-graded unit; the other would be most accurately called an ellipsoidally-graded unit. My implementation is such that the location and parameters of the surface are explicitly represented. These assemblies are of low complexity, and learn via back-prop. I submitted this news to NIPS, but alas, perhaps due to lack of meat in my hastily submitted summary and abstract, it was not accepted. I have not submitted this to anything else since, due to other obligations. My question is: in light of the recent discussions of spherically graded units, is what I did now Old Hat? thanks, keep up the good work! Mark Plutowski INTERNET: pluto%cs@ucsd.edu Department of Computer Science, C-014 pluto@beowulf.ucsd.edu University of California, San Diego La Jolla, California 92093 ------------------------------ Subject: B.P. nets as associative memory nets. From: nadi@janus.berkeley.edu (Fariborz Nadi) Organization: University of California, Berkeley Date: 15 Dec 88 00:47:22 +0000 Fellow neural-neters: I am presently doing modeling of microfabrication processes using neural-nets, specifically back-propagation nets to learn the nonlinear relation between the input and output nodes. This is the first part of the story, Now having a model I would like to use an associative memory type network to learn the groups of the input output pairs chosen by an expert. This is to divide the space of the model into subspaces that are interesting to an expert in terms of some optimal choices in his/her mind. The second net will help a novice make close to optimalchoices for a given partial-input partial-output pair. I am trying to use again a second back-propagation net as an associative type net. The way it works , I will use a net that as input has the input and output of the first net, and as the output has the same, Therefore creating a mapping between similar patterens. Kind of like 8-3-8 network (coding decimal to binary back to decimal). After the network has learned the mapping given a partial input and partial output( the same ) we can lock the values of these nodes and holding the weights and thresholds constant, change the values of the unknown input and output nodes, given that the corresponding input-output nodes should change together. This can be done using an optimization technique, not necessarily a locally computable one. Now I have two questions: 1) What do you think about the use of backpropagation nets as an associative type net, given the method discribed above or some other? 2) Is this work done before and if so where is it published? What would be very interesting to me is finding an optimization technique for the second part that would be locally computable. ------------------------------ End of Neurons Digest *********************