rao@enuxha.eas.asu.edu (Arun Rao) (11/01/88)
I'm making some studies on the theoretical capabilities of neural systems. I need information concerning the resolution of neurons. For example, what is the order of variance in measured firing frequencies ? Thiscould be a measure of inherent uncertainty, and hence of resolution. Also, how accurate are the methods/instruments used to make such measurements ? None of the work I've read so far addresses these issues, and I would be grateful if someone could post/e-mail potentially useful references. I will post a summary if there is sufficient interest. - Arun Rao rao@enuxha.asu.edu rao%enuxha.asu.edu@relay.cs.net BITNET: agaxr@asuacvax
rao@enuxha.eas.asu.edu (Arun Rao) (11/10/88)
Here's the only useful reply I received to my posting (actually, I
received two from Chris Lishka, and this is his second). I got the
book he talks about from the library. It is authoritative (Kuffler
was apparently a leading light in experimental neurophysiology) 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. The trouble seems to be that
engineers need information that neurophysiologists never think of
obtaining.
Another useful book on the human visual system is "The Eye and
the Brain" (title ?) by David Hubel. It is the most readable book
on the subject that I have yet encountered.
Hope you find this useful.
- Arun Rao
ARPA: rao@enuxha.asu.edu or rao%enuxha.asu.edu@relay.cs.net.
BITNET: agaxr@asuacvax.bitnet.
P.S.: Chris Lishka's e-mail address is lishka%uwslh@cs.wisc.edu.
___________________________________________________________________
Subject: Re: Synaptic Strengths and Other Neurobiological Issues
Thanks a lot for the reply.
You're welcome!
I was thinking after I made the posting yesterday, and I
realized that what I really need for the kind of studies
I'm making is the variance in synaptic strength, rather
than in firing rate. Conventional neural-net wisdom
suggests that a neuron's output is binary - i.e. it either
fires or doesn't fire. One is not supposed to have to worry
at all about firing rates.
The most important lesson I learned in the Neurobiology classes which
I took was that the "conventional neural-net wisdom" that most AI
researchers keep is not very relevant to real nervous systems. There are
too many important differences, and the AI models are still much too
different from the Neurobiological models to be effectively related. My
semester project for a graduate level AI course was to try and find a
fairly close tie between AI and Neurobiology. I succeeded in finding
interesting correlations in Associative Memory theories, but I was also
very disappointed to find so little in common between
Connectionism/Neural-Nets and Neurobiology besides the very thin
correlation between Connectionist neurons and real neurons.
I get the feeling that the
measurement of synaptic strength is probably a considerably
more difficult task - have people done it at all ? I'd
appreciate any comments that you may have. In the meantime,
I'll look into the kind of references you suggest.
Oh yeah! This area is a big area in terms of research! Realize that
the synaptic strength typically depends on the types of Neurotransmitters
that travel across the synapses. One fairly standard fact that aids this
research is that neurons almost always release only *one* type of
Neurotransmitter (also called an NT). However, many different terminals
(each possibly containing a different NT) can synapse at the same point on
the same dendrite, so there can be very interesting combinational effects.
Furthermore, different neurons will react differently to incoming NT's!
Not too mention the role of Calcium channels in regulating the release of
NT's. Firing rate also figures into this as well. Synaptic strength is a
real complex area!
As for references, *the* introductory textbook which is highly
regarded here at the UW is:
_From_Neuron_to_Brain_ by S. Kuffler, J. Nichols, and A. R. Martin
Published by Sinauer Associates, Inc. (1984)
This is a textbook intended for biologists, so if you are not up on
biology, it may take a while to get through these chapters (don't worry, my
background is light on biology too!). A quick browse through the chapters
yields the following ones which relate to synapses:
Chap. Six: Control of Membrane Permeability
Chap. Eight: Active Transport of Ions
** Chap. Nine: Synaptic Transmission
** Chap. Ten: Release of Chemical Transmitters
** Chap. Eleven: Microphysiology of Chemical Transmission
Chap. Twelve: The Search for Chemical Transmitters
** Chap. Sixteen: Transformation of Information by Synaptic Action
in Individual Neurons
The starred chapters (**'s) are likely to be very relevant. Realize that
this is over 1/3'd of the book, so you can see that Synaptic Transmission
is a hot area!
There are certainly other books. One which I do not own and cannot
remember the name of was a huge, comprehensive book covering the entire
range of Neurobiology, with beautiful illustrations. I would search the
nearest medical library for more information (we are blessed here with
having a wonderful medical lib.). Also, if you can reach some Professors
in Neurobiolgy, they should certainly be able to help you. I am but an
undergrad!
.oO Chris Oo.lishka@uwslh.UUCP (Fish-Guts) (11/12/88)
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"