sfreed@triton.unm.edu (Steven Freed CIRT) (05/16/91)
Newsgroups: bionet.biology.computationa
Path: triton.unm.edu!prentic
From: prentice@triton.unm.edu (John Prentice
Subject: What are the interesting problems
Organization: University of New Mexico, Albuquerqu
Originator: prentice@triton.unm.edu
Message-Id: <1991May14.084100.22397@ariel.unm.edu
Date: Tue, 14 May 91 08:41:00 GM
Lines: 15
Apparently-To: bionet-biology-computational@gatech.edu
I am a computational physicist and not a biologist. I am extremely
experienced with computational mathematics and physics however, including
running a small research group in computational physics. I subscribed to
this newsgroup out of curiosity. We are looking for new areas to get involved
in and computational biology would seem to me to be a rather interesting
one. So, to that end, I would be interested to hear from people what they
consider the major problems in computational biology. Perhaps there are
things from computational physics that could help or things from
computational biology that could help computational physics. There is
too little interchange between fields. Wanna help me change that?
John
---
John K. Prentice john@unmfys.unm.edu (Internet)
Computational Physics Group, Amparo Corporation, Albuquerque, NM, USA
--
--- Moderator ---
Domain: curtiss@umiacs.umd.edu Phillip Curtiss
UUCP: uunet!mimsy!curtiss UMIACS - Univ. of Maryland
Phone: +1-301-405-6710 College Park, Md 20742slehar@park.bu.edu (05/17/91)
> Perhaps there are things from computational physics that could help or > things from computational biology that could help computational > physics. Absolutely! Many times I am sure certain mathematical theorums are proven for one application, and could very well apply to another application if only the proper people knew about it. Let me tell you some of the stuff we do in our department, in the hope that it triggers something in your mind that may be applicable. In the Cognitive and Neuroscience department (CNS) of Boston University we explore theoretical models of natural (neural) computational mechanisms. Although there are others who make detailed models of the incredibly complex dynamics within a single neuron, our emphasis is at a higher, more symbolic level, where we are concerned less with neural spiking dynamics and intracellular mechanisms, and more with the signal processing and information representation issues. In the same way, electrical engineers are concerned with voltages and time delays while computer specialists consider logic gates and boolean operations, where all values are 0 or 1. Our models however are not computer-like boolean mechanisms (as are common in more traditional "AI" {artificial intelligence} models), but rather, the fundamental unit of our model is a dynamic "node", which may represent either the spiking frequency of a single neuron, the activity of a block of neural tissue, or even just an abstract perceptual "notion" that is active in the brain. The activity of this node is represented by a time-varying quantity x, as defined by a differential equation, such as... dx/dt = -Ax + (B-x)E - xI DECAY EXCITATORY INHIBITORY where A is the passive decay (goes slowly to zero without input), E and I are the excitatory and inhibitory inputs (usually a sum of many inputs from neighboring nodes) and (B-x) and x are the "shunting" terms that hold the x value within the bounds between 0 and B (usually B=1) like an analog op-amp that saturates with large inputs. The excitatory and inhibitory connections between such nodes transfer signal from one to another through (theoretical) "synapses" whose conductance can vary slowly over time, depending on the average pre- and post-synaptic activity of the nodes that they connect (there are a variety of "learning" rules). Inspired by neurophysiology, our models are parallel analog mechanisms with multiple criss-crossed connections and feedback loops, which makes for extremely complex dynamics that are difficult to analyze mathematically. Shown below is a typical circuit, with two fields of nodes, F1 and F2, input lines to F1, and interconnections (axons & synapses) between F1 and F2. (Typically such interconnections go from each node in F1 to every node in F2, and often have reciprocal connections from F2 back to F1). The pattern of synaptic "weights" (conductances) determines the transform between the pattern of activations in F1 and F2. F2 ( ) ( ) ( ) ( ) |\ /|\ /|\ /| | \ / | \ / | \ / | | \ / | \ / | \ / | | / | / | / | | / \ | / \ | / \ | | / \ | / \ | / \ | |/ \|/ \|/ \| F1 ( ) ( ) ( ) ( ) ^ ^ ^ ^ | | | | INPUTS For example, each node in F2 might respond to a particular pattern of activations in F1 by virtue of that pattern being represented in the F1 -> F2 synaptic weights, so that presentation of that pattern at the inputs lights up the corresponding node in F2, thus performing a "recognition" operation at F2. Competition within F2 (through lateral inhibitory connections, not shown) would effect a "choice" between competing interpretations of ambiguous patterns, and feedback connections would complete missing elements or remove extraneous elements of the pattern at F1 to make it conform more closely to the patterns stored in the "memory" of the synaptic weights. Mathematically speaking, this dynamic system, given initial conditions (with the inputs on) will tend to settle into stable states (points in state-space) where each stable point represents a "memory". A lot of our mathematical analysis is devoted to ensuring that such systems are stable (will not search for ever) and examine their capacity to store patterns, learn patterns, generalize by clustering similar patterns to the same stable state, stabilize existing memories while maintaining the plasticity to learn new memories, and so forth. We also use such models to simulate specific neurocomputational mechanisms in vision, motor control, and conditioning, which can be tested against psychophysical data. Such models are thus perceptual or behavioral models, rather than neurological models, since they simulate and reproduce perceptual and behavioral phenomena rather than neural responses. So, does this kind of model, a parallel analog dynamic system, bring to mind similar dynamics in other physical systems, and are there any interesting mathematical analyses of such systems that might pertain to our kind of models? -- (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) (O)((O))((( slehar@park.bu.edu )))((O))(O) (O)((O))((( Steve Lehar Boston University Boston MA )))((O))(O) (O)((O))((( (617) 424-7035 (H) (617) 353-6741 (W) )))((O))(O) (O)((O))(((O)))((((O))))(((((O)))))(((((O)))))((((O))))(((O)))((O))(O) -- --- Moderator --- Domain: curtiss@umiacs.umd.edu Phillip Curtiss UUCP: uunet!mimsy!curtiss UMIACS - Univ. of Maryland Phone: +1-301-405-6710 College Park, Md 20742
evensen@husc9.harvard.edu (05/18/91)
In-reply-to: sfreed@triton.unm.edu's message of 15 May 91 23:38:12 GMT
I'd like to add a "me-too" to this query. I'm a first year grad
student in biophysics and am looking for perspectives on what are
interesting, important, pertinent, etc. problems in biology that can
be addressed computationally. One of the things I'm particularly
interested in is the application of computer graphics to solving
problems. Post your ideas or mail them to me and I'll compile a
summary.
Thanks...
--Erik (evensen@husc9.harvard.edu)
--
--- Moderator ---
Domain: curtiss@umiacs.umd.edu Phillip Curtiss
UUCP: uunet!mimsy!curtiss UMIACS - Univ. of Maryland
Phone: +1-301-405-6710 College Park, Md 20742