ssingh@watserv1.waterloo.edu (The Sanj-Machine aka Ice) (02/02/91)
Could someone tell me if there is any significant difference regarding the properties of neural networks with a finite set of states for connection strengths as opposed to continuous values. Which is more biologically accurate? I always thought that neurons assume one of a finite set of strengths. It is just that it is a very large set, so from our vantage point it appears continuous. I would like to explore the dynamical properties of nonlinear neural networks, so this is important. Thanks in advance for your time. -- "No one had the guts... until now!" $anjay $ingh Fire & "Ice" ssingh@watserv1.[u]waterloo.{edu|cdn}/[ca] ROBOTRON Hi-Score: 20 Million Points | A new level of (in)human throughput... "The human race is inefficient and therefore must be destroyed."-Eugene Jarvis
rao@gabber.kodak.com (Arun Rao) (02/06/91)
In article <1991Feb2.001242.3473@watserv1.waterloo.edu>, ssingh@watserv1.waterloo.edu (The Sanj-Machine aka Ice) writes: ... [stuff deleted ] |> |> I always thought that neurons assume one of a finite set of strengths. It |> is just that it is a very large set, so from our vantage point it |> appears continuous. ... [stuff deleted ] How large is very large ? It appears unlikely to me that neuron activation could possess as much resolution as (say) even a typical binary float representation. I don't remember having seen any numbers, but I would tend to think that if you need more than 8 bits of resolution to get a neural computational model to work, the biological plausibility of such a model is suspect. This is not to say, of course, that biological plausibility should be the acid test in evaluating models, especially application-oriented work. I'd be glad to hear about any experimental evidence that supports a considerably higher resolution in individual neuron activation. -Arun