Path: utzoo!utgpu!news-server.csri.toronto.edu!mailrus!cs.utexas.edu!uwm.edu!ogicse!emory!hubcap!ncrcae!usceast!park From: park@usceast.UUCP (Kihong Park) Newsgroups: comp.theory.cell-automata Subject: Re: cellular automata and neural networks Message-ID: <3199@usceast.UUCP> Date: 14 Apr 90 17:52:41 GMT References: Organization: University of South Carolina, Columbia Lines: 45 In article baker2@husc9.harvard.edu (James Baker) writes: >Has anyone constructed ``cellular automata'' that learn? You have to remember that certain types of neural networks can be suitably represented as cellular automata. It is advantageous that the NN be discrete, and have nontotal connectivity. But these are not absolute requirements. You will need to have a cell type which encodes the weights, and another cell type which encodes the neuron. Again, it's not necessary to view as there being two cell types because you can always merge them as one --- a standard trick. If your neurons are modeled to have continuous transfer functions, this is remedied by incorporating a table of the functional values with as accurate finite quantization level as one pleases. >There seems to be some good reasons to explore this possibility: > >1. Arbitrary connections make analyzing neural networks difficult, if >not just impossible. I don't quite understand your statement. Even though CAs are "simpler" systems than NNs, it is very often the case that the latter are analytically more tractable. That's why you have all this hype surrounding NNs. People feel that they understand their neural nets to a certain degree. It makes them semi-confident design/engineers. >2. Cellular automata models are more readily simulated on hyper cube >parallel architectures than conventional neural networks. Since, as you point out, the CA's neighborhood is often times quite limited, CAs can be more easily simulated on parallel machines such as hypercubes. But in many implementations of NNs on multi-processor machines, in effect, a morphism to a cellular automata structure is performed anyway. So, even though there exist BIG differences between CAs and NNs, and they are studied in different contexts, one has to always remeber that they are close cousins. >Since one would train these models, they would not be cellular automata >in the strict sense; for example, they might use some global reward >signal or noise, in addition to receiving input and target data. There is a field called systolic arrays which deals with practical design issues in implementing "easy"-to-parallelize algorithms on CA-like environments. There exist terminologies such as "global broadcasting" which would suggest terms you may be looking for. Kihong Park. (park@cs.scarolina.edu)