Path: utzoo!utgpu!news-server.csri.toronto.edu!rutgers!att!bu.edu!slehar From: slehar@thalamus.bu.edu (Steve Lehar) Newsgroups: comp.ai.neural-nets Subject: Re: Defining a Nerual Network Message-ID: Date: 18 Nov 90 20:29:11 GMT References: <2491@bimacs.BITNET> <6904@jhunix.HCF.JHU.EDU> <1990Nov18.140416.10297@lut.fi> Sender: news@bu.edu.bu.edu Organization: Boston University Center for Adaptive Systems Lines: 55 In-reply-to: pako@lut.fi's message of 18 Nov 90 14:04:16 GMT Let me try my hand at this one... NEURAL NETWORKS =============== A neural network is a computational model that is inspired by observation of natural computational mechanisms. Natural architectures are fundamentally different from conventional architectures in that they tend to represent information in a distributed way, and to perform computation in a parallel analog manner that seems to be more fault tolerant and robust if the input information is somewhat ambiguous. Neural approaches work best in applications where traditional computation has performed poorly, usually because the data is ambiguous or the context has a large influence on the data, such as vision, speech and cognition. They generally perform poorly in realms where computers perform well, usually because the data is deterministic, clearly defined and well understood, such as word processors, spreadsheets, arithmetic computation. SPECIAL NOTE to the "there's no such thing as neural networks" folks: ============ Since most neural networks are implemented by computer simulation, (except the real ones, that is) there is of course some overlap between "neural" and "non-neural" models; very simple neural systems are similar to non-neural equations, and very large networks of conventional systems are sometimes similar to very small neural networks. The difference is really in the inspiration of the model- neural models tend towards simple computational units and lots of them, whereas conventional architectures have much more complicated units and less of them. The need for the separate term "neural" is that this approach has been so counterintuitive that it took a couple of decades of bashing our heads against certain insoluable problems to realize that the way it is done in the brain is very different from the way it is done in sequential computers. The reason conventional computers were initially so popular is that theirs is a more obvious, predictable and direct road to the solution (IF this AND that THEN theother) than the neural way (IF some of these AND some of those AND I'm in the right mood THEN perhaps a bit of theother). There are those who claim that so-and-so's neural model is nothing more than such-and-such a mathematical procedure, and therefore neural networks don't exist. To those I say, since the geometrical solution to a problem can also be solved algebraeically, therefore geometry (or algebra, take your pick) doesn't exist. Many mathematical problems can be solved in a variety of ways, which can be shown ultimately to be equivalent. It just happens that certain classes of problems are more easily solved with one technique than another, so it is important to match your mathematical tools to the nature of your problem to get the most results for the least effort. -- (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)