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Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: What is a neural network (NN)? Next Document: Are there any online books about NNs? See reader questions & answers on this topic! - Help others by sharing your knowledge Several excellent introductory books on NNs are listed in part 4 of the FAQ under "Books and articles about Neural Networks?" If you want a book with minimal math, see "The best introductory book for business executives." Dr. Leslie Smith has a brief on-line introduction to NNs with examples and diagrams at http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html. If you are a Java enthusiast, see Jochen Fröhlich's diploma at http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html For a more detailed introduction, see Donald Tveter's excellent Backpropagator's Review at http://www.dontveter.com/bpr/bpr.html or http://gannoo.uce.ac.uk/bpr/bpr.html, which contains both answers to additional FAQs and an annotated neural net bibliography emphasizing on-line articles. StatSoft Inc. has an on-line Electronic Statistics Textbook, at http://www.statsoft.com/textbook/stathome.html that includes a chapter on neural nets as well as many statistical topics relevant to neural nets. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: What is a neural network (NN)? Next Document: Are there any online books about NNs? Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Part7 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: saswss@unx.sas.com (Warren Sarle)
Last Update March 27 2014 @ 02:11 PM
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PDP++ is a neural-network simulation system written in C++, developed as an advanced version of the original PDP software from McClelland and Rumelhart's "Explorations in Parallel Distributed Processing Handbook" (1987). The software is designed for both novice users and researchers, providing flexibility and power in cognitive neuroscience studies. Featured in Randall C. O'Reilly and Yuko Munakata's "Computational Explorations in Cognitive Neuroscience" (2000), PDP++ supports a wide range of algorithms. These include feedforward and recurrent error backpropagation, with continuous and real-time models such as Almeida-Pineda. It also incorporates constraint satisfaction algorithms like Boltzmann Machines, Hopfield networks, and mean-field networks, as well as self-organizing learning algorithms, including Self-organizing Maps (SOM) and Hebbian learning. Additionally, it supports mixtures-of-experts models and the Leabra algorithm, which combines error-driven and Hebbian learning with k-Winners-Take-All inhibitory competition. PDP++ is a comprehensive tool for exploring neural network models in cognitive neuroscience.