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Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: Where is comp.ai.neural-nets archived? Next Document: May I copy this FAQ? See reader questions & answers on this topic! - Help others by sharing your knowledge If your question is not answered in the FAQ, you can try a web search. The following search engines are especially useful: http://www.google.com/ http://search.yahoo.com/ http://www.altavista.com/ http://citeseer.nj.nec.com/cs Another excellent web site on NNs is Donald Tveter's Backpropagator's Review at http://www.dontveter.com/bpr/bpr.html or http://gannoo.uce.ac.uk/bpr/bpr.html. For feedforward NNs, the best reference book is: Bishop, C.M. (1995), Neural Networks for Pattern Recognition, Oxford: Oxford University Press. If the answer isn't in Bishop, then for more theoretical questions try: Ripley, B.D. (1996) Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press. For more practical questions about MLP training, try: Masters, T. (1993). Practical Neural Network Recipes in C++, San Diego: Academic Press. Reed, R.D., and Marks, R.J, II (1999), Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, Cambridge, MA: The MIT Press. There are many more excellent books and web sites listed in the Neural Network FAQ, Part 4: Books, data, etc. 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: Where is comp.ai.neural-nets archived? Next Document: May I copy this FAQ? 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.