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Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. Answers provided by other authors as cited below are copyrighted by those authors, who by submitting the answers for the FAQ give permission for the answer to be reproduced as part of the FAQ in any of the ways specified in part 1 of the FAQ. This is part 7 (of 7) of a monthly posting to the Usenet newsgroup comp.ai.neural-nets. See the part 1 of this posting for full information what it is all about. ========== Questions ========== ******************************** Part 1: Introduction Part 2: Learning Part 3: Generalization Part 4: Books, data, etc. Part 5: Free software Part 6: Commercial software Part 7: Hardware and miscellaneous Neural Network hardware? What are some applications of NNs? General Agriculture Automotive Chemistry Criminology Face recognition Finance and economics Games, sports, gambling Industry Materials science Medicine Music Robotics Weather forecasting Weird What to do with missing/incomplete data? How to forecast time series (temporal sequences)? How to learn an inverse of a function? How to get invariant recognition of images under translation, rotation, etc.? How to recognize handwritten characters? What about pulsed or spiking NNs? What about Genetic Algorithms and Evolutionary Computation? What about Fuzzy Logic? Unanswered FAQs Other NN links? User Contributions:Comment about this article, ask questions, or add new information about this topic:Section Contents
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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.