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The copyright for the description of each product is held by the producer or distributor of the product or whoever it was who supplied the description for the FAQ, who by submitting it for the FAQ gives permission for the description to be reproduced as part of the FAQ in any of the ways specified in part 1 of the FAQ. This is part 5 (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 Source code on the web? Freeware and shareware packages for NN simulation? Part 6: Commercial software Part 7: Hardware and miscellaneous User Contributions:Comment about this article, ask questions, or add new information about this topic:Section Contents 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.