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Top Document: comp.ai.neural-nets FAQ, Part 2 of 7: Learning Previous Document: Should I nonlinearly transform the data? Next Document: What is ART? See reader questions & answers on this topic! - Help others by sharing your knowledge The answer to this question is rather long and so is not included directly in the posted FAQ. See ftp://ftp.sas.com/pub/neural/importance.html. Also see Pierre van de Laar's bibliography at ftp://ftp.mbfys.kun.nl/snn/pub/pierre/, but don't believe everything you read in those papers. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: comp.ai.neural-nets FAQ, Part 2 of 7: Learning Previous Document: Should I nonlinearly transform the data? Next Document: What is ART? 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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comp.ai.neural-nets FAQ, Part 2 of 7: Learning
Section - How to measure importance of inputs?
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comp.ai.neural-nets FAQ, Part 2 of 7: Learning
Section - How to measure importance of inputs?
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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.