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Top Document: comp.ai.neural-nets FAQ, Part 1 of 7: Introduction Previous Document: How many kinds of Kohonen networks exist? Next Document: What are cases and variables? See reader questions & answers on this topic! - Help others by sharing your knowledge How to count layers is a matter of considerable dispute. o Some people count layers of units. But of these people, some count the input layer and some don't. o Some people count layers of weights. But I have no idea how they count skip-layer connections. To avoid ambiguity, you should speak of a 2-hidden-layer network, not a 4-layer network (as some would call it) or 3-layer network (as others would call it). And if the connections follow any pattern other than fully connecting each layer to the next and to no others, you should carefully specify the connections. 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: How many kinds of Kohonen networks exist? Next Document: What are cases and variables? 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 1 of 7: Introduction
Section - How are layers counted?
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comp.ai.neural-nets FAQ, Part 1 of 7: Introduction
Section - How are layers counted?
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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.