Neural Java
Neural Networks Tutorial with Java
Applets

Introduction
Neural Java is a series of exercises and demos. Each exercise consists
of a short introduction, a small demonstration program written in Java (Java
Applet), and a series of questions which are intended as an invitation to
play with the programs and explore the possibilities of different algorithms.
The aim of the applets is to illustrate the dynamics of different artificial
neural networks. Emphasis has been put on visualization and interactive interfaces.
The Java Applets are not intended for and not useful for largescale
applications! Users interested in application programs should use other simulators.
The list below covers standard neural network algorithms like BackProp,
Kohonen, and the Hopfield model. It also includes some models that are
more biological, and features visualizations of the HodgkinHuxley and the
integrateandfire models.
Additional material
The following are available for download:
See also
Exercises
If there is this image
on the right of the link, then you can download the applet in order to execute
it at your place. Moreover you can download the source code of the applet.
But you must agree before with the GNU General Public
Licence.
If so follow the instructions here to download
and install the applets.
Single Neurons
 Artificial Neuron.
 McCullochPitts Neuron.
 Spiking Neuron. (Requires Swing).
 HodgkinHuxley Model.
 Axons and Action Potential Propagation.
Supervised Learning
Singlelayer networks (simple perceptrons)
 Perceptron Learning Rule.
 Adaline, Perceptron and Backpropagation.
Multilayer networks
 Multilayer Perceptron (with neuron
outputs in {0;1}).
 Multilayer Perceptron (with neuron
outputs in {1;1}).
 Multilayer Perceptron and C language.
 Generalization in Multilayer
Perceptrons (with neuron outputs in {0;1}).
 Generalization in Multilayer
Perceptrons (with neuron outputs in {1;1}).
 Optical Character Recognition with
Multilayer Perceptron.
 Prediction with Multilayer Perceptron.
Support Vector Machine
 Support Vector Machine with a polynomial kernel.
Density Estimation and Interpolation
 Radial Basis Function Network.
 Gaussian Mixture Model / EM.
 Mixture model, using unlabeled
data
Unsupervised Learning
 Principal Component Analysis.
 PCA for Character Recognition.
 Competitive Learning Methods.
Reinforcement Learning
 Blackjack and Reinforcement Learning.
Network Dynamics
 Hopfield Network.
 Pseudoinverse Network.
 Network of spiking neurons. (Requires Swing).
 Retina Simulation. (Runs very
slow with some netscape versions).
URL: http://lcn.epfl.ch/tutorial/english/
Last updated: 06October2000 by Sébastien
Baehni