Free neural software for linux

Java Neural Network Trainer is a neural network trainer with the ability to easily add new training algorithms and training patterns.

Java Neural Network Trainer project includes a parallel training graphical interface where you can view each trainer working in real-time in parallel.

AI::NeuralNet::BackProp is a simple back-prop neural net that uses Deltas and Hebbs rule.

SYNOPSIS

use AI::NeuralNet::BackProp;
# Create a new network with 1 layer, 5 inputs, and 5 outputs.
my $net = new AI::NeuralNet::BackProp(1,5,5);

# Add a small amount of randomness to the network
$net->random(0.001);

# Demonstrate a simple learn() call
my @inputs = ( 0,0,1,1,1 );
my @ouputs = ( 1,0,1,0,1 );

print $net->learn(@inputs, @outputs),"n";

# Create a data set to learn
my @set = (
[ 2,2,3,4,1 ], [ 1,1,1,1,1 ],
[ 1,1,1,1,1 ], [ 0,0,0,0,0 ],
[ 1,1,1,0,0 ], [ 0,0,0,1,1 ]
);

# Demo learn_set()
my $f = $net->learn_set(@set);
print "Forgetfulness: $f unitn";

# Crunch a bunch of strings and return array refs
my $phrase1 = $net->crunch("I love neural networks!");
my $phrase2 = $net->crunch("Jay Lenno is wierd.");
my $phrase3 = $net->crunch("The rain in spain...");
my $phrase4 = $net->crunch("Tired of word crunching yet?");

# Make a data set from the array refs
my @phrases = (
$phrase1, $phrase2,
$phrase3, $phrase4
);

# Learn the data set
$net->learn_set(@phrases);

# Run a test phrase through the network
my $test_phrase = $net->crunch("I love neural networking!");
my $result = $net->run($test_phrase);

# Get this, it prints "Jay Leno is networking!" ... LOL!
print $net->uncrunch($result),"n";

AI::NeuralNet::BackProp is the flagship package for this file. It implements a nerual network similar to a feed-foward, back-propagtion network; learning via a mix of a generalization of the Delta rule and a disection of Hebbs rule. The actual neruons of the network are implemented via the AI::NeuralNet::BackProp::neuron package

Fast Artificial Neural Network Library implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks.
Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, Python, Delphi and Mathematica bindings are available.
Main features:
- Multilayer Artificial Neural Network Library in C
- Backpropagation training (RPROP, Quickprop, Batch, Incremental)
- Evolving topology training which dynamically builds and trains the ANN (Cascade2)
- Easy to use (create, train and run an ANN with just three function calls)
- Fast (up to 150 times faster execution than other libraries)
- Versatile (possible to adjust many parameters and features on-the-fly)
- Well documented (An easy to use reference manual, a 50+ page university report describing the implementation considerations etc. and an introduction article)
- Cross-platform (configure script for linux and unix, dll files for windows, project files for MSVC++ and Borland compilers are also reported to work)
- Several different activation functions implemented (including stepwise linear functions for that extra bit of speed)
- Easy to save and load entire ANNs
- Several easy to use examples (simple train example and simple test example)
- Can use both floating point and fixed point numbers (actually both float, double and int are available)
- Cache optimized (for that extra bit of speed)
- Open source (licenced under LGPL)
- Framework for easy handling of training data sets
- Graphical Interface
- C++ Bindings
- PHP Extension
- Python Bindings
- Delphi Bindings
- .NET Bindings
- Mathematica Extension
- Octave Extension
- Ruby Bindings
- Pure Data Bindings
- Debian package

Gnaural is a multi-platform programmable binaural-beat generator, implementing the principles described in October 1973 Scientific American, Gerald Oster, "Auditory Beats in the Brain."

There has been considerable research done on the subject since that publication, and Gnaurals Windows-based predecessor, WinAural, has been used as the audio stimulus in at least one published study, "The Induced Rhythmic Oscillations of Neural Activity in the Human Brain", D. Cvetkovic, D. Djuwari, I. Cosic (Australia), from Proceeding (417) Biomedical Engineering - 2004.

The central finding of Osters article: brain activity can be entrained to the auditory beat frequencies created when each ear is presented simultaneously with tones of slightly offset in frequency. My interest has been exploring how this effect (known as "frequency following response" or "brainwave entrainment") can be used to explore mental states, ranging from profoundly meditative to highly alert.

What are auditory binaural beats?

In 1839, German experimenter Heinrich Wilhelm Dove discovered that playing two tones simultaneously, one in to each ear, induced the perception of a "beat frequency" when the tones were of slightly differing frequency (generally less than 100 Hz apart).

While an acoustic mixing of the two tones will also produce a beat frequency, what is notable about auditory binaural beats is that there is no acoustic mixing of the tones: the beats exist solely within the auditory system. Some researchers believe that they are an artifact of the "neural wiring" used to spatially determine the origins of sounds in our environment.

Gerald Osters breakthrough in 1973 was to observe that the neural processing associated with binaural beats can induce an overall entrainment of brainwave activity (essentially, an oscillation between the two hemispheres in sync with the beat frequency). The neurology of this phenomenon is, according to Oster, tied to the contralateral integration of auditory input taking place in the superior olivary nucleus in the brainstem.

My main interest in the principle has been the possibility that brainwave entrainment can be used to target specific mental states. Gnaural has a long lineage, starting with a DOS program in the mid 1990s, progressing to WinAural for Windows and (in the hope a making a cross-platform solution) BrainJav for Java, and finally the truly cross-platform solution in Gnaural. In over a decade of experience with the technique, I have mainly found it useful for slowing-down brain activity. In that capacity, it has served me in areas ranging from stress reduction, sleep make-up, and particularly as a sort of "poor mans meditation", requiring almost no effort to achieve states of mind that I usually have found rather hard to achieve with real meditation.

But these are strictly my observations, and I make no guarantees about what the technique can do for anyone else. Some of the more unusual applications Ive heard about for my software include sustaining a heightened mental focus for online tournament gaming, and enhancing flotation-tank and related sensory deprivation environments. Many people also apparently use the technique to study more effectively.

One of the stranger facts regarding binaural beats is their seeming ability to be equally-at-home in the laboratory setting as in "grass roots" contexts (such as alternative medicine and the New Age phenomenon). That there is a grass-roots enthusiasm (easily demonstrated by googling binaural beats) is probably related to the sense of promise inherent in an easy-to-implement technique offering the possibility of direct influence of brain behavior. But from a scientific standpoint, there is a big gap between claims (of what binaural beats can do) and corroboration (by scientific method), which has led to a sense in many that the actual subject of binaural beats is "controversial." But this is an irrational response, given that the actual scientific/laboratory basis of binaural beats has remained an established part of the scientific literature for over 30 years.

One of my goals in writing Gnaural was to implement the binaural beat principle within the bounds of my understanding of the established scientific facts regarding the subject. To some extent, this has meant leaving-out many of the "bells and whistles" prevailent in other implementations. That my software has been used for at least one published "hard-science" study suggests that it has been somewhat successful. However, I also hope that Gnaural proves useful for people who wish to explore subjective areas unfettered by scientific rigor. In a subject dealing with matters of the mind, I see both sides -- "grass-roots empiricism" and "scientific empiricism" -- as being complementary halves of a complete investigation of the possibilities, and ultimately, Id hope to see the two sides of our culture be catalysts for each other, rather than inhibited by a mutual antagonism.

scnANNlib is a Sentinel Chicken Networks Artificial Neural Network Library.

Documentation on what is what can be found in README.files. Currently, the API specification and documentation can only be found in the source of the header files (in lib/), but this will be improved in later releases.

If you have questions about how to use the Makefiles, see README.make.

For more information about the development project, and to obtain newer versions of the library, visit http://www.sentinelchicken.org/ .

Joone project is a FREE Neural Network framework to create, train and test artificial neural networks. The aim is to create a powerful environment both for enthusiastic and professional users, based on the newest Java technologies.
Joone is composed by a central engine that is the fulcrum of all applications that are developed with Joone. Joones neural networks can be built on a local machine, be trained on a distributed environment and run on whatever device.
Everyone can write new modules to implement new algorithms or new architectures starting from the simple components distributed with the core engine. The main idea is to create the basis to promote a zillion of AI applications that revolve around the core framework.
Main features:
Architecture
- The Joones framework is built with a modular architecture: the core engine is separated from the visual interface and permits easily to implement any new application based on it.
- Joone is portable, being written in 100% pure Java. It can run in any environment, from big multiprocessor machines to small palmtop devices.
Neural Networks usability and transportation
- The neural networks based on Joone are usable stand-alone (separated from the framework that has created or trained them).
- The Joones based neural networks can be transported using common protocols (like http or ftp) to run on remote machines
Framework expandability
- The framework is expandable with more components to implement new learning algorithms or new architectures.
- With Joone its possible to implement any kind of optimization; there are two main methods to find the best solution to a given problem (i.e. to find the best neural network): local optimization and global optimization techniques. The local optimization is obtained applying some internal mechanism (the most famous is the momentum), the global optimization, instead, try to find the best solution applying some external technique to select the best performing NN among a predefined group of NNs (like genetic algorithms). Both are implemented with Joone, and many new optimization techniques can be experimented thanks to its expansibility.
Multithreading and scalability
- Joones core engine is based on a multithreaded engine, capable to scale using all the computing resources available.
- Joone provides the professional users with a distributed environment to train many neural networks in parallel on several machines.
Licensing
- Joone is freely usable. Its license is the Lesser General Public License (LGPL).
- Youre encouraged to try it and use it for whatever (both commercial and academic) application.
Whats New in 1.2.1 Stable Release:
- This release adds support for the Groovy scripting language.
- LogarithmicPlugin has been added, to apply a logarithmic transformation (base e) to input data. "Save as XML" has been added to the GUI Editor, in order to permit saving a neural network in XML format.
- A number of bugs have been fixed, including a problem that prevented SangerSynapse from learning when in training mode.
- The inspection panel no longer shows the biases for Layers for which this doesnt make sense.
- This release fixes the lack of the first column when the inspected values were copied in the clipboard.
Whats New in 2.0.0RC1 Development Release:
- Performance was improved by 50%, thanks to heavy refactoring of the core engine, by adding the single-thread mode.
- A set of tools was added in order to hide the complexity of the API.
- A new Image I/O component has been added in order read and write directly from/to image files.
- A new SoftMax Layer has been added to build neural networks able to resolve 1 of C classification problems.
- The usability of the property panels has been improved by adopting a new file chooser panel and a visual calendar panel.
- Several bugs were fixed and the documentation has been updated with the new features.