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   all neural network problems. It is lightning fast and can be used in
   conjunction with many different programming languages. It does not
   require iterative learning, has no limits in variables (input and output
   neurons), no limits in sample size. It is not sensitive toward noise in
   the data. The database can be changed dynamically. It provides a way to
   estimate the rate of error in your prediction. It has a graphical
   spreadsheet-like user interface. The AINET manual (more than 100 pages)
   is divided into: "User's Guide", "Basics About Modeling with the AINET",
   "Examples", "The AINET DLL library" and "Appendix" where the theoretical
   background is revealed. You can get a full working copy from: 
   http://www.ainet-sp.si/ 

28. DemoGNG
+++++++++++

   This simulator is written in Java and should therefore run without
   compilation on all platforms where a Java interpreter (or a browser with
   Java support) is available. It implements the following algorithms and
   neural network models: 
    o Hard Competitive Learning (standard algorithm) 
    o Neural Gas (Martinetz and Schulten 1991) 
    o Competitive Hebbian Learning (Martinetz and Schulten 1991, Martinetz
      1993) 
    o Neural Gas with Competitive Hebbian Learning (Martinetz and Schulten
      1991) 
    o Growing Neural Gas (Fritzke 1995) 
   DemoGNG is distributed under the GNU General Public License. It allows to
   experiment with the different methods using various probability
   distributions. All model parameters can be set interactively on the
   graphical user interface. A teach modus is provided to observe the models
   in "slow-motion" if so desired. It is currently not possible to
   experiment with user-provided data, so the simulator is useful basically
   for demonstration and teaching purposes and as a sample implementation of
   the above algorithms. 

   DemoGNG can be accessed most easily at 
   http://www.neuroinformatik.ruhr-uni-bochum.de/ in the file 
   /ini/VDM/research/gsn/DemoGNG/GNG.html where it is embedded as Java
   applet into a Web page and is downloaded for immediate execution when you
   visit this page. An accompanying paper entitled "Some competitive
   learning methods" describes the implemented models in detail and is
   available in html at the same server in the directory 
   ini/VDM/research/gsn/JavaPaper/. 

   It is also possible to download the complete source code and a Postscript
   version of the paper via anonymous ftp from
   ftp.neuroinformatik.ruhr-uni-bochum [134.147.176.16] in directory
   /pub/software/NN/DemoGNG/. The software is in the file 
   DemoGNG-1.00.tar.gz (193 KB) and the paper in the file sclm.ps.gz (89
   KB). There is also a README file (9 KB). Please send any comments and
   questions to demogng@neuroinformatik.ruhr-uni-bochum.de which will reach
   Hartmut Loos who has written DemoGNG as well as Bernd Fritzke, the author
   of the accompanying paper. 

29. Trajan 2.1 Shareware
++++++++++++++++++++++++

   Trajan 2.1 Shareware is a Windows-based Neural Network simulation
   package. It includes support for the two most popular forms of Neural
   Network: Multilayer Perceptrons with Back Propagation and Kohonen
   networks.

   Trajan 2.1 Shareware concentrates on ease-of-use and feedback. It
   includes Graphs, Bar Charts and Data Sheets presenting a range of
   Statistical feedback in a simple, intuitive form. It also features
   extensive on-line Help.

   The Registered version of the package can support very large networks (up
   to 128 layers with up to 8,192 units each, subject to memory limitations
   in the machine), and allows simple Cut and Paste transfer of data to/from
   other Windows-packages, such as spreadsheet programs. The Unregistered
   version features limited network size and no Clipboard Cut-and-Paste.

   There is also a Professional version of Trajan 2.1, which supports a
   wider range of network models, training algorithms and other features.

   See Trajan Software's Home Page at http://www.trajan-software.demon.co.uk
   for further details, and a free copy of the Shareware version.

   Alternatively, email andrew@trajan-software.demon.co.uk for more details.

30. Neural Networks at your Fingertips
++++++++++++++++++++++++++++++++++++++

   "Neural Networks at your Fingertips" is a package of ready-to-reuse
   neural network simulation source code which was prepared for educational
   purposes by Karsten Kutza. The package consists of eight programs, each
   of which implements a particular network architecture together with an
   embedded example application from a typical application domain.
   Supported network architectures are 
    o Adaline, 
    o Backpropagation, 
    o Hopfield Model, 
    o Bidirectional Associative Memory, 
    o Boltzmann Machine, 
    o Counterpropagation, 
    o Self-Organizing Map, and 
    o Adaptive Resonance Theory. 
   The applications demonstrate use of the networks in various domains such
   as pattern recognition, time-series forecasting, associative memory,
   optimization, vision, and control and include e.g. a sunspot prediction,
   the traveling salesman problem, and a pole balancer.
   The programs are coded in portable, self-contained ANSI C and can be
   obtained from the web pages at 
   http://www.geocities.com/CapeCanaveral/1624. 

31. NNFit
+++++++++

   NNFit (Neural Network data Fitting) is a user-friendly software that
   allows the development of empirical correlations between input and output
   data. Multilayered neural models have been implemented using a
   quasi-newton method as learning algorithm. Early stopping method is
   available and various tables and figures are provided to evaluate fitting
   performances of the neural models. The software is available for most of
   the Unix platforms with X-Windows (IBM-AIX, HP-UX, SUN, SGI, DEC, Linux).
   Informations, manual and executable codes (english and french versions)
   are available at http://www.gch.ulaval.ca/~nnfit
   Contact: Bernard P.A. Grandjean, department of chemical engineering,
   Laval University; Sainte-Foy (Quibec) Canada G1K 7P4;
   grandjean@gch.ulaval.ca 

32. Nenet v1.0
++++++++++++++

   Nenet v1.0 is a 32-bit Windows 95 and Windows NT 4.0 application designed
   to facilitate the use of a Self-Organizing Map (SOM) algorithm. 

   The major motivation for Nenet was to create a user-friendly SOM
   algorithm tool with good visualization capabilities and with a GUI
   allowing efficient control of the SOM parameters. The use scenarios have
   stemmed from the user's point of view and a considerable amount of work
   has been placed on the ease of use and versatile visualization methods. 

   With Nenet, all the basic steps in map control can be performed. In
   addition, Nenet also includes some more exotic and involved features
   especially in the area of visualization. 

   Features in Nenet version 1.0: 
    o Implements the standard Kohonen SOM algorithm 
    o Supports 2 common data preprocessing methods 
    o 5 different visualization methods with rectangular or hexagonal
      topology 
    o Capability to animate both train and test sequences in all
      visualization methods 
    o Labelling 
       o Both neurons and parameter levels can be labelled 
       o Provides also autolabelling 
    o Neuron values can be inspected easily 
    o Arbitrary selection of parameter levels can be visualized with Umatrix
      simultaneously 
    o Multiple views can be opened on the same map data 
    o Maps can be printed 
    o Extensive help system provides fast and accurate online help 
    o SOM_PAK compatible file formats 
    o Easy to install and uninstall 
    o Conforms to the common Windows 95 application style - all
      functionality in one application 

   Nenet web site is at: 
   http://www.mbnet.fi/~phodju/nenet/Nenet/General.html The web site
   contains further information on Nenet and also the downloadable Nenet
   files (3 disks totalling about 3 Megs) 

   If you have any questions whatsoever, please contact: Nenet-Team@hut.fi
   or phassine@cc.hut.fi 

33. Machine Consciousness Toolbox
+++++++++++++++++++++++++++++++++

   See listing for Machine Consciousness Toolbox in part 6 of the FAQ. 

34. NICO Toolkit (speech recognition)
+++++++++++++++++++++++++++++++++++++

         Name: NICO Artificial Neural Network Toolkit
       Author: Nikko Strom
      Address: Speech, Music and Hearing, KTH, S-100 44, Stockholm, Sweden
        Email: nikko@speech.kth.se
          URL: http://www.speech.kth.se/NICO/index.html
    Platforms: UNIX, ANSI C; Source code tested on: HPUX, SUN Solaris, Linux
        Price: Free

   The NICO Toolkit is an artificial neural network toolkit designed and
   optimized for automatic speech recognition applications. Networks with
   both recurrent connections and time-delay windows are easily constructed.
   The network topology is very flexible -- any number of layers is allowed
   and layers can be arbitrarily connected. Sparse connectivity between
   layers can be specified. Tools for extracting input-features from the
   speech signal are included as well as tools for computing target values
   from several standard phonetic label-file formats. 

   Algorithms: 
    o Back-propagation through time, 
    o Speech feature extraction (Mel cepstrum coefficients, filter-bank) 

35. SOM Toolbox for Matlab 5
++++++++++++++++++++++++++++

   SOM Toolbox, a shareware Matlab 5 toolbox for data analysis with
   self-organizing maps is available at the URL 
   http://www.cis.hut.fi/projects/somtoolbox/. If you are interested in
   practical data analysis and/or self-organizing maps and have Matlab 5 in
   your computer, be sure to check this out! 

   Highlights of the SOM Toolbox include the following: 
    o Tools for all the stages of data analysis: besides the basic SOM
      training and visualization tools, the package includes also tools for
      data preprocessing and model validation and interpretation. 
    o Graphical user interface (GUI): the GUI first guides the user through
      the initialization and training procedures, and then offers a variety
      of different methods to visualize the data on the trained map. 
    o Modular programming style: the Toolbox code utilizes Matlab
      structures, and the functions are constructed in a modular manner,
      which makes it convenient to tailor the code for each user's specific
      needs. 
    o Advanced graphics: building on the Matlab's strong graphics
      capabilities, attractive figures can be easily produced. 
    o Compatibility with SOM_PAK: import/export functions for SOM_PAK
      codebook and data files are included in the package. 
    o Component weights and names: the input vector components may be given
      different weights according to their relative importance, and the
      components can be given names to make the figures easier to read. 
    o Batch or sequential training: in data analysis applications, the speed
      of training may be considerably improved by using the batch version. 
    o Map dimension: maps may be N-dimensional (but visualization is not
      supported when N > 2 ). 

36. FastICA package for MATLAB
++++++++++++++++++++++++++++++

   The FastICA algorithm for independent component analysis. 

   Independent component analysis, or ICA, is neural network or signal
   processing technique that represents a multidimensional random vector as
   a linear combination of nongaussian random variables ('independent
   components') that are as independent as possible. ICA is a nongaussian
   version of factor analysis, and somewhat similar to principal component
   analysis. ICA has many applications in data analysis, source separation,
   and feature extraction. 

   The FastICA algorithm is a computationally optimized method for
   performing the estimation of ICA. It uses a fixed-point iteration scheme
   that has been found in independent experiments to be 10-100 times faster
   than conventional gradient descent methods for ICA. Another advantage of
   the FastICA algorithm is that it can be used to estimate the independent
   components one-by-one, as in projection pursuit, which is very practical
   in exploratory data analysis. 

   The FastICA package for MATLAB (versions 5 or 4) is freeware package with
   a graphical user interface that implements the fixed-point algorithm for
   ICA. The package is available on the Web at 
   http://www.cis.hut.fi/projects/ica/fastica/.
   Email contact: Aapo Hyvarinen  

37. NEXUS: Large-scale biological simulations
+++++++++++++++++++++++++++++++++++++++++++++

   Large-scale biological neural network simulation engine. Includes
   automated network construction tool that allows extremely complex
   networks to be generated according to user-supplied architectural
   specifications. 

   The network engine is an attempt at creating a biological neural network
   simulator. It consists of a C++ class, called "network". A network object
   houses a set of objects of another C++ class, called "neuron". The neuron
   class is a detailed functional simulation of a neuron (i.e. the actual
   chemical processes that lead to a biological neuron's behavior are not
   modeled explicitly, but the behavior itself is). The simulation of the
   neuron is handled entirely by the neuron class. The network class
   coordinates the functioning of the neurons that make up the neural
   network, as well as providing addressing services that allow the neurons
   to interact. It is also responsible for facilitating the interface of the
   neural network it houses onto any existing software into which the neural
   network is to be integrated. 

   Since a simulated neural network consisting of a large number of heavily
   interconnected neurons is extremely difficult to generate manually, NEXUS
   was developed. To create a network with NEXUS, one need only describe the
   network in general terms, in terms of groups of sets of specifically
   arranged neurons, and how the groups interface onto each other and onto
   themselves. This information constitutes a network architecture
   descriptor. A network architecture descriptor is read by NEXUS, and NEXUS
   uses the information to generate a network, building all the neurons and
   connecting them together appropriately. This system is analogous to
   nature's brain construction system. For example, human brains, in
   general, are very similar. The basic design is stored in human DNA. Since
   it is certainly not possible to record information about each neuron and
   its connections, DNA must instead contain (in some form) what is
   essentially a set of guidelines, a set of rules about how the brain is to
   be laid out. These guidelines are used to build the brain, just like
   NEXUS uses the guidelines set out in the network architecture descriptor
   to build the simulated neural network. 

   NEXUS and the network engine have deliberately been engineered to be
   highly efficient and very compact. Even so, large, complex networks
   require tremendous amounts of memory and processing power. 

   The network engine: 
    o flexible and elegant design; highly customizable simulation
      parameters; extremely efficient 
    o throughout, nonlinear magnitude decay modeling 
    o dendritic tree complexity and network connection density limited only
      by the computer hardware 
    o simulation of dendritic logic gate behaviors via a sophisticated
      excitation thresholding and conduction model 
    o detailed simulation of backprop, allowing realistic simulation of
      associated memory formation processes 
    o simulation of all known postsynaptic memory formation mechanisms (STP,
      STD, LTP, LTD) 
    o dynamic presynaptic output pattern modeling, including excitation
      magnitude dependent output pattern selection 
    o simulation of all known presynaptic activity-based output modifiers
      (PPF, PTP, depression) 

   NEXUS: 
    o allows networks to be designed concisely and as precisely as is
      necessary 
    o makes massively complex large-scale neural network design and
      construction possible 
    o allows existing networks to be augmented without disturbing existing
      network structure 
    o UNIX and Win32 compatible 

   URL: http://www.sfu.ca/~loryan/neural.html
   Email: Lawrence O. Ryan 

38. Netlab: Neural network software for Matlab
++++++++++++++++++++++++++++++++++++++++++++++

   http://www.ncrg.aston.ac.uk/netlab/index.html 

   The Netlab simulation software is designed to provide the central tools
   necessary for the simulation of theoretically well founded neural network
   algorithms for use in teaching, research and applications development. It
   consists of a library of Matlab functions and scripts based on the
   approach and techniques described in Neural Networks for Pattern
   Recognition by Christopher M. Bishop, (Oxford University Press, 1995).
   The functions come with on-line help, and further explanation is
   available via HTML files. 

   The Netlab library includes software implementations of a wide range of
   data analysis techniques. Netlab works with Matlab version 5.0 and
   higher. It is not compatible with earlier versions of Matlab. 

39. NuTank
++++++++++

   NuTank stands for NeuralTank. It is educational and entertainment
   software. In this program one is given the shell of a 2 dimentional
   robotic tank. The tank has various I/O devices like wheels, whiskers,
   optical sensors, smell, fuel level, sound and such. These I/O sensors are
   connected to Neurons. The player/designer uses more Neurons to
   interconnect the I/O devices. One can have any level of complexity
   desired (memory limited) and do subsumptive designs. More complex design
   take slightly more fuel, so life is not free. All movement costs fuel
   too. One can also tag neuron connections as "adaptable" that adapt their
   weights in acordance with the target neuron. This allows neurons to
   learn. The Neuron editor can handle 3 dimention arrays of neurons as
   single entities with very flexible interconect patterns.

   One can then design a scenario with walls, rocks, lights, fat (fuel)
   sources (that can be smelled) and many other such things. Robot tanks are
   then introduced into the Scenario and allowed interact or battle it out.
   The last one alive wins, or maybe one just watches the motion of the
   robots for fun. While the scenario is running it can be stopped, edited,
   zoom'd, and can track on any robot.

   The entire program is mouse and graphicly based. It uses DOS and VGA and
   is written in TurboC++. There will also be the ability to download
   designs to another computer and source code will be available for the
   core neural simulator. This will allow one to design neural systems and
   download them to real robots. The design tools can handle three
   dimentional networks so will work with video camera inputs and such. 

   NuTank source code is free from 
   http://www.xmission.com/~rkeene/NuTankSrc.ZIP
   Contact: Richard Keene; Keene Educational Software
   Email: rkeene@xmission.com or r.keene@center7.com

40. Lens
++++++++

   http://www.cs.cmu.edu/~dr/Lens

   Lens (the light, efficient network simulator) is a fast, flexible, and
   customizable neural network package written primarily in C. It currently
   handles standard backpropagation networks, simple recurrent (including
   Jordan and Elman) and fully recurrent nets, deterministic Boltzmann
   machines, self-organizing maps, and interactive-activation models. 

   Lens runs under Windows as well as a variety of Unix platforms. It
   includes a graphical interface and an embedded script language (Tcl). The
   key to the speed of Lens is its use of tight inner-loops that minimize
   memory references when traversing links. Frequently accessed values are
   stored in contiguous memory to achieve good cache performance. It is also
   able to do batch-level parallel training on multiple processors. 

   Because it is recognized that no simulator will satisfy sophisticated
   users out of the box, Lens was designed to facilitate code modification.
   Users can create and register such things as new network or group types,
   new weight update algorithms, or new shell commands without altering the
   main body of code. Therefore, modifications can be easily transferred to
   new releases. 

   Lens is available free-of-charge to those conducting research at academic
   or non-profit institutions. Other users should contact Douglas Rohde for
   licensing information at dr+lens@cs.cmu.edu. 

41. Joone: Java Object Oriented Neural Engine
+++++++++++++++++++++++++++++++++++++++++++++

   http://sourceforge.net/projects/joone 

   Joone is a neural net engine written in Java. It's a modular, scalable,
   multitasking and extensible engine. It can be extended by writing new
   modules to implement new algorithms or new architectures starting from
   simple base components. It's an Open Source project and everybody can
   contribute to its development. 

   Contact: Paolo Marrone, paolo@marrone.org 

42. NV: Neural Viewer
+++++++++++++++++++++

   http://www.btinternet.com/~cfinnie/ 

   A free software application for modelling and visualizing complex
   recurrent neural networks in 3D. 

43. EasyNN
++++++++++

   URL: http://www.easynn.com/ 

   EasyNN is a neural network system for Microsoft Windows. It can generate
   multi layer neural networks from text files or grids with minimal user
   intervention. The networks can then be trained, validated and queried.
   Network diagrams, graphs, input/output data and all the network details
   can be displayed and printed. Nodes can be added or deleted while the
   network is learning. The graph, grid, network and detail displays are
   updated dynamically so you can see how the neural networks work. EasyNN
   runs on Windows 95, 98, ME, NT 4.0, 2000 or XP. 

44. Multilayer Perceptron - A Java Implementation 
++++++++++++++++++++++++++++++++++++++++++++++++++

   Download java from: http://www.geocities.com/aydingurel/neural/ 

   What can you exactly do with it? You can: 
    o Build nets with any number of layers and units. Layers are connected
      to each other consecutively, each unit in a layer is connected to all
      of the units on the next layer (and vice versa) if there is one, 
    o Set units with linear and sigmoid activation functions and set them
      separately for each layer, 
    o Set parameters for sigmoid functions and set them separately for each
      layer, 
    o Use momentum, set different momentum parameters for each layer, 
    o Initialize the net using your own set of weights, 
    o Train the net using backpropagation and with any training rate. 

   Contact: Aydin Gurel, aydin.gurel@lycos.com 

------------------------------------------------------------------------

For some of these simulators there are user mailing lists. Get the packages
and look into their documentation for further info.

------------------------------------------------------------------------

Next part is part 6 (of 7). Previous part is part 4. 

-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
saswss@unx.sas.com    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.

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