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Section - Freeware and shareware packages for NN

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Since the FAQ maintainer works for a software company, he does not recommend
or evaluate software in the FAQ. The descriptions below are provided by the
developers or distributors of the software. 

Note for future submissions: Please restrict product descriptions to a
maximum of 60 lines of 72 characters, in either plain-text format or,
preferably, HTML format. If you include the standard header (name, company,
address, etc.), you need not count the header in the 60 line maximum. Please
confine your HTML to features that are supported by primitive browsers,
especially NCSA Mosaic 2.0; avoid tables, for example--use <pre> instead.
Try to make the descriptions objective, and avoid making implicit or
explicit assertions about competing products, such as "Our product is the
*only* one that does so-and-so." The FAQ maintainer reserves the right to
remove excessive marketing hype and to edit submissions to conform to size
requirements; if he is in a good mood, he may also correct your spelling and

The following simulators are described below: 

1. JavaNNS 
2. SNNS 
3. PDP++ 
4. Rochester Connectionist Simulator 
6. NeurDS 
7. PlaNet (formerly known as SunNet) 
9. Mactivation 
10. Cascade Correlation Simulator 
11. Quickprop 
12. DartNet 
13. Aspirin/MIGRAINES 
14. ALN Workbench 
15. Uts (Xerion, the sequel) 
16. Multi-Module Neural Computing Environment (MUME) 
18. Nevada Backpropagation (NevProp) 
19. Fuzzy ARTmap 
21. Basis-of-AI-NN Software 
22. Matrix Backpropagation 
24. FuNeGen 
25. NeuDL -- Neural-Network Description Language 
26. NeoC Explorer 
27. AINET 
28. DemoGNG 
29. Trajan 2.1 Shareware 
30. Neural Networks at your Fingertips 
31. NNFit 
32. Nenet v1.0 
33. Machine Consciousness Toolbox 
34. NICO Toolkit (speech recognition) 
35. SOM Toolbox for Matlab 5 
36. FastICA package for MATLAB 
37. NEXUS: Large-scale biological simulations 
38. Netlab: Neural network software for Matlab 
39. NuTank 
40. Lens 
41. Joone: Java Object Oriented Neural Engine 
42. NV: Neural Viewer 
43. EasyNN 
44. Multilayer Perceptron - A Java Implementation 

See also 

1. JavaNNS: Java Neural Network Simulator
   JavaNNS is the successor to SNNS. JavaNNS is based on the SNNS computing
   kernel, but has a newly developed graphical user interface written in
   Java set on top of it. Hence compatibility with SNNS is achieved while
   platform-independence is increased. 

   In addition to SNNS features, JavaNNS offers the capability of linking
   HTML browsers to it. This provides for accessing the user manual
   (available in HTML) or, optionally, a reference coursebook on neural
   networks directly from within the program. 

   JavaNNS is available for Windows NT / Windows 2000, Solaris and RedHat
   Linux. Additional ports are planed. JavaNNS is freely available and can
   be downloaded from the URL shown above. 

   Contact: Igor Fischer, Phone: +49 7071 29-77176, 

2. SNNS 4.2

   SNNS (Stuttgart Neural Network Simulator) is a software simulator for
   neural networks on Unix workstations developed at the Institute for
   Parallel and Distributed High Performance Systems (IPVR) at the
   University of Stuttgart. The goal of the SNNS project is to create an
   efficient and flexible simulation environment for research on and
   application of neural nets. 

   The SNNS simulator consists of two main components:

   1. simulator kernel written in C
   2. graphical user interface under X11R4 or X11R5 

   The simulator kernel operates on the internal network data structures of
   the neural nets and performs all operations of learning and recall. It
   can also be used without the other parts as a C program embedded in
   custom applications. It supports arbitrary network topologies and, like
   RCS, supports the concept of sites. SNNS can be extended by the user with
   user defined activation functions, output functions, site functions and
   learning procedures, which are written as simple C programs and linked to
   the simulator kernel. C code can be generated from a trained network.

   Currently the following network architectures and learning procedures are

    o Backpropagation (BP) for feedforward networks 
       o vanilla (online) BP 
       o BP with momentum term and flat spot elimination 
       o batch BP 
       o chunkwise BP 
    o Counterpropagation 
    o Quickprop 
    o Backpercolation 1 
    o RProp 
    o Generalized radial basis functions (RBF) 
    o ART1 
    o ART2 
    o ARTMAP 
    o Cascade Correlation 
    o Dynamic LVQ 
    o Backpropagation through time (for recurrent networks) 
    o Quickprop through time (for recurrent networks) 
    o Self-organizing maps (Kohonen maps) 
    o TDNN (time-delay networks) with Backpropagation 
    o Jordan networks 
    o Elman networks and extended hierarchical Elman networks 
    o Associative Memory 
    o TACOMA 

   The graphical user interface XGUI (X Graphical User Interface), built on
   top of the kernel, gives a 2D and a 3D graphical representation of the
   neural networks and controls the kernel during the simulation run. In
   addition, the 2D user interface has an integrated network editor which
   can be used to directly create, manipulate and visualize neural nets in
   various ways. 

   SNNSv4.1 has been tested on SUN SparcSt ELC,IPC (SunOS 4.1.2, 4.1.3), SUN
   SparcSt 2 (SunOS 4.1.2), SUN SparcSt 5, 10, 20 (SunOS 4.1.3, 5.2),
   DECstation 3100, 5000 (Ultrix V4.2), DEC Alpha AXP 3000 (OSF1 V2.1),
   IBM-PC 80486, Pentium (Linux), IBM RS 6000/320, 320H, 530H (AIX V3.1, AIX
   V3.2), HP 9000/720, 730 (HP-UX 8.07), and SGI Indigo 2 (IRIX 4.0.5, 5.3).

   The distributed kernel can spread one learning run over a workstation

   SNNS web page:
   Ftp server:
    o SNNSv4.1.Readme 
    o SNNSv4.1.tar.gz (1.4 MB, Source code) 
    o (1 MB, Documentation) 
   Mailing list: 

3. PDP++


   The PDP++ software is a neural-network simulation system written in C++.
   It represents the next generation of the PDP software released with the
   McClelland and Rumelhart "Explorations in Parallel Distributed Processing
   Handbook", MIT Press, 1987. It is easy enough for novice users, but very
   powerful and flexible for research use. PDP++ is featured in a new
   textbook, Computational Explorations in Cognitive Neuroscience:
   Understanding the Mind by Simulating the Brain, by Randall C. O'Reilly
   and Yuko Munakata, MIT Press, 2000.

   Supported algorithms include: 

    o Feedforward and recurrent error backpropagation. Recurrent BP includes
      continuous, real-time models, and Almeida-Pineda. 
    o Constraint satisfaction algorithms and associated learning algorithms
      including Boltzmann Machine, Hopfield models, mean-field networks
      (DBM), Interactive Activation and Competition (IAC), and continuous
      stochastic networks. 
    o Self-organizing learning including Competitive Learning, Soft
      Competitive Learning, simple Hebbian, and Self-organizing Maps
      ("Kohonen Nets"). 
    o Mixtures-of-experts using backpropagation experts, EM updating, and a
      SoftMax gating module. 
    o Leabra algorithm that combines error-driven and Hebbian learning with
      k-Winners-Take-All inhibitory competition. 

   The software can be obtained by anonymous ftp from: 
    o or 
    o or 

4. Rochester Connectionist Simulator

   A versatile simulator program for arbitrary types of neural nets. Comes
   with a backprop package and a X11/Sunview interface. Available via
   anonymous FTP from
   There's also a patch available from 


   The UCLA-SFINX, a "neural" network simulator is now in public domain.
   UCLA-SFINX (Structure and Function In Neural connec- tions) is an
   interactive neural network simulation environment designed to provide the
   investigative tools for studying the behavior of various neural
   structures. It was designed to easily express and simulate the highly
   regular patterns often found in large networks, but it is also general
   enough to model parallel systems of arbitrary interconnectivity. For more
   information, see 

6. NeurDS

   Neural Design and Simulation System. This is a general purpose tool for
   building, running and analysing Neural Network Models in an efficient
   manner. NeurDS will compile and run virtually any Neural Network Model
   using a consistent user interface that may be either window or "batch"
   oriented. HP-UX 8.07 source code is available from or 

7. PlaNet5.7 (formerly known as SunNet)

   A popular connectionist simulator with versions to run under X Windows,
   and non-graphics terminals created by Yoshiro Miyata (Chukyo Univ.,
   Japan). 60-page User's Guide in Postscript. Send any questions to Available for anonymous ftp from as /pub/neuron/PlaNet5.7.tar.gz (800 kb) 


   GENESIS 2.0 (GEneral NEural SImulation System) is a general purpose
   simulation platform which was developed to support the simulation of
   neural systems ranging from complex models of single neurons to
   simulations of large networks made up of more abstract neuronal
   components. Most current GENESIS applications involve realistic
   simulations of biological neural systems. Although the software can also
   model more abstract networks, other simulators are more suitable for
   backpropagation and similar connectionist modeling. Runs on most Unix
   platforms. Graphical front end XODUS. Parallel version for networks of
   workstations, symmetric multiprocessors, and MPPs also available. Further
   information via WWW at 

9. Mactivation

   A neural network simulator for the Apple Macintosh. Available for ftp
   from as /pub/cs/misc/Mactivation-3.3.sea.hqx 

10. Cascade Correlation Simulator

   A simulator for Scott Fahlman's Cascade Correlation algorithm. Available
   for ftp from in directory
   /afs/cs/project/connect/code/supported as the file cascor-v1.2.shar (223
   KB) There is also a version of recurrent cascade correlation in the same
   directory in file rcc1.c (108 KB). 

11. Quickprop

   A variation of the back-propagation algorithm developed by Scott Fahlman.
   A simulator is available in the same directory as the cascade correlation
   simulator above in file nevprop1.16.shar (137 KB)
   (There is also an obsolete simulator called quickprop1.c (21 KB) in the
   same directory, but it has been superseeded by NevProp. See also the
   description of NevProp below.) 

12. DartNet

   DartNet is a Macintosh-based backpropagation simulator, developed at
   Dartmouth by Jamshed Bharucha and Sean Nolan as a pedagogical tool. It
   makes use of the Mac's graphical interface, and provides a number of
   tools for building, editing, training, testing and examining networks.
   This program is available by anonymous ftp from as 
   /pub/mac/dartnet.sit.hqx (124 KB). 

13. Aspirin/MIGRAINES

   Aspirin/MIGRAINES 6.0 consists of a code generator that builds neural
   network simulations by reading a network description (written in a
   language called "Aspirin") and generates a C simulation. An interface
   (called "MIGRAINES") is provided to export data from the neural network
   to visualization tools. The system has been ported to a large number of
   platforms. The goal of Aspirin is to provide a common extendible
   front-end language and parser for different network paradigms. The
   MIGRAINES interface is a terminal based interface that allows you to open
   Unix pipes to data in the neural network. Users can display the data
   using either public or commercial graphics/analysis tools. Example
   filters are included that convert data exported through MIGRAINES to
   formats readable by Gnuplot 3.0, Matlab, Mathematica, and xgobi. 

   The software is available from 

14. ALN Workbench (a spreadsheet for Windows) 

   ALNBench is a free spreadsheet program for MS-Windows (NT, 95) that
   allows the user to import training and test sets and predict a chosen
   column of data from the others in the training set. It is an easy-to-use
   program for research, education and evaluation of ALN technology. Anyone
   who can use a spreadsheet can quickly understand how to use it. It
   facilitates interactive access to the power of the Dendronic Learning
   Engine (DLE), a product in commercial use. 

   An ALN consists of linear functions with adaptable weights at the leaves
   of a tree of maximum and minimum operators. The tree grows automatically
   during training: a linear piece splits if its error is too high. The
   function computed by an ALN is piecewise linear and continuous. It can
   learn to approximate any continuous function to arbitrarily high

   Parameters allow the user to input knowledge about a function to promote
   good generalization. In particular, bounds on the weights of the linear
   functions can be directly enforced. Some parameters are chosen
   automatically in standard mode, and are under user control in expert

   The program can be downloaded from 

   For further information please contact: 

   William W. Armstrong PhD, President
   Dendronic Decisions Limited
   3624 - 108 Street, NW
   Edmonton, Alberta,
   Canada T6J 1B4
   Tel. +1 403 421 0800
   (Note: The area code 403 changes to 780 after Jan. 25, 1999)

15. Uts (Xerion, the sequel)

   Uts is a portable artificial neural network simulator written on top of
   the Tool Control Language (Tcl) and the Tk UI toolkit. As result, the
   user interface is readily modifiable and it is possible to simultaneously
   use the graphical user interface and visualization tools and use scripts
   written in Tcl. Uts itself implements only the connectionist paradigm of
   linked units in Tcl and the basic elements of the graphical user
   interface. To make a ready-to-use package, there exist modules which use
   Uts to do back-propagation (tkbp) and mixed em gaussian optimization
   (tkmxm). Uts is available in in directory /pub/xerion.

16. Multi-Module Neural Computing Environment (MUME)

   MUME is a simulation environment for multi-modules neural computing. It
   provides an object oriented facility for the simulation and training of
   multiple nets with various architectures and learning algorithms. MUME
   includes a library of network architectures including feedforward, simple
   recurrent, and continuously running recurrent neural networks. Each
   architecture is supported by a variety of learning algorithms. MUME can
   be used for large scale neural network simulations as it provides support
   for learning in multi-net environments. It also provide pre- and
   post-processing facilities. For more information, see


   These are packages for Learning Vector Quantization and Self-Organizing
   Maps, respectively. They have been built by the LVQ/SOM Programming Team
   of the Helsinki University of Technology, Laboratory of Computer and
   Information Science, Rakentajanaukio 2 C, SF-02150 Espoo, FINLAND There
   are versions for Unix and MS-DOS available from 

18. Nevada Backpropagation (NevProp)

   NevProp, version 3, is a relatively easy-to-use, feedforward
   backpropagation multilayer perceptron simulator-that is, statistically
   speaking, a multivariate nonlinear regression program. NevProp3 is
   distributed for free under the terms of the GNU Public License and can be
   downloaded from and 

   The program is distributed as C source code that should compile and run
   on most platforms. In addition, precompiled executables are available for
   Macintosh and DOS platforms. Limited support is available from Phil
   Goodman (, University of Nevada Center for Biomedical

   MAJOR FEATURES OF NevProp3 OPERATION (* indicates feature new in version
   1. Character-based interface common to the UNIX, DOS, and Macintosh
   2. Command-line argument format to efficiently initiate NevProp3. For
      Generalized Nonlinear Modeling (GNLM) mode, beginners may opt to use
      an interactive interface. 
   3. Option to pre-standardize the training data (z-score or forced
   4. Option to pre-impute missing elements in training data (case-wise
      deletion, or imputation with mean, median, random selection, or
      k-nearest neighbor).* 
   5. Primary error (criterion) measures include mean square error,
      hyperbolic tangent error, and log likelihood (cross-entropy), as
      penalized an unpenalized values. 
   6. Secondary measures include ROC-curve area (c-index), thresholded
      classification, R-squared and Nagelkerke R-squared. Also reported at
      intervals are the weight configuration, and the sum of square weights.
   7. Allows simultaneous use of logistic (for dichotomous outputs) and
      linear output activation functions (automatically detected to assign
      activation and error function).* 
   8. 1-of-N (Softmax)* and M-of-N options for binary classification. 
   9. Optimization options: flexible learning rate (fixed global adaptive,
      weight-specific, quickprop), split learn rate (inversely proportional
      to number of incoming connections), stochastic (case-wise updating),
      sigmoidprime offset (to prevent locking at logistic tails). 
  10. Regularization options: fixed weight decay, optional decay on bias
      weights, Bayesian hyperpenalty* (partial and full Automatic Relevance
      Determination-also used to select important predictors), automated
      early stopping (full dataset stopping based on multiple subset
      cross-validations) by error criterion. 
  11. Validation options: upload held-out validation test set; select subset
      of outputs for joint summary statistics;* select automated
      bootstrapped modeling to correct optimistically biased summary
      statistics (with standard deviations) without use of hold-out. 
  12. Saving predictions: for training data and uploaded validation test
      set, save file with identifiers, true targets, predictions, and (if
      bootstrapped models selected) lower and upper 95% confidence limits*
      for each prediction. 
  13. Inference options: determination of the mean predictor effects and
      level effects (for multilevel predictor variables); confidence limits
      within main model or across bootstrapped models.* 
  14. ANN-kNN (k-nearest neighbor) emulation mode options: impute missing
      data elements and save to new data file; classify test data (with or
      without missing elements) using ANN-kNN model trained on data with or
      without missing elements (complete ANN-based expectation
  15. AGE (ANN-Gated Ensemble) options: adaptively weight predictions (any
      scale of scores) obtained from multiple (human or computational)
      "experts"; validate on new prediction sets; optional internal
      prior-probability expert.* 

19. Fuzzy ARTmap

   This is just a small example program. Available for anonymous ftp from []
   (44 kB). 


   This is a prototype that stems from an ESPRIT project. It implements
   back-propagation, self organising map, and Hopfield nets. Avaliable for
   ftp from [] as 
   /pub/sci/neural/sims/pygmalion.tar.Z (1534 kb). (Original site is archive/pygmalion/pygmalion.tar.Z). 

21. Basis-of-AI-NN Software

   Non-GUI DOS and UNIX source code, DOS binaries and examples are available
   in the following different program sets and the backprop package has a
   Windows 3.x binary and a Unix/Tcl/Tk version: 

      [backprop, quickprop, delta-bar-delta, recurrent networks],
      [simple clustering, k-nearest neighbor, LVQ1, DSM],
      [Hopfield, Boltzman, interactive activation network],
      [interactive activation network],
      [feedforward counterpropagation],
      [ART I],
      [a simple BAM] and
      [the linear pattern classifier]

   For details see: 

   An improved professional version of backprop is also available; see Part
   6 of the FAQ. 

   Questions to: Don Tveter, 

22. Matrix Backpropagation

   MBP (Matrix Back Propagation) is a very efficient implementation of the
   back-propagation algorithm for current-generation workstations. The
   algorithm includes a per-epoch adaptive technique for gradient descent.
   All the computations are done through matrix multiplications and make use
   of highly optimized C code. The goal is to reach almost peak-performances
   on RISCs with superscalar capabilities and fast caches. On some machines
   (and with large networks) a 30-40x speed-up can be measured with respect
   to conventional implementations. The software is available by anonymous
   ftp from as /neural/MBP/MBPv1.1.tar.Z (Unix
   version), or /neural/MBP/ (PC version)., For more information,
   contact Davide Anguita ( 


   BIOSIM is a biologically oriented neural network simulator. Public
   domain, runs on Unix (less powerful PC-version is available, too), easy
   to install, bilingual (german and english), has a GUI (Graphical User
   Interface), designed for research and teaching, provides online help
   facilities, offers controlling interfaces, batch version is available, a
   DEMO is provided. 

   REQUIREMENTS (Unix version): X11 Rel. 3 and above, Motif Rel 1.0 and
   above, 12 MB of physical memory, recommended are 24 MB and more, 20 MB
   disc space. REQUIREMENTS (PC version): PC-compatible with MS Windows 3.0
   and above, 4 MB of physical memory, recommended are 8 MB and more, 1 MB
   disc space. 

   Four neuron models are implemented in BIOSIM: a simple model only
   switching ion channels on and off, the original Hodgkin-Huxley model, the
   SWIM model (a modified HH model) and the Golowasch-Buchholz model.
   Dendrites consist of a chain of segments without bifurcation. A neural
   network can be created by using the interactive network editor which is
   part of BIOSIM. Parameters can be changed via context sensitive menus and
   the results of the simulation can be visualized in observation windows
   for neurons and synapses. Stochastic processes such as noise can be
   included. In addition, biologically orientied learning and forgetting
   processes are modeled, e.g. sensitization, habituation, conditioning,
   hebbian learning and competitive learning. Three synaptic types are
   predefined (an excitatatory synapse type, an inhibitory synapse type and
   an electrical synapse). Additional synaptic types can be created
   interactively as desired. 

   Available for ftp from in directory /pub/bio/neurobio: Get 
   /pub/bio/neurobio/biosim.readme (2 kb) and /pub/bio/neurobio/biosim.tar.Z
   (2.6 MB) for the Unix version or /pub/bio/neurobio/biosimpc.readme (2 kb)
   and /pub/bio/neurobio/ (150 kb) for the PC version. 

   Stefan Bergdoll 
   Department of Software Engineering (ZXA/US) 
   BASF Inc. 
   D-67056 Ludwigshafen; Germany phone 0621-60-21372 fax 0621-60-43735 

24. FuNeGen 1.0

   FuNeGen is a MLP based software program to generate fuzzy rule based
   classifiers. For more information, see

25. NeuDL -- Neural-Network Description Language

   NeuDL is a description language for the design, training, and operation
   of neural networks. It is currently limited to the backpropagation
   neural-network model; however, it offers a great deal of flexibility. For
   example, the user can explicitly specify the connections between nodes
   and can create or destroy connections dynamically as training progresses.
   NeuDL is an interpreted language resembling C or C++. It also has
   instructions dealing with training/testing set manipulation as well as
   neural network operation. A NeuDL program can be run in interpreted mode
   or it can be automatically translated into C++ which can be compiled and
   then executed. The NeuDL interpreter is written in C++ and can be easly
   extended with new instructions. For more information, see

26. NeoC Explorer (Pattern Maker included)

   The NeoC software is an implementation of Fukushima's Neocognitron neural
   network. Its purpose is to test the model and to facilitate interactivity
   for the experiments. Some substantial features: GUI, explorer and tester
   operation modes, recognition statistics, performance analysis, elements
   displaying, easy net construction. PLUS, a pattern maker utility for
   testing ANN: GUI, text file output, transformations. For more
   information, see 


   AINET is a probabilistic neural network application which runs on Windows
   95/NT. It was designed specifically to facilitate the modeling task in
   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: 

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
    o Neural Gas with Competitive Hebbian Learning (Martinetz and Schulten
    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 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 

   It is also possible to download the complete source code and a Postscript
   version of the paper via anonymous ftp from [] 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 (89
   KB). There is also a README file (9 KB). Please send any comments and
   questions to 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

   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
   for further details, and a free copy of the Shareware version.

   Alternatively, email 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 

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
   Contact: Bernard P.A. Grandjean, department of chemical engineering,
   Laval University; Sainte-Foy (Quibec) Canada G1K 7P4; 

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
    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
    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: 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:

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
    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. 

    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 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
    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
   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

   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) 

    o allows networks to be designed concisely and as precisely as is
    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 

   Email: Lawrence O. Ryan <>

38. Netlab: Neural network software for Matlab

   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
   Contact: Richard Keene; Keene Educational Software
   Email: or

40. 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 

41. Joone: Java Object Oriented Neural Engine

   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, 

42. NV: Neural Viewer

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

43. EasyNN


   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: 

   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
    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, 


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    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.

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