Home > Science > ai-faq > neural-nets >

comp.ai.neural-nets FAQ, Part 5 of 7: Free software

Section 1 of 2 - Prev - Next

Archive-name: ai-faq/neural-nets/part5 Last-modified: 2002-08-19 URL: ftp://ftp.sas.com/pub/neural/FAQ5.html Maintainer: saswss@unx.sas.com (Warren S. Sarle) The copyright for the description of each product is held by the producer or distributor of the product or whoever it was who supplied the description for the FAQ, who by submitting it for the FAQ gives permission for the description to be reproduced as part of the FAQ in any of the ways specified in part 1 of the FAQ. This is part 5 (of 7) of a monthly posting to the Usenet newsgroup comp.ai.neural-nets. See the part 1 of this posting for full information what it is all about. ========== Questions ========== ******************************** Part 1: Introduction Part 2: Learning Part 3: Generalization Part 4: Books, data, etc. Part 5: Free software Source code on the web? Freeware and shareware packages for NN simulation? Part 6: Commercial software Part 7: Hardware and miscellaneous ------------------------------------------------------------------------ Subject: Source code on the web? ================================ The following URLs are reputed to have source code for NNs. Use at your own risk. o C/C++ http://www.generation5.org/xornet.shtml http://www.netwood.net/~edwin/Matrix/ http://www.netwood.net/~edwin/svmt/ http://www.geocities.com/Athens/Agora/7256/c-plus-p.html http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/faces.html http://www.cog.brown.edu/~rodrigo/neural_nets_library.html http://www.agt.net/public/bmarshal/aiparts/aiparts.htm http://www.geocities.com/CapeCanaveral/1624/ http://www.neuroquest.com/ or http://www.grobe.org/LANE http://www.neuro-fuzzy.de/ http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/cascor/ http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/qprop/ http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/rcc/ etc. o Java http://www.philbrierley.com/code http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html http://neuron.eng.wayne.edu/software.html http://www.aist.go.jp/NIBH/~b0616/Lab/Links.html http://www.aist.go.jp/NIBH/~b0616/Lab/BSOM1/ http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/loos http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html http://www.isbiel.ch/I/Projects/janet/index.html http://www.born-again.demon.nl/software.html http://www.patol.com/java/NN/index.html http://www-isis.ecs.soton.ac.uk/computing/neural/laboratory/laboratory.html http://www.neuro-fuzzy.de/ http://sourceforge.net/projects/joone http://joone.sourceforge.net/ http://openai.sourceforge.net/ http://www.geocities.com/aydingurel/neural/ http://www-eco.enst-bretagne.fr/~phan/emergence/complexe/neuron/mlp.html o FORTRAN http://www.philbrierley.com/code http://www.cranfield.ac.uk/public/me/fo941992/mlpcode.htm o Pascal http://www.ibrtses.com/delphi/neuralnets.html If you are using a small computer (PC, Mac, etc.) you may want to have a look at the Central Neural System Electronic Bulletin Board (see question "Other sources of information"). There are lots of small simulator packages. Some of the CNS materials can also be found at http://www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/neural/cns/0.html ------------------------------------------------------------------------ Subject: Freeware and shareware packages for NN =============================================== simulation? =========== 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

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
punctuation.

The following simulators are described below:

1. JavaNNS
2. SNNS
3. PDP++
4. Rochester Connectionist Simulator
5. UCLA-SFINX
6. NeurDS
7. PlaNet (formerly known as SunNet)
8. GENESIS
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)
17. LVQ_PAK, SOM_PAK
18. Nevada Backpropagation (NevProp)
19. Fuzzy ARTmap
20. PYGMALION
21. Basis-of-AI-NN Software
22. Matrix Backpropagation
23. BIOSIM
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
http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.html

1. JavaNNS: Java Neural Network Simulator
+++++++++++++++++++++++++++++++++++++++++

http://www-ra.informatik.uni-tuebingen.de/forschung/JavaNNS/welcome_e.html
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,
fischer@informatik.uni-tuebingen.de

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
included:

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
cluster.

SNNS web page: http://www-ra.informatik.uni-tuebingen.de/SNNS
Ftp server: ftp://ftp.informatik.uni-tuebingen.de/pub/SNNS
o SNNSv4.1.Readme
o SNNSv4.1.tar.gz (1.4 MB, Source code)
o SNNSv4.1.Manual.ps.gz (1 MB, Documentation)
Mailing list:
http://www-ra.informatik.uni-tuebingen.de/SNNS/about-ml.html

3. PDP++
++++++++

URL: http://www.cnbc.cmu.edu/PDP++/PDP++.html

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 ftp://grey.colorado.edu/pub/oreilly/pdp++ or
o ftp://cnbc.cmu.edu/pub/pdp++/ or
o ftp://unix.hensa.ac.uk/mirrors/pdp++/

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
ftp://ftp.cs.rochester.edu/pub/packages/simulator/simulator_v4.2.tar.Z
There's also a patch available from
ftp://ftp.cs.rochester.edu/pub/packages/simulator/simulator_v4.2.patch.1

5. UCLA-SFINX
+++++++++++++

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
http://decus.acornsw.com/vs0121/AISIG/F90/NETS/UCLA_SIM.TXT

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
http://hpux.u-aizu.ac.jp/hppd/hpux/NeuralNets/NeurDS-3.1/ or
http://askdonna.ask.uni-karlsruhe.de/hppd/hpux/NeuralNets/NeurDS-3.1/

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
miyata@sccs.chukyo-u.ac.jp Available for anonymous ftp from
ftp.ira.uka.de as /pub/neuron/PlaNet5.7.tar.gz (800 kb)

8. GENESIS
++++++++++

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 http://www.genesis-sim.org/GENESIS/.

9. Mactivation
++++++++++++++

A neural network simulator for the Apple Macintosh. Available for ftp
from ftp.cs.colorado.edu 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 ftp.cs.cmu.edu 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 ftp.dartmouth.edu 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
http://www.elegant-software.com/software/aspirin/

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
accuracy.

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
mode.

The program can be downloaded from http://www.dendronic.com/main.htm

For further information please contact:

William W. Armstrong PhD, President
Dendronic Decisions Limited
3624 - 108 Street, NW
Edmonton, Alberta,
Canada T6J 1B4
Email: arms@dendronic.com
URL: http://www.dendronic.com/
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 ftp.cs.toronto.edu 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
http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/mume/0.html

17. LVQ_PAK, SOM_PAK
++++++++++++++++++++

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
http://nucleus.hut.fi/nnrc/nnrc-programs.html

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 http://brain.cs.unr.edu/publications/NevProp.zip and
http://brain.cs.unr.edu/publications/NevPropManual.pdf

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 (goodman@unr.edu), University of Nevada Center for Biomedical
Research.

MAJOR FEATURES OF NevProp3 OPERATION (* indicates feature new in version
3)
1. Character-based interface common to the UNIX, DOS, and Macintosh
platforms.
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
range*).
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
maximization).*
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
park.bu.edu [128.176.121.56] ftp://cns-ftp.bu.edu/pub/fuzzy-artmap.tar.Z
(44 kB).

20. PYGMALION
+++++++++++++

This is a prototype that stems from an ESPRIT project. It implements
back-propagation, self organising map, and Hopfield nets. Avaliable for
ftp from ftp.funet.fi [128.214.248.6] as
/pub/sci/neural/sims/pygmalion.tar.Z (1534 kb). (Original site is
imag.imag.fr: 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: http://www.dontveter.com/nnsoft/nnsoft.html

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

Questions to: Don Tveter, don@dontveter.com

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 ftp.esng.dibe.unige.it as /neural/MBP/MBPv1.1.tar.Z (Unix
version), or /neural/MBP/MBPv11.zip (PC version)., For more information,
contact Davide Anguita (anguita@dibe.unige.it).

23. BIOSIM
++++++++++

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 ftp.uni-kl.de 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/biosimpc.zip (150 kb) for the PC version.

Contact:
Stefan Bergdoll
Department of Software Engineering (ZXA/US)
BASF Inc.
D-67056 Ludwigshafen; Germany
bergdoll@zxa.basf-ag.de 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
http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/fuzzy/systems/funegen/

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
http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/neudl/0.html

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 http://www.simtel.net/pub/pd/39893.html

27. AINET
+++++++++

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

Section 1 of 2 - Prev - Next

Back to category neural-nets - Use Smart Search

Home - Smart Search - About the project - Feedback