Neural Lab

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Neural Lab is a free neural network simulator that designs and trains artificial neural networks for use in engineering, business, computer science and technology. It integrates with Microsoft Visual Studio using C (Win32 - Wintempla) to incorporate artificial neural networks into custom applications, research simulations or end user interfaces.

It provides a visual environment to design and test artificial neural networks.

The latest Neural Lab version is 4.1. The two major versions are version 3.1 and 4.0.

Version 3.x[edit]

Version 3.1 is navigated using a standard computer mouse. Version 3.1 is considered easier to use, however, it is difficult to perform complex tasks programmatically. Version 3.1 is therefore primarily useful for people without a programming background.

The version 3.1 tutorial provided very little theoretical background on artificial neural networks. Despite the number of examples, most of the examples focus only on multi-layer networks with supervised training.

Version 4.x[edit]

In version 4.0, it is possible to perform neural network operations by writing code. The code is very similar to C/C++, Java or C#.

In version 4.0, the authors incorporate background information on artificial neural networks.

Version 4.0 incorporates Kohonen networks that can be trained without supervision and probabilistic neural networks.


  • The tools allow reviewing and analyzing the structure of the training set.
  • The activation of the neurons for each case in the data set are visible. The tutorial provides examples in prediction, data mapping, data classification and autoassociative memory problems.
  • Once a network has been trained, it is possible to save it to a file. The file can be opened using Microsoft Visual Studio to create a standalone application that can employ the network.


Specific examples of neural networks include:

  • Prediction
  • Mapping
  • Auto Association
  • Classification
  • Network Simulation


Neural Lab is developed using Wintempla (a plug in that works with Microsoft Visual Studio). Wintempla encapsulates Win32 and simplifies the development of Microsoft Windows applications using C++ and native Win32 APIs.

Wintempla is a tool that integrates with Microsoft Visual Studio. Wintempla encapsulates Win32 to simplify the creation of Web and Desktop applications using C++ and object-oriented programming. The programmer has the option to use the native Win32 APIs or the Wintempla classes.

Wintempla includes:

  • A tutorial with key concepts in programming
  • Videos to illustrates how common control instructions (such as: if, else, for, while, etc.) work
  • Many examples and problems that can be used in: programming classes, SQL, PLSQL, Graphics
  • Support to create SQL database applications
  • SQL Import to create (in seconds) desktop or web applications from a SQL file
  • Simulated annealing optimization
  • Genetic algorithm optimization
  • Asynchronous module for Digital to Analog converters (DAC)
  • Asynchronous module for Analog to Digital converters (ADC)
  • Asynchronous module for serial ports
  • Multithread applications
  • Document printing
  • Microsoft Windows services
  • GUI deployment
  • Digital Signal Processing (remez, FFT and Filtering)
  • Common Object Model (COM)
  • A Lexical Analyzer, a compiler and virtual Machine
  • Artificial Neural Networks
  • Matrix operations
  • Data Visualization: Pie Chart, XY Chart, Polar Chart, Histogram, 3D Visualization, Simulation View
  • Native support for string manipulation using the STL
  • Native support for Math operations using the STL
  • Native support for data file storage
  • GDI Game application
  • Support for DirectX applications
  • Support for Open GL applications
  • Support to create PDF files programmatically

File extensions[edit]

  • .lab Neural Lab code (a UNICODE text file)
  • .lay A multi-layer neural network file
  • .lax A complex-domain multi-layer neural network file
  • .koh A Kohonen neural network file
  • .prb A probabilistic neural network file
  • .csv A comma separated data file

See also[edit]


  • Masters, Timothy (25 July 1994). Signal and Image Processing with Neural Networks: A C++ Sourcebook. John Wiley & Sons. ISBN 978-0-471-04963-0.
  • Masters, Timothy (17 April 1995). Advanced algorithms for neural networks: a C++ sourcebook. Wiley. ISBN 978-0-471-10588-6.

External links[edit]