Simulator of Modular Neural Networks

My photographSiMoNNe is a modular neural network simulator. It is the language driven machine. Simulator is not independent on praticular neural paradigm which means that the neural networks with variable topology and function can be simulated using SiMoNNe. The communication with simulator is based on a simple language which can describe the network, control experiment and return result. The results of the simulation are written in the SiMoNNe language too and are reusable as a simulator input. The SiMoNNe simulator is written in pure Java so it is platform independent. The simulator is determined to simulate modular neural networks.


The SiMoNNe is a neural network simulator driven by a high-level programming language. The language combines pleasant features such as objects for neural network units representation, bidirectionality for easy experiment control, connections for making of synapses, modularity for modular neural networks, weak typed variables, matrix calculations etc.

There is also a graphical user interface (GUI) available.

SiMoNNe screenshotSiMoNNe screenshot


  • Jan Koutnik, Miroslav Šnorek: New Trends in Simulation of Neural Networks. In: Proceedings of 6th EUROSIM Congress on Modelling and Simulation, , Ljubljana, 2007. ISBN 3-901608-32-X BibTex

    In this paper actual simulation techniques and simulation systems for artificial neural networks are compared. We focus on neural network simulators that allow a user easy design of new neural networks. There are several simulation strategies that can be exploited by modern neural network simulators described. We considered the synchronous simulation as the most effective for parallel systems like artificial neural networks. Examples of general simulation systems that can be used for simulation of neural networks are mentioned. Current neural network simulators commonly depend on a type of neural network simulated and cannot be easily extended to simulate a different or a neural network with a brand new architecture and function. Universal simulation tools seem to be suitable for network design but do not support connectionism natively. The missing language constructions and tools for native support of connecting objects in the simulation lead us to design a new simulation tool SiMoNNe - Simulator of Modular Neural Networks, which allows easy design and simulation of neural networks using a high level programming language. The language itself is object oriented with weak type control. It supports native connection of simulated neurons, layers, modules and networks, matrix calculations, easy control of simulation parameters using expressions, re-usability of the result as a source code and more. The language is interactive and allows connection of a GUI to the SiMoNNe core.

  • Vladimír Klimeš: Grafické uživatelské rozhraní pro simulaci neuronových sítí. At: Czech Technical University in Prague, 2005 BibTex, PDF
  • Tomáš Horyl: Implementace simulátoru neuronových sítí. At: Czech Technical University in Prague, 2005 BibTex, PDF
  • Koutnik J., Snorek M.: Efficient Simulation of Modular Neural Networks. In: Proceedings of the 5th EUROSIM Congres Modelling and Simulation, Vienna: EUROSIM-FRANCOSIM-ARGESIM, 2004. ISBN 3-901608-28-1 BibTex, PDF

    In this paper we describe a new language for efficient simulation of modular neural networks called SiMoNNe. After an unsuccessful search for a suitable simulation environment we designed a simulator driven by a high level programming language which allows easy and fast creation, simulation and testing of various neural network architectures. Not only modular neural networks can be simulated but also well known conventional neural network paradigms can be simulated by SiMoNNe.

  • Brunner J., Koutnik J.: Simonne - Simulator of Modular Neural Networks. Neural Network World vol. 12 nr. 3, p. 267-278, , 2002. ISSN 1210-0552 BibTex