Coneural's scientists have developed several software packages that can be used as research tools:
The Oscillation Score
The Oscillation Score is an efficient method for estimating oscillation strength in neuronal activity. The software (developed by Raul Muresan and colleagues) can be freely downloaded here and accompanies a paper published in Journal of Neurophysiology.
Robby (developed by Catalin Rusu) is an open robotic framework which enables an efficient and easy distributed control of different devices, such as Khepera or E-puck, by spiking neural networks. The framework provides both the abstractization layer for robot communication and the logic to support the development and simulation of large-scale spiking neural controllers. It is written in C++ and adheres to the POSIX standards. It is available at https://github.com/robby-project/robby.
Thyrix (developed by Razvan Florian) is a fast agent/environment simulator designed for artificial intelligence or artificial life research. It is optimized for speed, and thus it is very appropriate for evolutionary experiments. Thyrix allows the simulation of agents with articulated arms or bodies that interact with objects within a 2D environment with Aristotelian physics. The simulator includes a stable collision resolution system and a fast algorithm for solving the constraints generated by the articulations.
SpikeNNS (developed by Ioana Goga) represents an extension of SNNS - Stuttgart Neural Network Simulator for the simulation of spiking neural networks. The neural model implemented is based on a simplified version of the Spike Response Model. Neurons are simulated with a limited number of parameters that include: postsynaptic potential, threshold, noise, delays, refractoriness. The SpikeNNS was designed to produce biologically inspired models of cognitive phenomena based on a spike-coding neural model. SpikeNNS is free, open-source software.
Neocortex and RetinotopicNet are two efficient simulators for large scale spiking neural networks, developed by Raul Muresan. For more information, see the following papers:
R.C. Muresan, I. Ignat (2004), The "Neocortex" Neural Simulator: A Modern Design. International Conference on Intelligent Engineering Systems, September 19-21, 2004, Cluj-Napoca, Romania.
R.C. Muresan, I. Ignat (2004). Principles of Design for Large Scale Neural Simulators. International Conference on Automation, Quality and Testing, Robotics, May 13-15, 2004, Cluj-Napoca, Romania.
R. C. Muresan (2003), RetinotopicNET: An efficient simulator for retinotopic visual architectures, Proceedings of the European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 247-254.
The modulus-metric defines a distance between two spike trains. It does not depend on any parameter and it allows a fast computational implementation that depends linearly on the number of spikes in the two spike trains. A Python implementation of the modulus-metric algorithm, by Razvan V. Florian and Catalin V. Rusu, is available here. For more information, see: C. V. Rusu and R. V. Florian, A new class of metrics for spike trains.