SIMBRAIN is a free tool for building, running, and analyzing neural-networks (computer simulations of brain circuitry). Simbrain aims to be as visual and easy-to-use as possible. See our design goals. Unique features of Simbrain include its integrated "world components" and its ability to represent a network's state space. Simbrain is written in Java and runs on Windows, Mac OS X, and Linux. Click here for a video introduction to some of Simbrain's features, check out the screenshots, or just download the software and start experimenting. Simbrain is open source, and constantly evolving. We'd love for you to join our team. To discuss any aspect of Simbrain check out the forum.
Fræser is a framework for estimating the parameters of static and dynamic errors-in-variables systems with the opportunity to compare various errors-in-variables parameter estimation algorithms via simulations. It features a graphical user interface and several examples for simultaneously estimating model and noise parameters.
The framework incorporates the following linear and nonlinear estimation methods for static and dynamic systems:
* model parameter estimation for static systems
o Koopmans method
* linear model and noise parameter estimation for dynamic systems
o (extended) instrumental variables method (XIV)
o bias-compensating least-squares method (BCLS)
o Frisch scheme (FS)
o generalized Koopmans-Levin method (GKL)
* nonlinear model parameter estimation for static systems
o nonlinear Koopmans method (NK)
o approximated maximum likelihood method (AML)
* nonlinear model and noise parameter estimation for dynamic systems
o bias-compensated least squares method (BCLS)
o nonlinear Koopmans-Levin method (NKL)
o nonlinear extennonlinear extension to generalized Koopmans-Levin method (NGKL)