The brain is an extraordinarily complex machine. The workings of this machine can be described as a multitude of levels of abstractions and in various description languages, yet the different levels of abstraction are not mutually exclusive at heart and must be combined into a multi-level description; focusing on a single level of abstraction can fall short where only a holistic, systemic view can adequately explain the system under investigation. If we want to build systems able to generate meaningful behavior in the real world, they have to be complete in the sense that they must spawn from sensory processing to the Behavioral output. Systems compliant with this requirement will inevitably be of a large-scale, where the overall architecture is of critical importance. To meet these challenges we have developed a multi-level neuronal simulation environment iqr. iqr provides a mean to design neuronal models graphically and to visualize and analyze data online. In iqr connectivity is defined in a flexible, yet compact way and simulations run at a high speed, which allows the control of real-world devices in real-time. The architecture of iqr is modular, providing the possibility to write new neuron, and synapse types, and custom interfaces to hardware.

Key features of iqr

• Graphical on-line control of the simulation
• Change of model parameters at run-time
• On-line visualization and analysis of data
• Connect neural models to real-world devices (cameras, mobile robots, etc.)
• Pre-defined neuron and synapse types
• Open architecture for new neurons, synapses, and hardware interfaces
• Documented, and available
• GPL open source

Applications
iqr is and has been used successfully in a number of projects:

• Construction of mixed-reality systems
  An installation such as the eXperience Induction Machine needs an “operating system” for the integration and control of the different effectors and sensors. iqr is well suited for this role due on the one hand to it’s the data-flow oriented approach of information processing, and on the other hand, because it is very easy to build interfaces to virtually any kind of hardware.
• rePERcurso
• Amoth
• Synthetic Forager
• ReNaChip
• NeuroChem