Towards reverse engineering the brain: Modeling abstractions and simulation frameworks

Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. Biological circuits are made up of low-precision, unreliable and massively parallel neural elements with highly reconfigurable and plastic connections. Two of the most interesting properties of the neural systems are its self-organizing capabilities and its template architecture. Recent research in spiking neural networks has demonstrated interesting principles about learning and neural computation. Understanding and applying these principles to practical problems is only possible if large-scale spiking neural simulators can be constructed. Recent advances in low-cost multiprocessor architectures make it possible to build large-scale spiking network simulators. In this paper we review modeling abstractions for neural circuits and frameworks for modeling, simulating and analyzing spiking neural networks.