|author(s)||Daniel Brüderle, Eric Müller, Andrew Davison, Eilif Muller, Johannes Schemmel, Karlheinz Meier|
|title||Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System|
|Keywords||neuromorphic, VLSI, hardware, wafer-scale, software, modeling, computational neuroscience, python, PyNN|
|source||Front. Neuroinform. 3:17 (2009)|
Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated.
|URL||abstract and download|