Abstract
This paper presents configuration methods for an existing neuromorphic hardware and shows first experimental results. The utilized mixed-signal VLSI device implements a highly accelerated network of integrate-and-fire neurons. We present a software framework, which provides the possibility to interface the hardware and explore it from the point of view of neuroscience. It allows to directly compare both spike times and membrane potentials which are emulated by the hardware or are computed by the software simulator NEST, respectively, from within a single software scope. Membrane potential and spike timing dependent plasticity measurements are shown which illustrate the capabilities of the software framework and document the functionality of the chip.
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Brüderle, D., Grübl, A., Meier, K., Mueller, E., Schemmel, J. (2007). A Software Framework for Tuning the Dynamics of Neuromorphic Silicon Towards Biology. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_59
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DOI: https://doi.org/10.1007/978-3-540-73007-1_59
Publisher Name: Springer, Berlin, Heidelberg
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