title: Six networks on a universal neuromorphic computing substrate.
abstract: In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality.
authors: Kirchhoff-Institute for Physics, Universität Heidelberg Heidelberg, Germany.
authors: Thomas
authors: T
authors: Pfeil
authors: Andreas
authors: A
authors: Grübl
authors: Sebastian
authors: S
authors: Jeltsch
authors: Eric
authors: E
authors: Müller
authors: Paul
authors: P
authors: Müller
authors: Mihai A
authors: MA
authors: Petrovici
authors: Michael
authors: M
authors: Schmuker
authors: Daniel
authors: D
authors: Brüderle
authors: Johannes
authors: J
authors: Schemmel
authors: Karlheinz
authors: K
authors: Meier
keywords: accelerated neuromorphic hardware system; classifier; cortical model; highly configurable; mixed-signal VLSI; soft winner-take-all; spiking neural networks; universal computing substrate