Recent developments in neuromorphic hardware engineering make mixed-signal VLSI neural network models promising candidates for neuroscientific research tools and massively parallel computing devices, especially for tasks which exhaust the computing power of software simulations. Still, like all analog hardware systems, neuromorphic models suffer from a constricted configurability and production-related fluctuations of device characteristics. Since also future systems, involving ever-smaller structures, will inevitably exhibit such inhomogeneities on the unit level, self-regulation properties become a crucial requirement for their successful operation. By applying a cortically inspired self-adjusting network architecture, we show that the activity of generic spiking neural networks emulated on a neuromorphic hardware system can be kept within a biologically realistic firing regime and gain a remarkable robustness against transistor-level variations. As a first approach of this kind in engineering practice, the short-term synaptic depression and facilitation mechanisms implemented within an analog VLSI model of I&F neurons are functionally utilized for the purpose of network level stabilization. We present experimental data acquired both from the hardware model and from comparative software simulations which prove the applicability of the employed paradigm to neuromorphic VLSI devices.
Keywords: neuromorphic hardware, spiking neural networks, self-regulation, short-term synaptic plasticity, robustness, leaky integrate-and-fire neuron, parallel computing, PCSIM
Citation: Bill J, Schuch K, Brüderle D, Schemmel J, Maass W and Meier K (2010) Compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity. Front. Comput. Neurosci. 4:129. doi: 10.3389/fncom.2010.00129
Received: 25 February 2010;
Paper pending published: 01 March 2010;
Accepted: 11 August 2010; Published online: 08 October 2010
Edited by:Stefano Fusi, Columbia University, USA
Reviewed by:Larry F. Abbott, Columbia University, USA
Copyright: © 2010 Bill, Schuch, Brüderle, Schemmel, Maass and Meier. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence: Johannes Bill, Institute for Theoretical Computer Science, Graz University of Technology, Inffeldgasse 16b/1, A–8010 Graz, Austria. e-mail: email@example.com