Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity - Bill, Johannes; Schuch, Klaus; Brüderle, Daniel; Schemmel, Johannes; Maass, Wolfgang; Meier, Karlheinz (Frontiers in Computational Neuroscience, 2010)
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 inhomogeities 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.