Kirchhoff Institute for Physics, University of Heidelberg Heidelberg, Germany.
Front Comput Neurosci. 2010 Oct 8;4:129. doi: 10.3389/fncom.2010.00129. eCollection 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 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.
神经形态硬件工程的最新进展使得混合信号 VLSI 神经网络模型成为神经科学研究工具和大规模并行计算设备的有前途的候选者，特别是对于那些需要消耗软件模拟计算能力的任务。尽管如此，像所有模拟硬件系统一样，神经形态模型受到配置受限和与生产相关的器件特性波动的影响。由于未来的系统也将不可避免地在单元级别上表现出这种非均匀性，因此自我调节特性成为其成功运行的关键要求。通过应用皮质启发的自调节网络架构，我们表明，在神经形态硬件系统上模拟的通用尖峰神经网络的活动可以保持在生物现实的发射范围内，并对晶体管级别的变化具有显著的鲁棒性。作为工程实践中的第一种方法，我们在 I&F 神经元的模拟 VLSI 模型中实现的短期突触抑制和易化机制在网络级别的稳定化方面得到了功能性的利用。我们展示了从硬件模型和比较软件模拟中获得的实验数据，证明了所采用的范例在神经形态 VLSI 器件中的适用性。
