A Systematic Method for Configuring VLSI Networks of Spiking Neurons
A Systematic Method for Configuring VLSI Networks of Spiking Neurons
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DOI：
10.1162/neco_a_00182
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发表时间：
2011-10-01
影响因子：
2.9
通讯作者：
Douglas, Rodney
中科院分区：
文献类型：
--
作者：
Neftci, Emre;Chicca, Elisabetta;Douglas, Rodney
越来越多的研究小组正在开发定制的混合模拟/数字超大规模集成电路（VLSI）芯片和系统，这些芯片和系统实现了数百到数千个具有生物物理学现实动力学的尖峰神经元，其目的是在硬件和机器人系统中模拟大脑的真实世界行为，而不是简单地在通用数字计算机上模拟它们的性能。虽然这些仿真系统的电子工程方面进展顺利，但由于缺乏合适的高级配置方法，限制了对类脑任务进行实际仿真的进展，而这种方法已经在通用计算机上开发了几十年。关键的困难在于CMOS电子模拟的动态是由晶体管偏置决定的，而晶体管偏置并不简单地映射到神经元及其网络的典型抽象数学模型中使用的参数类型和值。在这里，我们提供了一个解决这个困难的一般方法。我们描述了一个参数映射技术，允许自动配置的VLSI神经网络，使他们的电子仿真符合一个更高层次的神经元模拟。我们表明，通过我们的方法配置的神经元表现出与软件模拟神经元中观察到的相同的尖峰定时统计和时间动态，特别是，递归VLSI神经网络的关键参数（例如。例如，在一个实施例中，实现软赢家通吃）。所提出的方法允许软件仿真与硬件仿真之间的无缝集成和抽象神经元模型的参数之间的互译性和它们的仿真同行。最重要的是，我们的方法提供了一个高层次的任务配置语言的神经形态VLSI系统的路线。
An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e. g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.