Six networks on a universal neuromorphic computing substrate.

Six networks on a universal neuromorphic computing substrate.
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DOI:
10.3389/fnins.2013.00011
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发表时间:
2013
影响因子:
4.3
通讯作者:
Meier K
Meier K
中科院分区:
医学2区
文献类型:
--
作者:
Pfeil T;Grübl A;Jeltsch S;Müller E;Müller P;Petrovici MA;Schmuker M;Brüderle D;Schemmel J;Meier K

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在这项研究中,我们提出了一个高度可配置的神经形态计算基板,并使用它来模拟几种类型的神经网络。该系统的核心是一个混合信号芯片,具有神经元和突触的模拟实现以及动作电位的数字传输。该仿真装置被明确设计为通用神经网络仿真器,其主要优点是其固有的并行性和与传统计算机相比的高加速因子。它的可配置性允许实现几乎任意的网络拓扑结构和使用广泛变化的神经元和突触参数。固定模式噪声固有的模拟电路是减少校准程序。集成的开发环境允许神经科学家在没有任何神经形态电路设计的先验知识的情况下操作该设备。作为系统功能的展示,我们描述了六个不同的神经网络的成功仿真,这些网络涵盖了广泛的结构和功能。
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.
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