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A current-mode conductance-based silicon neuron for address-event neuromorphic systems

机译:基于电流模式电导的硅神经元,用于地址事件神经形态系统

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Silicon neuron circuits emulate the electrophysiological behavior of real neurons. Many circuits can be integrated on a single very large scale integration (VLSI) device, and form large networks of spiking neurons. Connectivity among neurons can be achieved by using time multiplexing and fast asynchronous digital circuits. As the basic characteristics of the silicon neurons are determined at design time, and cannot be changed after the chip is fabricated, it is crucial to implement a circuit which represents an accurate model of real neurons, but at the same time is compact, low-power and compatible with asynchronous logic. Here we present a current-mode conductance-based neuron circuit, with spike-frequency adaptation, refractory period, and bio-physically realistic dynamics which is compact, low-power and compatible with fast asynchronous digital circuits.
机译:硅神经元电路模拟实际神经元的电生理行为。许多电路可以集成在单个超大规模集成(VLSI)设备上,并形成尖峰神经元的大型网络。神经元之间的连通性可以通过使用时分复用和快速异步数字电路来实现。由于硅神经元的基本特征是在设计时确定的,并且在芯片制造后不能更改,因此实现代表真实神​​经元精确模型的电路至关重要,但同时又要紧凑,低功耗。强大且与异步逻辑兼容。在这里,我们介绍了一种基于电流模式电导的神经元电路,具有峰值频率自适应,不应期和生物物理现实的动态特性,该结构紧凑,低功耗并且与快速异步数字电路兼容。

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