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首页> 外文期刊>Journal of physiology, Paris. >The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks.
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The role of the asymptotic dynamics in the design of FPGA-based hardware implementations of gIF-type neural networks.

机译：渐近动力学在基于FPGA的基于FPGA的神经网络硬件实现中的作用。

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摘要

This paper presents a numerical analysis of the role of asymptotic dynamics in the design of hardware-based implementations of the generalised integrate-and-fire (gIF) neuron models. These proposed implementations are based on extensions of the discrete-time spiking neuron model, which was introduced by Soula et al., and have been implemented on Field Programmable Gate Array (FPGA) devices using fixed-point arithmetic. Mathematical studies conducted by Cessac have evidenced the existence of three main regimes (neural death, periodic and chaotic regimes) in the activity of such neuron models. These activity regimes are characterised in hardware by considering a precision analysis in the design of an architecture for an FPGA-based implementation. The proposed approach, although based on gIF neuron models and FPGA hardware, can be extended to more complex neuron models as well as to different in silico implementations.
机译：本文介绍了渐近动力学在广义集成和火（GIF）神经元模型的基于硬件的实施设计中的作用的数值分析。 这些建议的实现基于由Soula等人引入的离散时间尖峰神经元模型的扩展，并且已经在现场可编程栅极阵列（FPGA）设备上使用了定点算术来实现。 Cessac进行的数学研究证明了在这种神经元模型的活动中存在三个主要制度（神经死亡，周期性和混沌制度）。 这些活动制度通过考虑在基于FPGA的实现的架构设计中的精确分析来表征硬件。 虽然基于GIF神经元模型和FPGA硬件，但可以将所提出的方法扩展到更复杂的神经元模型以及硅实施中的不同。

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