基于互反抑制的自振荡神经网络局部膜动态偏置CMOS LIF神经元。

Mannhee Cho, Minil Kang, Minseong Um, Hangue Park, Hyung-Min Lee
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引用次数: 0

摘要

本文提出了一种基于cmos的神经元网络,可以模拟生物神经振荡器模型中的自振荡偏置行为。基于LIF (leaky integrative -and-fire)神经元模型,本文提出的神经元回路采用互反抑制网络和突触疲劳的概念以及兴奋性驱动刺激来复制膜电位的胞外流体偏倚。在基础神经元电路的顶部,一个激励积分器集成正、负兴奋输入尖峰来刺激膜电位偏置,一个偏置控制器接收抑制驱动输入并根据膜电位偏置水平产生输出抑制驱动。所提出的具有抑制性连接的多个神经元网络可以产生振荡膜电位偏差,这可以用作神经元尖峰放电的局部动态阈值,从而产生自模式输出尖峰,如开关或动态放电速率模式。该神经元网络采用250 nm CMOS工艺实现,工作电压为2.5 V,全工作时每个神经元平均功耗为99.31μW。测量了不同输入条件下的工作波形,可以产生多种输出模式。为了验证操作的稳定性,我们从32个神经元中测量了由于过程变化而导致的输出信号的方差,显示每个输入尖峰的膜电位增益的标准差为18%,膜电位偏置的振荡周期的标准差为12%。结果表明,所提出的神经元网络能够复制生物神经元模型的自振荡行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CMOS LIF Neurons with Local Membrane Dynamic Biasing Based on Reciprocal Inhibition for Self-Oscillatory Neural Networks.

This paper presents a CMOS-based neuron network that can emulate self-oscillatory biasing behaviors found in biological neural oscillator models. Based on leaky integrate-and-fire (LIF) neuron models, the proposed neuron circuit adopts the concept of reciprocal inhibitory network and synaptic fatigue as well as excitatory drive stimulation for replicating extracellular fluidic biasing of membrane potentials. On top of the base neuron circuit, an excitation integrator integrates positive and negative excitatory input spikes to stimulate the membrane potential bias, and a bias controller receives inhibitory drive input and generates output inhibitory drives depending on the membrane potential bias level. The proposed networks of multiple neurons with inhibitory connections can generate oscillating membrane potential biases, which can be used as local dynamic thresholds for neuron spike firing, resulting in self-patterned output spikes such as switching or dynamic firing rate patterns. The proposed neuron network was implemented with 250-nm CMOS process operating at the supply voltage of 2.5 V and consuming average power of 99.31μW per neuron during full operation. Operation waveforms were measured in various input conditions which can produce multiple output patterns. Variances in output signals due to process variation were measured from 32 neurons to verify the stability of operation, showing the standard deviation of 18% in the membrane potential gain per input spike and 12% in oscillation periods of the membrane potential bias. The results verified that the proposed neuron network can replicate the self-oscillatory behaviors of biological neuron models.

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