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Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries.
Journal of Computational Neuroscience ( IF 1.5 ) Pub Date : 2019-06-04 , DOI: 10.1007/s10827-019-00714-8
Pedro D Maia 1, 2 , Ashish Raj 1, 2 , J Nathan Kutz 3
Affiliation  

We introduce a computational model for the cellular level effects of firing rate filtering due to the major forms of neuronal injury, including demyelination and axonal swellings. Based upon experimental and computational observations, we posit simple phenomenological input/output rules describing spike train distortions and demonstrate that slow-gamma frequencies in the 38–41 Hz range emerge as the most robust to injury. Our signal-processing model allows us to derive firing rate filters at the cellular level for impaired neural activity with minimal assumptions. Specifically, we model eight experimentally observed spike train transformations by discrete-time filters, including those associated with increasing refractoriness and intermittent blockage. Continuous counterparts for the filters are also obtained by approximating neuronal firing rates from spike trains convolved with causal and Gaussian kernels. The proposed signal processing framework, which is robust to model parameter calibration, is an abstraction of the major cellular-level pathologies associated with neurodegenerative diseases and traumatic brain injuries that affect spike train propagation and impair neuronal network functionality. Our filters are well aligned with the spectrum of dynamic memory fields including working memory, visual consciousness, and other higher cognitive functions that operate in a frequency band that is - at a single cell level - optimally guarded against common types of pathological effects. In contrast, higher-frequency neural encoding, such as is observed with short-term memory, are susceptible to neurodegeneration and injury.

中文翻译:

最佳伽玛频率可以最佳地防止神经退行性疾病和脑外伤的影响。

我们针对由于主要形式的神经元损伤(包括脱髓鞘和轴突肿胀)导致的射速过滤对细胞水平的影响引入了计算模型。根据实验和计算结果,我们提出了简单的现象学输入/输出规则来描述尖峰脉冲串失真,并证明38-41 Hz范围内的慢伽玛频率对伤害最强。我们的信号处理模型允许我们以最小的假设在细胞水平上导出神经活动受损的发射率滤波器。具体来说,我们通过离散时间滤波器对八个实验观察到的尖峰序列转换进行建模,包括与增加耐火度和间歇性堵塞相关的那些。滤波器的连续对应项也可以通过从与因果和高斯核卷积的尖峰序列中估计神经元激发速率来获得。所提出的信号处理框架具有强大的模型参数校准能力,它是与神经退行性疾病和创伤性脑损伤相关的主要细胞水平病理学的抽象,神经病理性神经病影响刺突训练传播并损害神经元网络功能。我们的过滤器与动态记忆域的频谱非常吻合,包括工作记忆,视觉意识和其他更高的认知功能,它们在单个细胞水平的频带内运行,可以最佳地防御常见类型的病理效应。相反,高频神经编码(例如在短期记忆中观察到的)
更新日期:2019-06-04
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