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Improvement of signal-to-noise ratio in parallel neuron arrays with spatially nearest neighbor correlated noise

机译:具有空间最近邻居相关噪声的并行神经元阵列中信噪比的改善

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摘要

We theoretically investigate the signal-to-noise ratio (SNR) of a parallel array of leaky integrate-and-fire (LIF) neurons that receives a weak periodic signal and uses spatially nearest neighbor correlated noise. By using linear response theory, we derive the analytic expression of the SNR. The results show that the amplitude of internal noise can be increased up to an optimal value, which corresponds to a maximum SNR. Given the existence of spatially nearest neighbor correlated noise in the neural ensemble, the SNR gain of the collective ensemble response can exceed unity, especially for a negative correlation. This nonlinear collective phenomenon of SNR gain amplification may be related to the array stochastic resonance. In addition, we show that the SNR can be improved by varying the number of neurons, frequency, and amplitude of the weak periodic signal. We expect that this investigation will be useful for both controlling the collective response of neurons and enhancing weak signal transmission.
机译:我们从理论上研究了泄漏的积分和发射(LIF)神经元的并行阵列的信噪比(SNR),该神经元接收微弱的周期性信号并使用空间最近的邻居相关噪声。利用线性响应理论,推导了信噪比的解析表达式。结果表明,内部噪声的幅度可以增加到最佳值,这对应于最大SNR。给定神经集合中存在空间上最邻近的相关噪声,集体集合响应的SNR增益可能超过1,特别是对于负相关。 SNR增益放大的这种非线性集体现象可能与阵列随机共振有关。此外,我们表明可以通过更改微弱周期信号的神经元数量,频率和幅度来改善SNR。我们希望这项研究对于控制神经元的集体反应和增强微弱的信号传递都将是有用的。

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