Paffi Alessandra, Camera Francesca, Apollonio Francesca, d'Inzeo Guglielmo, Liberti Micaela
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome Rome, Italy ; Italian Inter-University Center for the Study of Electromagnetic Fields and Biological Systems Genova, Italy.
Front Comput Neurosci. 2015 May 6;9:42. doi: 10.3389/fncom.2015.00042. eCollection 2015.
Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed.
在此，我们评估通过将外源噪声叠加到外部电刺激信号上，来改善受损神经元系统编码特性的可能性。该方法基于使用由类随机霍奇金-赫胥黎（HH）电路组成的数学神经元模型，其中内源性突触前输入的损伤被描述为阈下注入电流，而外源刺激信号是跨膜的正弦电压扰动。我们的结果表明，添加到正弦信号中的相关高斯噪声可通过随机共振（SR）现象显著提高受损系统的编码特性。这些结果表明，经过适当调整的外源噪声可以提高在神经元系统中使用的那些刺激技术的功效，在这些系统中，突触前感觉神经元受损，必须通过人工方式绕过。
