模型神经通信网络中神经编码的性能评估
Evaluating performance of neural codes in model neural communication networks
摘要：
信息需要适当地编码，才能在物理介质上可靠地传输。同样，神经元也有自己的代码来传达大脑中的信息。尽管众所周知，神经元使用多个时空编码协议来交换信息，但每个代码的适用性及其作为网络参数和外部刺激的函数的性能仍然是神经科学中最大的谜团之一。本文揭示了这一点，通过建模化学和电耦合Hindmarsh-Rose尖峰神经元的小规模网络。我们专注于一类的时间和发射率代码，导致神经元的膜电位和相位，并量化其性能估计的互信息率，又名信息交换的速率。我们的研究结果表明，发射率和尖峰间隔码是更强大的加性高斯白噪声。在四个相互连接的神经元的网络中，在没有这种噪声的情况下，具有最大的信息交换速率的神经元对使用尖峰间期和发射速率代码在网络中不相邻，而尖峰定时和相位代码（时间）促进相邻神经元的大的信息交换速率。如果这一结果可以扩展到更大的神经网络，那么它将表明小型微电路将更好地使用时间代码（尖峰定时和相位代码）交换信息，而在宏观尺度上，由于大脑的稀疏性，通常会有神经元对不直接连接，发射率和尖峰间隔代码将是最有效的代码。
英文摘要：
Information needs to be appropriately encoded to be reliably transmitted over physical media. Similarly, neurons have their own codes to convey information in the brain. Even though it is well-known that neurons exchange information using a pool of several protocols of spatio-temporal encodings, the suitability of each code and their performance as a function of network parameters and external stimuli is still one of the great mysteries in neuroscience. This paper sheds light on this by modeling small-size networks of chemically and electrically coupled Hindmarsh-Rose spiking neurons. We focus on a class of temporal and firing-rate codes that result from neurons' membrane-potentials and phases, and quantify numerically their performance estimating the Mutual Information Rate, aka the rate of information exchange. Our results suggest that the firing-rate and interspike-intervals codes are more robust to additive Gaussian white noise. In a network of four interconnected neurons and in the absence of such noise, pairs of neurons that have the largest rate of information exchange using the interspike-intervals and firing-rate codes are not adjacent in the network, whereas spike-timings and phase codes (temporal) promote large rate of information exchange for adjacent neurons. If that result would have been possible to extend to larger neural networks, it would suggest that small microcircuits would preferably exchange information using temporal codes (spike-timings and phase codes), whereas on the macroscopic scale, where there would be typically pairs of neurons not directly connected due to the brain's sparsity, firing-rate and interspike-intervals codes would be the most efficient codes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
