Department of Mathematical Sciences, University of Essex, Wivenhoe Park, UK.
Data Science Studio - IBM Netherlands, Amsterdam, The Netherlands.
Neural Netw. 2019 Jan;109:90-102. doi: 10.1016/j.neunet.2018.10.008. Epub 2018 Oct 23.
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.
信息需要进行适当编码，才能在物理媒体上可靠传输。同样，神经元也有自己的编码方式来在大脑中传递信息。尽管众所周知，神经元通过几种时空编码协议的集合来交换信息，但每种编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的一个重大谜团。本文通过对化学和电耦合 Hindmarsh-Rose 尖峰神经元的小尺寸网络进行建模，揭示了这一点。我们专注于一类由神经元膜电位和相位产生的时间和发放率编码，并通过数值估计互信息率（即信息交换率）来量化它们的性能。我们的结果表明，发放率和尖峰间隔编码对加性高斯白噪声更具鲁棒性。在一个由四个相互连接的神经元组成的网络中，在没有这种噪声的情况下，使用尖峰间隔和发放率编码进行信息交换的最大速率的神经元对在网络中不是相邻的，而尖峰时间和相位编码（时间）则促进了相邻神经元的信息交换。如果这一结果能够扩展到更大的神经网络，那么这表明小的微电路可能更愿意使用时间编码（尖峰时间和相位编码）来交换信息，而在宏观尺度上，由于大脑的稀疏性，通常会有不直接连接的神经元对，因此发放率和尖峰间隔编码将是最有效的编码。
