Wang Jixuan, Deng Bin, Gao Tianshi, Wang Jiang, Tan Hong
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Front Neurosci. 2023 Jun 12;17:1193930. doi: 10.3389/fnins.2023.1193930. eCollection 2023.
The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear.
We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. Moreover, we use dynamic thresholds to explore the effect of SFA on output correlation. Furthermore, a simple phenomenological neuron model with a threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation.
The results show that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state, and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, SFA simulated by dynamic thresholds also decreases the output correlation. Furthermore, the simple phenomenological neuron model with a threshold-linear transfer function confirms the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, could together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation.
The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neurons. This study provides a link between cellular non-linear mechanisms and network coding strategies.
尖峰序列输出与成对神经元之间的相关性决定了神经群体编码，而这又依赖于单个神经元的平均放电率。作为一种重要的细胞编码策略，尖峰频率适应（SFA）调节着单个神经元的放电率。然而，SFA调节尖峰序列输出相关性的机制仍不清楚。
我们引入一个成对神经元模型，该模型接收相关输入以生成尖峰序列，并使用皮尔逊相关系数来衡量输出相关性。通过适应电流对SFA进行建模，以研究其对输出相关性的影响。此外，我们使用动态阈值来探索SFA对输出相关性的作用。此外，还利用一个具有阈值线性传递函数的简单唯象神经元模型来证实SFA对降低输出相关性的作用。
结果表明，适应电流通过降低单个神经元的放电率来降低输出相关性。在相关输入开始时，一个瞬态过程显示出峰峰间隔（ISI）减小，导致相关性暂时增加。当适应电流充分激活时，相关性达到稳态，ISI保持在较高值。通过增加适应电导实现的增强适应电流进一步降低了成对相关性。虽然时间和滑动窗口会影响相关性，但它们对SFA降低输出相关性的效果没有影响。此外，由动态阈值模拟的SFA也会降低输出相关性。此外，具有阈值线性传递函数的简单唯象神经元模型证实了SFA对降低输出相关性的作用。信号输入的强度和传递函数线性部分的斜率（后者可被SFA降低）共同调节输出相关性的强度。更强的SFA会降低斜率，从而降低输出相关性。
结果表明，SFA通过降低单个神经元的放电率来降低网络中与成对神经元的输出相关性。本研究提供了细胞非线性机制与网络编码策略之间的联系。
