School of Electrical Engineering, Korea University, Seoul, 02841, South Korea.
Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, South Korea.
Sci Rep. 2024 Nov 28;14(1):29623. doi: 10.1038/s41598-024-81029-2.
Various spike patterns from sensory/motor neurons provide information about the dynamic sensory stimuli. Based on the information theory, neuroscientists have revealed the influence of spike variables on information transmission. Among diverse spike variables, inter-trial heterogeneity, known as jitter, has been observed in physiological neuron activity and responses to artificial stimuli, and it is recognized to contribute to information transmission. However, the relationship between inter-trial heterogeneity and information remains unexplored. Therefore, understanding how jitter impacts the heterogeneity of spiking activities and information encoding is crucial, as it offers insights into stimulus conditions and the efficiency of neural systems. Here, we systematically explored how neural information is altered by number of neurons as well as by each of three fundamental spiking characteristics: mean firing rate (MFR), duration, and cross-correlation (spike time tiling coefficient; STTC). First, we generated groups of spike trains to have specific average values for those characteristics. Second, we quantified the transmitted information rate as a function of each parameter. As population size, MFR, and duration increased, the information rate was enhanced but gradually saturated with further increments in number of cells and MFR. Regarding the cross-correlation level, homogeneous and heterogeneous spike trains (STTC = 0.9 and 0.1) showed the lowest and highest information transmission, respectively. Interestingly however, when jitters were added to mimic physiological noisy environment, the information was reduced by ~ 46% for the spike trains with STTC = 0.1 but rather substantially increased by ~ 63% for the spike trains with STTC = 0.9. Our study suggests that optimizing various spiking characteristics may enhance the robustness and amount of neural information transmitted.
各种来自感觉/运动神经元的尖峰模式提供了关于动态感觉刺激的信息。基于信息论，神经科学家揭示了尖峰变量对信息传递的影响。在各种尖峰变量中，在生理神经元活动和对人工刺激的反应中观察到了跨试验异质性，也称为抖动，并且被认为有助于信息传递。然而，跨试验异质性和信息之间的关系尚未得到探索。因此，了解抖动如何影响尖峰活动和信息编码的异质性至关重要，因为它提供了对刺激条件和神经系统效率的深入了解。在这里，我们系统地研究了神经元数量以及三种基本尖峰特征（平均发放率（MFR）、持续时间和互相关（尖峰时间平铺系数；STTC））如何改变神经信息。首先，我们生成了具有特定特征平均值的尖峰序列组。其次，我们将信息传输率作为每个参数的函数进行量化。随着群体大小、MFR 和持续时间的增加，信息率得到了提高，但随着细胞数量和 MFR 的进一步增加，信息率逐渐饱和。关于互相关水平，同质和异质尖峰序列（STTC = 0.9 和 0.1）分别显示了最低和最高的信息传输。然而，有趣的是，当添加抖动以模拟生理噪声环境时，STTC = 0.1 的尖峰序列的信息减少了约 46%，而 STTC = 0.9 的尖峰序列的信息增加了约 63%。我们的研究表明，优化各种尖峰特征可以提高神经信息传输的稳健性和数量。
