Variability and coding efficiency of noisy neural spike encoders
Variability and coding efficiency of noisy neural spike encoders
复制标题
DOI：
10.1016/s0303-2647(01)00139-3
复制
发表时间：
2001-09-01
期刊：
影响因子：
1.6
通讯作者：
Koch, C
中科院分区：
文献类型：
--
作者：
Steinmetz, PN;Manwani, A;Koch, C
将突触输入编码为一系列动作电位是神经细胞的基本功能。虽然在体内记录的尖峰序列已被证明是高度可变的，但目前还不清楚尖峰定时的可变性是否代表对随时间变化的突触输入的忠实编码或尖峰编码机制中固有的噪声。据报道，尖峰定时变异性是更明显的恒定的，不变的输入比丰富的时间结构的输入。这可能对神经编码的性质产生重大影响，特别是如果神经元之间的尖峰和时间同步的精确定时被用来表示神经系统中的信息。为了研究潜在的功能作用的尖峰定时变异性，我们估计的分数尖峰定时变异性，传达信息的输入为两种类型的嘈杂的尖峰编码器-一个集成和消防模型与随机选择的阈值和模型的补丁的神经元膜随机Na+和K+通道服从霍奇金-赫胥黎动力学。通过使用最佳线性均方估计从输出锋电位序列重建输入刺激来评估信号编码的质量。噪声神经元模型的尖峰生成的估计性能的比较，使我们能够评估神经元噪声的神经编码的功效的影响。这两个模型的结果表明，尖峰时间的可变性降低的能力，尖峰列车编码快速随时间变化的刺激。此外，与先前研究的预期相反，我们发现，噪声尖峰编码模型编码缓慢变化的刺激比快速变化的更有效。(C)2001爱思唯尔科学爱尔兰有限公司保留所有权利。
Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders - an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na+ and K+ channels obeying Hodgkin-Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.