Entropy measures of signal in the presence of noise: evidence for 'byte' versus 'bit' processing in the nervous system.
作者信息
Klemm W R, Sherry C J
出版信息
Experientia. 1981 Jan 15;37(1):55-8. doi: 10.1007/BF01965566.
PMID:7202669
Abstract
A method for detecting signal in the presence of noise in a highly specific way is described. Using action potential interval data from 12 neurons in rat cerebellum, we have demonstrated that the sequential ordering of spike intervals contains both noise and signal. We have identified and quantified the magnitude of relative entropy (uncertainty) of specified sets of interval patterns, ranging in length from 3-5 successive intervals. Some of these sets have exceptionally low entropy and thus seem to be especially meaningful as a set ('word') to the brain.
摘要
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本文引用的文献
1
The statistical relationship between the "entropy" of a neuronal signal and its variability.神经元信号的“熵”与其变异性之间的统计关系。
Int J Neurosci. 1980;11(2):109-13. doi: 10.3109/00207458009150333.
2
Higher-order patterns of neuronal interspike intervals (trigrams and quadgrams) analyzed as self-adjusting sets of ratios.作为自调整比率集进行分析的神经元峰间间隔的高阶模式（三元组和四元组）。
Int J Neurosci. 1973 Apr;5(4):171-82. doi: 10.3109/00207457309149470.
3
Temporal patterns, their distribution and redundancy in trains of spontaneous neuronal spike intervals of the feline hippocampus studied with a non-parametric technique.
Brain Res. 1977 Apr 8;125(1):65-89. doi: 10.1016/0006-8993(77)90360-2.
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