NDLI: Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells

Content Provider	Springer Nature Link
Author	Gong, Han Yan Zhang, Ying Ying Liang, Pei Ji Zhang, Pu Ming
Copyright Year	2010
Abstract	Correlation between spike trains or neurons sometimes indicates certain neural coding rules in the visual system. In this paper, the relationship between spike timing correlation and pattern correlation is discussed, and their ability to represent stimulus features is compared to examine their coding strategies not only in individual neurons but also in population. Two kinds of stimuli, natural movies and checkerboard, are used to arouse firing activities in chicken retinal ganglion cells. The spike timing correlation and pattern correlation are calculated by cross-correlation function and Lempel–Ziv distance respectively. According to the correlation values, it is demonstrated that spike trains with similar spike patterns are not necessarily concerted in firing time. Moreover, spike pattern correlation values between individual neurons’ responses reflect the difference of natural movies and checkerboard; neurons cooperate with each other with higher pattern correlation values which represent spatiotemporal correlations during response to natural movies. Spike timing does not reflect stimulus features as obvious as spike patterns, caused by their particular coding properties or physiological foundation. As a result, separating the pattern correlation out of traditional timing correlation concept uncover additional insight in neural coding.
Starting Page	337
Ending Page	346
Page Count	10
File Format	PDF
ISSN	18714080
Journal	Cognitive Neurodynamics
Volume Number	4
Issue Number	4
e-ISSN	18714099
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-06-29
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neural coding Spike pattern correlation Spike timing correlation Cross-correlation Lempel–Ziv distance Cognitive Psychology Biochemistry Artificial Intelligence (incl. Robotics) Computer Science Neurosciences Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience

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