Cross-Correlograms for Neuronal Spike Trains. Different Types of Temporal Correlation in Neocortex, their Origin and Significance

2010 ◽  
pp. 53-96 ◽  
Author(s):  
Lionel G Nowak ◽  
Jean Bullier
Keyword(s):  
Temporal Correlation ◽  
Spike Trains ◽  
Neuronal Spike ◽  
Different Types ◽  
Neuronal Spike Trains

A Computational Model as Neurodecoder Based on Synchronous Oscillation in the Visual Cortex

2003 ◽  
Vol 15 (10) ◽  
pp. 2399-2418 ◽  
Author(s):  
Zhao Songnian ◽  
Xiong Xiaoyun ◽  
Yao Guozheng ◽  
Fu Zhi
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Visual Information ◽  
Spike Trains ◽  
Spike Timing ◽  
Contour Detection ◽  
System Level ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
External Stimuli

Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.

Renewal point processes and neuronal spike trains

1970 ◽  
Vol 6 ◽  
pp. 331-335 ◽  
Author(s):  
S.K. Srinivasan ◽  
G. Rajamannar
Keyword(s):  
Point Processes ◽  
Spike Trains ◽  
Neuronal Spike ◽  
Neuronal Spike Trains

scholarly journals Multi-scale detection of rate and variance changes in neuronal spike trains

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
Stefan Albert ◽  
Michael Messer ◽  
Brian Rummell ◽  
Torfi Sigurdsson ◽  
Gaby Schneider
Keyword(s):  
Spike Trains ◽  
Multi Scale ◽  
Neuronal Spike ◽  
Neuronal Spike Trains ◽  
Scale Detection

Note on the coefficient of variations of neuronal spike trains

2017 ◽  
Vol 111 (3-4) ◽  
pp. 229-235 ◽  
Author(s):  
Johannes Lengler ◽  
Angelika Steger
Keyword(s):  
Spike Trains ◽  
Neuronal Spike ◽  
Neuronal Spike Trains

Distribution-free graphical and statistical analysis of serial dependence in neuronal spike trains

1996 ◽  
Vol 64 (1) ◽  
pp. 25-37 ◽  
Author(s):  
Barry K. Rhoades ◽  
Jon C. Weil ◽  
Jacek M. Kowalski ◽  
Guenter W. Gross
Keyword(s):  
Statistical Analysis ◽  
Spike Trains ◽  
Serial Dependence ◽  
Distribution Free ◽  
Neuronal Spike ◽  
Neuronal Spike Trains
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