HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Songnian, Z. Articles by Zhi, F. Search for Related Content PubMed PubMed Citation Articles by Songnian, Z. Articles by Zhi, F.

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

zsnzhao{at}yeah.net, LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Xiong Xiaoyun

xiongxy{at}nsfc.gov.cn, National Science Foundation of China, Beijing 100085, China

Yao Guozheng

yaogz{at}cis.pku.edu.cn, Information Science Center, Peking University, Beijing 100871, China

Fu Zhi

jfu{at}labs.mot.com, Department of Computer Science, North Carolina State University, Raleigh, NC 27695, U.S.A.

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 multiscaled 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.