Dissertation > Biological Sciences > Physiology > Neurophysiology

Complexity analysis of retinal ganglion -cell non- classical receptive field model of brain signals

Author CaoYang
Tutor GuFanJi
School Fudan University
Course Biophysics
Keywords Retina Ganglion cells Non-classical receptive field Orientation tendency Model Two-dimensional complexity Optical imaging of endogenous Orientation Function Chart Intraocular pressure Reversible inactivation
Type PhD thesis
Year 2005
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To test the assumption that the non-classical receptive field of retinal ganglion cells might be formed from the mutual inhibition among the amacrine cells, and to explore the possible mechanism underlying the orientation bias, a three-layer model of retinal ganglion cells was constructed. Both area responses and orientation bias were simulated. Results hint that mutual inhibition among amacrine cells may play a key role in the formation of extended surround in the non-classical receptive field of the retinal ganglion cell, and the orientation bias may mainly be decided by the elliptic shape of thereceptive field of the ganglion cell.2D Co complexity was used to analyze the orientation maps recorded by optical imaging based on intrinsic signals, which is a powerful method to view the activities of neural assembly in the cortex of animals in vivo, especially the detailed functional architecture of visual cortex. Results have shown that 2D Co complexity could reveal the occurrence of the orientation preference map in the visual cortex, and describe the variance of the neural responses in the primary visual cortex under high intraocular pressure, and under the reversible inactivation of PLMS. This suggests 2D Co complexity could be used as a new quantitative measure for the intrinsic optical images.The 2nd order ApEn complexity of EEG signals from a patient in coma and in brain death was estimated. Results show that compared with coma state, the 2nd order ApEn complexity under brain death state is significant reduced, which suggested the significant reduction of nonstationarity degree may be one of the characteristics of EEG during brain death.

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