臺灣博碩士論文加值系統

視網膜為眼睛內的感光神經系統，它將接受的光訊號轉為電訊號傳至大腦。然而視網膜發放的電訊號與接受到的光刺激間的編碼關係仍不清楚，因此，我們利用發光二級體產生亮度隨時間變化的空間均勻光刺激，並藉由多電極陣列紀錄離體牛蛙視網膜的電訊號，目的在於瞭解視網膜發放頻率與刺激亮度間的關係。結果顯示，視網膜的發放頻率和刺激的平均亮度、亮度變化量與亮度變化率相關。更進一步，我們利用連續隨機亮度刺激，計算發放頻率與相對亮度間的互信息量。結果顯示當刺激的亮度變化量越大，或平均亮度越小時，互信息量越大，此結果與認知神經科學中的「韋伯律」相符合。最後，已知視網膜在接收刺激後，需要一段延遲時間後才發放。在發放頻率與不同相關時間的刺激間的互信息實驗結果顯示，此延遲時間與刺激的相關時間有關，當刺激的相關時間越大時，視網膜的延遲時間越短，視網膜最後甚至可以預測未來訊號。

A retina receives light stimulation and transforms the detected signal into spikes, which are then transmitted to the brain. However, how light stimulation information is conveyed by spike trains is not fully understood. Here, we utilize multi-electrode array (MEA) to record firings of the Bullfrog’s retinal ganglion cells (RGCs) under a time-varying whole field light stimulation I(t). Our goal is to understand the information shared between
stimulus light intensity (I) and the firing rate (r) of RGCs. Our result shows that r will be affected by the mean intensity, Ibar, the deviation from the mean, ΔI, and the rate of change, dI/dt. To quantify the amount of information encoded by the retina, the stochastic
stimulation is applied and the mutual information (MI) between I and r is calculated. We find that the MI increases as ΔI increases or I decreases, which is consistent with Weber’s law. Finally, the result from the stochastic stimulation with different correlation
time shows that if the correlation time of the stimulus is longer, the retina takes shorter processing time and can even predict future stimulus.

誌謝v
摘要vii
Abstract ix
Contents xi
Symbol xxv
1 Introduction 1
1.1 Retina 1
1.2 Weber-Fechner Law 4
1.3 Encoding the Light Intensity 6
1.3.1 The firing rate of RGCs 6
1.3.2 Information between firing rate and light intensity 7
1.4 Predictive Power in a Retina 7
2 Material and Method 9
2.1 Introduction 9
2.2 Sample Preparation 10
2.3 Experimental setups 10
2.3.1 The stimulus control device 10
2.3.2 Light intensity calibration 13
2.4 Multi-Electrode Array (MEA) 13
2.4.1 Setup 13
2.4.2 Signal analysis 15
2.5 Stimulation Forms 16
2.5.1 Periodic stimulus 1 : square wave 18
2.5.2 Periodic stimulus 2 : ramp stimuli 18
2.5.3 Periodic stimulus 3 : sine-wave stimuli 19
2.5.4 Pseudo-periodic Stimulus : Different Intensity Square Wave 19
2.5.5 Stochastic stimulus 1 : Hidden Markovian Model (HMM) 20
2.5.6 Stochastic stimulus 2 : Ornstein–Uhlenbeck process (OU) 22
2.6 Analysis Methods 24
2.6.1 Period stimuli: maximum firing rate and firing time 24
2.6.2 Pseudo-periodic and stochastic stimuli: mutual information 25
2.7 Experimental Condition 27
3 Result 29
3.1 Square Wave 29
3.1.1 The same I with different I1 30
3.1.2 The same I1 with different I 31
3.1.3 The same ΔI with different I 31
3.2 Ramp Stimuli 32
3.2.1 The same I with different ΔT 33
3.2.2 The same ΔT with different I 34
3.3 Sine-Wave Stimuli 35
3.3.1 The same Period with different amplitude 36
3.3.2 The same amplitude with different period 37
3.4 Different Intensity Square Wave Stimuli 38
3.5 Hidden Markovian Model 38
3.5.1 HMM with different contrast 39
3.5.2 HMM with different mean intensity 39
3.5.3 HMM with different correlation time 40
3.6 Ornstein–Uhlenbeck Process 42
3.6.1 Compare the mutual information under OU and HMM stimulation 42
3.6.2 OU with different correlation time 42
4 Conclusion and Discussion 45
4.1 Firing Rate and Light Intensity : Consistent with Weber’s Law 45
4.2 The Effect of Correlation Time on MI(t′-t) under HMM Stimulation 46
4.2.1 The width of MI(t′- t) is proportional to the correlation time of the stimulus 46
4.2.2 2-peak in MI(t′ - t) may come from the ON/OFF pathways 46
4.2.3 The tshift under HMM stimulation 48
4.3 The tshift under OU Stimulation 49
4.4 Linear-Nonlinear Model 49
4.5 The Encoding Strategy by ON/OFF Pathways 51
4.6 Conclusion and Future Work 52
REFERENCE 53
A Pretest Response 57
A.1 Spontaneous Firing 57
A.2 ON/OFF Response 58
A.3 Intensity Spike Trigger Average 59
B Binning time and state number 61
C Dissection Recipe 63
C.1 Preparation 63
C.1.1 Ringer’s solution 63
C.1.2 Tools 63
C.2 Dissection 64
C.3 Fixation 65

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