視網膜對動態視覺的預測資訊的編碼｜國立清華大學博碩士論文庫

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研究生：	陳冠豪 Chen, Kuan-Hao
論文名稱：	視網膜對動態視覺的預測資訊的編碼 Encoding the Predictive Information of Visual Motion in the Retina
指導教授：	焦傳金 Chiao, Chuan-Chin 陳志強 Chan, Chi-Keung
口試委員:	羅中泉 Lo, Chung-Chuan 黎璧賢 Lai, Pik-Yin
學位類別：	碩士 Master
系所名稱：	生命科學暨醫學院 - 分子醫學研究所 Institute of Molecular Medicine
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	85
中文關鍵詞：	視網膜、預測、模型、隨機運動、互信息、負回饋
外文關鍵詞：	Retina, Prediction, Model, Stochastic motion, Mutual Information, Negative feedback
相關次數：	點閱：3 下載：0
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為了及時反應，動物一定要透過預測來克服神經處理的延遲。過去的研究已經證明視網膜的細胞能夠預測預測物體移動。為了去了解這樣的特性，我們給視網膜看隨機移動的物體，並用多電極去量測。物體移動的軌跡是由一種隨機過程產生，並且會由低通濾波過濾，而產生不同頻率的軌跡。我們接著計算神經細胞輸出與軌跡的相互資訊。並且我們發現有兩種細胞—可預測性與不可預測的細胞。可預測性與軌跡頻率也有關係。最後我們發展一種模型，這個模型包括前饋與反饋的抑制，並且這個抑制是來自於水平細胞。這種模型可以解釋為什麼視網膜能預測物體移動。

To produce timely responses, animals must conquer delays from visual pro cessing pathway by predicting motion. Previous studies [1] revealed that predic tive information of motion is encoded in spiking activities of retinal ganglion cells (RGCs) early in the visual pathway. In order to study the predictive properties of a retina in a more systematic manner, stimuli in the form of a stochastic moving bar are used in experiments with retinas from bull frogs in a multielectrode sys tem. We then investigated the predictive properties of single RGC by calculating the time shifted (δt) mutual information (MI(x,r;δt)) between spiking output (r(t)) from a single RGC and the bar trajectories (x(t)). Two kinds of cells are charac terized: predictive RGCs and nonpredictive RGCs. In order to further understand the mechanism of prediction, we develop a negative group delay model which is based on Voss＇s [2] paper to generate anticipatory responses. We extend our model to spatial version and use the same stimulation condition as we use in experiments. The model indicates that delayed negative feedback is crucial for producing antic ipation dynamics. Besides, we also show feedforward inhibition can also generate similar prediction dynamics. Besides, our feedback and feedforward model can also predict constant velocity moving bar [3]. After adding LPOU noises into con stant velocity moving bar, our model even predicts better than Berry gain control model [3] which explains anticipation of constant velocity moving bar. To sum up, our feedback and feedforward model can anticipate both stochastic and constant velocity moving bar with and without noises.

摘要    i
Abstract    ii
Acknowledgements
  Introduction to the retina    5
1    Organization of the retina    5
1.1    Photoreceptors    5
1.2    Horizontal cells    6
1.3    Bipolar cells    6
1.4    Amacrine cells    7
1.5    Ganglion cells    8
2    Anticipation in retina    8
2.1    Anticipation of a constant velocity moving bar    9
2.2    Anticipation of a stochastic moving bar    11
2.3    Review of previous works    11
3    Organization of the thesis    11
  Material and Method    13
1    MEA recordings in the bull frog retina    13
1.1    Tissue Preparation    13
1.2    Experimental setup (Fig. 2.2)    14
1.3    Recording and spike sorting    15
1.4    Calibration of stimulus    16
2    Protocols of stimulation    16
2.1    On­off test    16
2.2    Contrast spike triggered average (cSTA)    17
2.3    Random checkerboard    19
2.4    Stochastic moving bar    19
3    Data analysis    22
3.1    Receptive field analysis    22
3.2    Mutual information    23
3.3    Mutual information between light intensity and firing rate    25
  Results    27
1    Receptive field    27
2    Contrast tuning    29
2.1    On Off response    29
2.2    cSTA    30
3    Response of RGCs under stimulation of stochastic moving bar    31
3.1    Predictive (P) cell and non­predictive (NP) cell under HMM stimulation    31
3.2    Predictive (P) cell under OU stimulation    32
3.3    Predictive (P) cell and non­predictive (NP) cell under LPOU stimulation    32
3.4    MI between light intensity and firing rate    34
4    STA of a HMM bar    34
  Model    37
1    Feedback and Feedforward model (FBFF model)    37
1.1    Numerical method    39
2    Nonlinearity in FBFF model    39
3    MI calculation in FBFF model    39
4    Berry Gain control model    41
5    Gain control    43
5.1    Period doubling    43
5.2    Combination of FBFF model and gain control (FBFF gain control model)  44
  Model Results    47
1    Period doubling    47
2    Constant velocity moving bar    49
2.1    Berry Gain control model    49
2.2    Feedback and Feedforward model    51
2.3    FBFF gain control model    51
3    Constant velocity moving bar with fluctuation    54
3.1    Quantification of constant velocity bar with noise    54
3.2    Berry Gain control model    55
3.3    Feedback and Feedforward model    56
3.4    FBFF gain control model    56
4    LPOU moving bar    58
4.1    Berry Gain control model    58
4.2    Feedback and Feedforward model    59
4.3    MI between light intensity and firing rate    59
5    Effect of bar width    61
5.1    Square bar    61
5.2    Gaussian bar    63
  Discussion    65
1    Comparison between FBFF model and Berry Gain control model    65
1.1    Constant velocity moving bar    65
1.2    Constant velocity moving bar with fluctuation    65
1.3    LPOU moving bar    66
2    Biophysical mechanism    66
3    Why Berry gain control model can predict constant velocity moving bar?    . . .    68
4    Why Gain control doesn’t improve anticipation about LPOU moving bar?  .  . .    68
5    Why FBFF model can predict LPOU moving bar?    69
6    Which inhibition does the retina use?    69
7    Effect of contrast on prediction    69
  Conclusion    71
References    73
Appendix    75




                                

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