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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 嚴震東(Chen-Tung Yen) | |
dc.contributor.author | Kevin Sean Chen | en |
dc.contributor.author | 陳曦 | zh_TW |
dc.date.accessioned | 2021-05-13T06:41:43Z | - |
dc.date.available | 2017-07-20 | |
dc.date.available | 2021-05-13T06:41:43Z | - |
dc.date.copyright | 2017-07-20 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-06-12 | |
dc.identifier.citation | [1] Tom Baden, Philipp Berens, Katrin Franke, Miroslav Román Rosón, Matthias Bethge, and Thomas Euler. The functional diversity of retinal ganglion cells in the mouse. Nature, 529(7586).
[2] H. B. Barlow. Summation and inhibition in the frog’s retina. The Journal of Physiology, 119(1):69–88, January 1953. [3] M. J. Berry and Schwartz G. II. The retina as embodying predictions about the visual world. Predictions in the Brain: Using Our Past to Generate a Future, pages 295–310, 2011. [4] Michael J. Berry, Iman H. Brivanlou, Thomas A. Jordan, and Markus Meister. Anticipation of moving stimuli by the retina. Nature, 398(6725):334–338, 1999. [5] Guo-qiang Bi and Mu-ming Poo. Distributed synaptic modification in neural networks induced by patterned stimulation. Nature, 401(6755):792–796, Octo- ber 1999. [6] W. Bialek, F. Rieke, R. R. de Ruyter van Steveninck, and D. Warland. Reading a neural code. Science (New York, N.Y.), 252(5014):1854–1857, June 1991. [7] William Bialek. Thinking about the brain. arXiv:physics/0205030, May 2002. arXiv: physics/0205030. [8] William Bialek. Biophysics: Searching for Principles. Princeton University Press, December 2012. Google-Books-ID: MLYJ5Rz3GLwC. [9] William Bialek, Ilya Nemenman, and Naftali Tishby. Predictability, complexity, and learning. Neural computation, 13(11):2409–2463, 2001. [10] William Bialek and Naftali Tishby. Predictive Information. arXiv:cond- mat/9902341, February 1999. arXiv: cond-mat/9902341. [11] William Bialek, Rob R. de Ruyter van Steveninck, and Naftali Tishby. Effi- cient representation as a design principle for neural coding and computation. arXiv:0712.4381 [q-bio], December 2007. arXiv: 0712.4381. [12] Stewart A. Bloomfield and Béla Völgyi. The diverse functional roles and regu- lation of neuronal gap junctions in the retina. Nature Reviews. Neuroscience, 10(7):495–506, July 2009. [13] Daniel Bölinger and Tim Gollisch. Closed-Loop Measurements of Iso-Response Stimuli Reveal Dynamic Nonlinear Stimulus Integration in the Retina. Neuron, 73(2):333–346, January 2012. [14] Bart G. Borghuis and Anthony Leonardo. The Role of Motion Extrapolation in Amphibian Prey Capture. The Journal of Neuroscience, 35(46):15430–15441, November 2015. [15] Joanna Borowska, Stuart Trenholm, and Gautam B. Awatramani. An Intrinsic Neural Oscillator in the Degenerating Mouse Retina. Journal of Neuroscience, 31(13):5000–5012, March 2011. [16] Alexander Borst and Frédéric E. Theunissen. Information theory and neural coding. Nature Neuroscience, 2(11):947–957, November 1999. [17] Edward S. Boyden, Feng Zhang, Ernst Bamberg, Georg Nagel, and Karl Deis- seroth. Millisecond-timescale, genetically targeted optical control of neural ac- tivity. Nature Neuroscience, 8(9):1263–1268, September 2005. [18] Naama Brenner, William Bialek, and Rob de Ruyter Van Steveninck. Adaptive rescaling maximizes information transmission. Neuron, 26(3):695–702, 2000. [19] Catalin V. Buhusi and Warren H. Meck. What makes us tick? Functional and neural mechanisms of interval timing. Nature Reviews. Neuroscience, 6(10):755– 765, October 2005. [20] Theodore H. Bullock, Michael H. Hofmann, Frederick K. Nahm, John G. New, and James C. Prechtl. Event-related potentials in the retina and optic tectum of fish. Journal of Neurophysiology, 64(3):903–914, 1990. [21] J. Burrone and L. Lagnado. Electrical resonance and Ca2+ influx in the synap- tic terminal of depolarizing bipolar cells from the goldfish retina. The Journal of Physiology, 505 (3):571–584, December 1997. [22] Gal Chechik, Amir Globerson, Naftali Tishby, and Yair Weiss. Information bottleneck for Gaussian variables. Journal of Machine Learning Research, 6(Jan):165–188, 2005. [23] E. J. Chichilnisky. A simple white noise analysis of neuronal light responses. Network (Bristol, England), 12(2):199–213, May 2001. [24] Thomas M. Cover and Joy A. Thomas. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, 2006. [25] Felix Creutzig and Henning Sprekeler. Predictive coding and the slowness prin- ciple: an information-theoretic approach. Neural Computation, 20(4):1026– 1041, April 2008. [26] Peter Dayan and Laurence F. Abbott. Theoretical neuroscience, volume 806. Cambridge, MA: MIT Press, 2001. [27] D. M. Eagleman and T. J. Sejnowski. Motion integration and postdiction in visual awareness. Science (New York, N.Y.), 287(5460):2036–2038, March 2000. [28] Thomas Euler, Silke Haverkamp, Timm Schubert, and Tom Baden. Retinal bipolar cells: elementary building blocks of vision. Nature Reviews Neuro- science, 15(8):507–519, August 2014. [29] Adrienne L. Fairhall, Geoffrey D. Lewen, William Bialek, and Robert R. de Ruyter van Steveninck. Efficiency and ambiguity in an adaptive neural code. Nature, 412(6849):787–792, August 2001. [30] Jeffrey J. Fox, Eberhard Bodenschatz, and Robert F. Gilmour. Period- Doubling Instability and Memory in Cardiac Tissue. Physical Review Letters, 89(13):138101, September 2002. [31] Karl Friston. The free-energy principle: a unified brain theory? Nature Reviews Neuroscience, 11(2):127–138, 2010. [32] Elad Ganmor, Ronen Segev, and Elad Schneidman. A thesaurus for a neural population code. eLife, 4:e06134, September 2015. [33] Juan Gao, Greg Schwartz, Michael J. Berry, and Philip Holmes. An oscillatory circuit underlying the detection of disruptions in temporally-periodic patterns. Network: Computation in Neural Systems, 20(2):106–135, 2009. [34] Jeffrey P. Gavornik and Mark F. Bear. Learned spatiotemporal sequence recog- nition and prediction in primary visual cortex. Nature Neuroscience, 17(5):732– 737, May 2014. [35] Tim Gollisch and Markus Meister. Rapid neural coding in the retina with relative spike latencies. Science, 319(5866):1108–1111, 2008. [36] Tim Gollisch and Markus Meister. Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina. Neuron, 65(2):150–164, January 2010. [37] Moritz Helmstaedter, Kevin L. Briggman, Srinivas C. Turaga, Viren Jain, H. Se- bastian Seung, and Winfried Denk. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461):168–174, August 2013. [38] Toshihiko Hosoya, Stephen A. Baccus, and Markus Meister. Dynamic predictive coding by the retina. Nature, 436(7047):71–77, 2005. [39] H. Ishikane, A. Kawana, and M. Tachibana. Short- and long-range syn- chronous activities in dimming detectors of the frog retina. Visual Neuroscience, 16(6):1001–1014, December 1999. [40] Hiroshi Ishikane, Mie Gangi, Shoko Honda, and Masao Tachibana. Synchro- nized retinal oscillations encode essential information for escape behavior in frogs. Nature Neuroscience, 8(8):1087–1095, August 2005. [41] Eric Jonas and Konrad Paul Kording. Could a Neuroscientist Understand a Microprocessor? PLOS Computational Biology, 13(1):e1005268, January 2017. [42] Eric R. Kandel, James H. Schwartz, Thomas M. Jessell, Steven A. Siegelbaum, and A. J. Hudspeth, editors. Principles of Neural Science, Fifth Edition. McGraw-Hill Education / Medical, New York, 5th edition edition, October 2012. [43] David B. Kastner and Stephen A. Baccus. Coordinated dynamic encoding in the retina using opposing forms of plasticity. Nature Neuroscience, 14(10):1317– 1322, 2011. [44] David B. Kastner and Stephen A. Baccus. Insights from the retina into the di- verse and general computations of adaptation, detection, and prediction. Cur- rent Opinion in Neurobiology, 25:63–69, April 2014. [45] David B. Kastner, Stephen A. Baccus, and Tatyana O. Sharpee. Critical and maximally informative encoding between neural populations in the retina. Pro- ceedings of the National Academy of Sciences, 112(8):2533–2538, February 2015. [46] Jinseop S. Kim, Matthew J. Greene, Aleksandar Zlateski, Kisuk Lee, Mark Richardson, Srinivas C. Turaga, Michael Purcaro, Matthew Balkam, Amy Robinson, Bardia F. Behabadi, Michael Campos, Winfried Denk, H. Sebastian Seung, and The EyeWirers. Space-time wiring specificity supports direction selectivity in the retina. Nature, 509(7500):331–336, May 2014. [47] Anthony Leonardo and Markus Meister. Nonlinear dynamics support a linear population code in a retinal target-tracking circuit. The Journal of Neuro- science, 33(43):16971–16982, 2013. [48] J. Y. Lettvin, H. R. Maturana, W. S. McCulloch, and W. H. Pitts. What the Frog’s Eye Tells the Frog’s Brain. Proceedings of the IRE, 47(11):1940–1951, November 1959. [49] Wen-Zhong Liu, Wei Jing, Hao Li, Hai-Qing Gong, and Pei-Ji Liang. Spatial and temporal correlations of spike trains in frog retinal ganglion cells. Journal of Computational Neuroscience, 30(3):543–553, June 2011. [50] John Makhoul. Linear prediction: A tutorial review. Proceedings of the IEEE, 63(4):561–580, 1975. [51] Richard H. Masland. The Neuronal Organization of the Retina. Neuron, 76(2):266–280, October 2012. [52] Humberto R. Maturana, J. Y. Lettvin, W. S. McCulloch, and W. H. Pitts. Anatomy and Physiology of Vision in the Frog (Rana pipiens). The Journal of General Physiology, 43(6):129–175, July 1960. [53] J. Jason McAnany and Kenneth R. Alexander. Is there an omitted stimulus response in the human cone flicker electroretinogram? Visual Neuroscience, 26(02):189–194, 2009. [54] M. Meister and M. J. Berry. The neural code of the retina. Neuron, 22(3):435– 450, March 1999. [55] M. Meister, J. Pine, and D. A. Baylor. Multi-neuronal signals from the retina: acquisition and analysis. Journal of Neuroscience Methods, 51(1):95–106, Jan- uary 1994. [56] Yuanyuan Mi, Xuhong Liao, Xuhui Huang, Lisheng Zhang, Weifeng Gu, Gang Hu, and Si Wu. Long-period rhythmic synchronous firing in a scale-free net- work. Proceedings of the National Academy of Sciences, 110(50):E4931–E4936, December 2013. [57] Yuanyuan Mi, Xiaohan Lin, and Si Wu. Neural Computations in a Dynamical System with Multiple Time Scales. Frontiers in Computational Neuroscience, 10, 2016. [58] Gianluigi Mongillo, Omri Barak, and Misha Tsodyks. Synaptic theory of work- ing memory. Science, 319(5869):1543–1546, 2008. [59] Risto Näätänen, Petri Paavilainen, Teemu Rinne, and Kimmo Alho. The mis- match negativity (MMN) in basic research of central auditory processing: a review. Clinical Neurophysiology, 118(12):2544–2590, 2007. [60] Amurta Nath and Gregory W. Schwartz. Cardinal Orientation Selectivity Is Represented by Two Distinct Ganglion Cell Types in Mouse Retina. The Jour- nal of Neuroscience, 36(11):3208–3221, March 2016. [61] Ilya Nemenman, Geoffrey D. Lewen, William Bialek, and Rob R. de Ruyter van Steveninck. Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution. PLOS Computational Biology, 4(3):e1000025, March 2008. [62] Bence P. Oˇlveczky, Stephen A. Baccus, and Markus Meister. Segregation of object and background motion in the retina. Nature, 423(6938):401–408, 2003. [63] Tohru Ozaki. Time Series Modeling of Neuroscience Data. CRC Press, January 2012. Google-Books-ID: wZPLBQAAQBAJ. [64] Yusuf Ozuysal and Stephen A. Baccus. Linking the computational structure of variance adaptation to biophysical mechanisms. Neuron, 73(5):1002–1015, March 2012. [65] Stephanie E. Palmer, Olivier Marre, Michael J. Berry, and William Bialek. Predictive information in a sensory population. Proceedings of the National Academy of Sciences, 112(22):6908–6913, June 2015. [66] Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, and Eero P. Simoncelli. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207):995– 999, August 2008. [67] Jason S. Prentice, Olivier Marre, Mark L. Ioffe, Adrianna R. Loback, GaÅ¡per TkaÄik, and Michael J. Berry Ii. Error-Robust Modes of the Retinal Population Code. PLOS Computational Biology, 12(11):e1005148, November 2016. [68] Rajesh PN Rao and Dana H. Ballard. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1):79–87, 1999. [69] Esteban Real, Hiroki Asari, Tim Gollisch, and Markus Meister. Neural Circuit Inference from Function to Structure. Current Biology, 27(2):189–198, January 2017. [70] Fred Rieke, Davd Warland, Rob de Ruyter van Steveninck, and William Bialek. Spikes: Exploring the Neural Code. MIT Press, Cambridge, MA, USA, 1999. [71] Jonathan Rubin, Nachum Ulanovsky, Israel Nelken, and Naftali Tishby. The Representation of Prediction Error in Auditory Cortex. PLoS Comput Biol, 12(8):e1005058, 2016. [72] Tetsu Saigusa, Atsushi Tero, Toshiyuki Nakagaki, and Yoshiki Kuramoto. Amoebae anticipate periodic events. Physical Review Letters, 100(1):018101, 2008. [73] Jared M. Salisbury and Stephanie E. Palmer. Optimal Prediction in the Retina and Natural Motion Statistics. Journal of Statistical Physics, 162(5):1309–1323, 2016. [74] Elad Schneidman, Michael J. Berry, Ronen Segev, and William Bialek. Weak pairwise correlations imply strongly correlated network states in a neural pop- ulation. Nature, 440(7087):1007–1012, April 2006. [75] Elad Schneidman, William Bialek, and Michael J. Berry. Synergy, Redundancy, and Independence in Population Codes. Journal of Neuroscience, 23(37):11539– 11553, December 2003. [76] Elad Schneidman, Jason L. Puchalla, Ronen Segev, Robert A. Harris, William Bialek, and Michael J. Berry. Synergy from Silence in a Combinatorial Neural Code. Journal of Neuroscience, 31(44):15732–15741, November 2011. [77] Greg Schwartz and Michael J. Berry. Sophisticated temporal pattern recog- nition in retinal ganglion cells. Journal of Neurophysiology, 99(4):1787–1798, 2008. [78] Greg Schwartz, Rob Harris, David Shrom, and Michael J. Berry. Detection and prediction of periodic patterns by the retina. Nature Neuroscience, 10(5):552– 554, May 2007. [79] Greg Schwartz, Sam Taylor, Clark Fisher, Rob Harris, and Michael J. Berry. Synchronized firing among retinal ganglion cells signals motion reversal. Neu- ron, 55(6):958–969, 2007. [80] Gregory W. Schwartz and Fred Rieke. Controlling gain one photon at a time. eLife, 2:e00467, May 2013. [81] Gregory William Schwartz. Computation and coding in the retina. PhD thesis, PRINCETON UNIVERSITY, 2008. [82] Ronen Segev, Joe Goodhouse, Jason Puchalla, and Michael J. Berry. Recording spikes from a large fraction of the ganglion cells in a retinal patch. Nature Neuroscience, 7(10):1155–1162, October 2004. [83] C. E. Shannon. A Mathematical Theory of Communication. Bell System Tech- nical Journal, 27(3):379–423, July 1948. [84] Mandyam V. Srinivasan, Simon B. Laughlin, and Andreas Dubs. Predictive coding: a fresh view of inhibition in the retina. Proceedings of the Royal Society of London B: Biological Sciences, 216(1205):427–459, 1982. [85] R. V. Stirling and E. G. Merrill. Functional morphology of frog retinal ganglion cells and their central projections: The dimming detectors. The Journal of Comparative Neurology, 258(4):477–495, April 1987. [86] Steven H. Strogatz. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Westview Press, July 2014. Google-Books-ID: aMrSDQAAQBAJ. [87] S. P. Strong, Roland Koberle, Rob R. de Ruyter van Steveninck, and William Bialek. Entropy and Information in Neural Spike Trains. Physical Review Letters, 80(1):197–200, January 1998. [88] Germán Sumbre, Akira Muto, Herwig Baier, and Mu-ming Poo. Entrained rhythmic activities of neuronal ensembles as perceptual memory of time inter- val. Nature, 456(7218):102–106, 2008. [89] Gašper Tkačik, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie E. Palmer, Michael J. Berry, and William Bialek. Thermodynamics and signatures of criticality in a network of neurons. Proceedings of the National Academy of Sciences, 112(37):11508–11513, September 2015. [90] Misha V. Tsodyks and Henry Markram. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences, 94(2):719–723, 1997. [91] George E. Uhlenbeck and Leonard S. Ornstein. On the theory of the Brownian motion. Physical Review, 36(5):823, 1930. [92] Catherine Wacongne, Jean-Pierre Changeux, and Stanislas Dehaene. A neuronal model of predictive coding accounting for the mismatch negativity. The Journal of Neuroscience, 32(11):3665–3678, 2012. [93] J. Andrew Wasserstrom, Yohannes Shiferaw, Wei Chen, Satvik Ramakrishna, Heetabh Patel, James E. Kelly, Matthew J. O’Toole, Amanda Pappas, Nimi Chirayil, Nikhil Bassi, Lisa Akintilo, Megan Wu, Rishi Arora, and Gary L. Aistrup. Variability in timing of spontaneous calcium release in the intact rat heart is determined by the time course of sarcoplasmic reticulum calcium load. Circulation Research, 107(9):1117–1126, October 2010. [94] Andrew Webb, Sergio Davies, and David Lester. Spiking Neural PID Con- trollers. In Neural Information Processing, pages 259–267. Springer, Berlin, Heidelberg, November 2011. [95] Birgit Werner, Paul B. Cook, and Christopher L. Passaglia. Complex temporal response patterns with a simple retinal circuit. Journal of Neurophysiology, 100(2):1087–1097, 2008. [96] Michael Wibral, Joseph Lizier, Sebastian Vogler, Viola Priesemann, and Ralf Galuske. Local active information storage as a tool to understand distributed neural information processing. Frontiers in Neuroinformatics, 8, 2014. [97] Lei Xiao, Mingsha Zhang, Dajun Xing, Pei-Ji Liang, and Si Wu. Shifted en- coding strategy in retinal luminance adaptation: from firing rate to neural correlation. Journal of Neurophysiology, 110(8):1793–1803, October 2013. [98] Ying-Jen Yang, Chun-Chung Chen, Pik-Yin Lai, and C. K. Chan. Adap- tive synchronization and anticipatory dynamical systems. Physical Review E, 92(3):030701, 2015. [99] Pu-Ming Zhang, Jin-Yong Wu, Yi Zhou, Pei-Ji Liang, and Jing-Qi Yuan. Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem. Journal of Neuroscience Methods, 135(1):55–65, May 2004. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2540 | - |
dc.description.abstract | 編碼隨時間變化的外界刺激並產生預期性的反應是神經系統重要的特性。視網 膜這樣的初級視覺系統,便能在一串週期性刺激停止後產生遺缺刺激反應,此反 應同步化、產生於準確的時間點、且能預測刺激的動態。然而,遺缺刺激反應的 生物物理機制以及在非周期性刺激下的預測行為仍不清楚。此研究中,我們重複 視網膜中遺缺刺激反應的實驗,並將實驗結果與一種預期性動力學的模型比較, 最後以信息理論推廣在複雜動態刺激下的預測行為。我們以多通道電極陣列量測 牛蛙視網膜在不同動態光刺激下的反應,顯示視網膜在刺激週期為100-250毫秒時 能產生具有預測性的遺缺刺激反應。藉由週期性刺激後給予的探測刺激,發現視 網膜產生遺缺性刺激的時間尺度長達3秒,此時間尺度的適應行為可能與突觸的 鈣離子動態有關。而在包含多重週期的刺激停止後,所引起的反應相對去同步化 且有高歧異度。以隨機過程刺激視網膜,我們計算隨機光刺激週期與視網膜反應 在不同時間點的互信息、量化預期信息並推廣視網膜在連續刺激下的預測行為。 視網膜的預期行為與刺激的統計性質有關、且能偵測隨機刺激中的隱藏變量以進 行預測,而這些時間序列的統計特性能對應暫態反應(即原先的遺缺刺激反應實 驗)中所量測的時間尺度。透過數值方法模擬,我們進一步探討視網膜預測時間 序列的行為與可能機制。 | zh_TW |
dc.description.abstract | Encoding time-dependent inputs and generating predictive activities are fun- damental properties in the nervous systems. Omitted stimulus response (OSR), a synchronized activity preserved after a periodic entrainment terminates, has been observed in primary visual systems such as the retina. OSR sensitively detects change and precisely predicts the upcoming stimulus patterns. However, the un- derlying biophysical mechanisms for OSR and responses to more general temporal patterns are still unknown. In this study, we repeated experiments for OSR, com- pared the behavior with an adaptive model that simulates OSR, and investigated predictive performance under stochastic stimuli. Experiments were operated with multiple electrode array recording for the bullfrog retina under programmed light stimuli. We show that OSR occurs in a dynamic range, when the period of stimuli is 100-250 ms. A probe provided after the periodic entrainment reveals the time scale of adaptation, showing preserved tendency to produce OSR after time delay up to 3 seconds, which might relate to the time scale of synaptic calcium dynamics. Under complex temporal patterns with multiple periods, the post-stimulus response is less synchronized and heterogeneous. By calculating the mutual information be- tween the input stochastic intervals and the retinal activity at different time shifts, we could quantify predictive information and characterize the predictive behaviors under stationary responses. It is shown that the predictive behavior depends on the statistics of the stimuli and the retina could detect the hidden variable in the stochastic process to make prediction. The time scales identified in the transient OSR phenomenon are observed in the predictive behavior under stationary responses and numerical methods were applied to implement possible mechanisms for temporal prediction. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T06:41:43Z (GMT). No. of bitstreams: 1 ntu-106-R04b21003-1.pdf: 20544913 bytes, checksum: 3d165878f1dda2a28b87ce177e4803ac (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | Certification i
Acknowledgment iii Chinese Abstract v Abstract vi Contents vii List of Figures xi 1 Introduction 1 1.1 Computation and Coding in the Retina................. 1 1.2 Omitted Stimulus Response ....................... 4 1.3 Anticipatory Response with Adaptive Dynamics . . . . . . . . . . . . 6 1.4 Prediction and Predictive Coding .................... 9 1.5 Predictive Information .......................... 10 1.6 Organization of the Thesis........................ 14 2 Materials and Methods 16 2.1 Sample Preparations ........................... 16 2.2 Experimental Setup............................ 17 2.3 Signal Analysis .............................. 20 2.3.1 Data processing and validity check . . . . . . . . . . . . . . . 20 2.3.2 Information theory ........................ 23 2.3.3 Finite data correction and parameter choice . . . . . . . . . . 24 2.4 Stimulation Design ............................ 26 2.4.1 Periodic stimuli.......................... 27 2.4.2 Stochastic intervals........................ 27 2.5 Numerical Modeling ........................... 30 2.5.1 Adaptive FitzHugh-Nagumo model ............... 31 2.5.2 Linear-nonlinear model...................... 32 2.5.3 “Gedanken”retina......................... 33 3 Results 37 3.1 Omitted Stimulus Response ....................... 37 3.1.1 OSR is predictive......................... 37 3.1.2 OSR is synchronized in a population . . . . . . . . . . . . . . 40 3.1.3 OSR is affected by bright-dark pulses . . . . . . . . . . . . . . 42 3.1.4 OSR is sensitive to the last pulse ................ 43 3.2 Adaptive Synchronization in the Retina. . . . . . . . . . . . . . . . . 44 3.2.1 An adaptive oscillator produces OSR . . . . . . . . . . . . . . 45 3.2.2 Probing the adaptive variable .................. 48 3.2.3 Calcium concentration perturbs OSR . . . . . . . . . . . . . . 51 3.3 Discriminating Complex Temporal Patterns . . . . . . . . . . . . . . 53 3.3.1 Heterogeneous response to multiple periods . . . . . . . . . . . 53 3.3.2 Effects of spatial patterns .................... 54 3.4 Characterization of Predictive Behavior by Mutual Information . . . 58 3.4.1 Encoding stimuli with firing rate ................ 58 3.4.2 Characterizing predictive power ................. 60 3.5 Predictive Information Under Stochastic Temporal Patterns . . . . . 61 3.5.1 Prediction depends on the statistics of stimuli . . . . . . . . . 63 3.5.2 Response latency shifts according to the predictability . . . . 64 3.5.3 Effects of higher-order statistics ................. 66 3.5.4 Predictive information relates with OSR . . . . . . . . . . . . 68 3.6 Simple Models for Prediction ...................... 69 3.6.1 AFHN............................... 71 3.6.2 Linear extrapolation ....................... 72 3.6.3 “Gedanken” retina......................... 74 4 Discussion 77 4.1 Behaviors and Possible Mechanisms for OSR . . . . . . . . . . . . . . 77 4.1.1 Comparison with previous studies................ 77 4.1.2 Possible biophysical mechanisms for OSR. . . . . . . . . . . . 79 4.2 Coding Temporal Patterns in Biological Systems . . . . . . . . . . . . 81 4.2.1 Possible functional explanation for OSR . . . . . . . . . . . . 81 4.2.2 Timeperception ......................... 83 4.3 Recapitulating Predictive Behaviors................... 84 4.3.1 How to define predictive behavior................ 84 4.3.2 Other methods to measure prediction . . . . . . . . . . . . . . 87 4.4 Theories for Neural Coding ....................... 89 4.5 Conclusions and Future Works...................... 92 References 94 A Preliminary Results 105 A.1 Pharmacological Tests ..........................105 A.1.1 AP5 ................................105 A.1.2 Bicuculline.............................105 B Additional Analysis 107 B.1 Other coding strategies..........................107 B.2 More on stochastic processes.......................107 B.3 Calibration in information measurements . . . . . . . . . . . . . . . .111 C Supplementary Materials 114 C.1 Codes and Parameters ..........................114 C.2 Setup and Protocol............................117 | |
dc.language.iso | en | |
dc.title | 視網膜中的預測行為與編碼 | zh_TW |
dc.title | Prediction and Coding for Temporal Patterns in the Retina | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳志強(C.K. Chan) | |
dc.contributor.oralexamcommittee | 陳俊仲(Chun-Chung Chen),焦傳金(Chuan-Chin Chiao) | |
dc.subject.keyword | 視網膜,遺缺刺激反應,預期性動力學,互信息,預期信息,隨機過程, | zh_TW |
dc.subject.keyword | Retina,Omitted Stimulus Response,Anticipative dynamics,Mutual information,Predictive information,Stochastic process, | en |
dc.relation.page | 119 | |
dc.identifier.doi | 10.6342/NTU201700926 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2017-06-12 | |
dc.contributor.author-college | 生命科學院 | zh_TW |
dc.contributor.author-dept | 生命科學系 | zh_TW |
顯示於系所單位: | 生命科學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-106-1.pdf | 20.06 MB | Adobe PDF | 檢視/開啟 |
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