最近的計算神經科學研究已發表利用果蠅神經體的資料建立的全腦神經模型，
其研究展示了果蠅大腦的基本自發性活動。而我的研究進一步了解大腦的自發
性活動以及接受外接刺激之後的反應，在不同的神經突觸條件下的活動為何。
其中，我發現了在特定的邊界突觸條件範圍下，全腦模型可展示出穩定的自發
性活動，而在這條件範圍內的全腦模型也才可以傳遞外界的刺激訊號。若超出
此範圍，全腦模型的活動將過度活躍或者過度靜謐。此外訊號傳遞效率也會影
響在不同神經訊號層的神經活動多樣性，由於在過度活躍或者過度靜謐的大腦
狀態其訊號傳遞效率較差，因此使神經活動多樣性不會有顯著變化。最後我發
現了特定的刺激型神經群的回授連結網路顯著影響著全腦的活動狀態，若大腦
在過度活躍的狀態底下，抑制此神經群將會使大腦回復到正常狀態，反之若刺
激此群神經，將使大腦進入不穩定的過度活動狀態。因此我的研究工作主要展
示了一個全腦神經理論，了解了大腦如何傳遞外界訊號並改變大腦狀態、微型
神經網路如何影響全腦基本活動以及自發性活動如何影響訊號傳遞結果。

A recent study has released the first fruit fly brain connectomics data and proposed a
computational model of the fruit fly brain, which exhibited stable spontaneous activity.
However, the functionality of the brain, specifically its stability and ability to transmit
signals, was not thoroughly investigated. In the present study, I systematically
examined the detailed activities of the model and the parameter regimes that lead to a
stable brain. I further studied how neural signals can efficiently propagate in the
modeled brain network, which is a crucial property that indicates a functioning brain.
It is found that the parameters for efficient signal propagation lies in a narrow and
critical regime, in which the brain is neither too stable nor too active. A highly stable
brain tends to strongly attenuate any input signal while a hyper-active brain tends to
magnify the input signals in an uncontrollable fashion. Furthermore, within this
functional and efficient regime, the brain activities also appear to become more diverse.
In contrast, the diversity does not undergo significant changes under the hyperactive or
silent state. Finally, I discovered that the stability of the brain is reigned by a small set
of recurrent micro-circuits. In other words, a hyperactive brain can be brought into a
stable state simply by inhibiting these micro-circuits. In summary, this study provides
a theory of how a brain network can process sensory information efficiently and how
the stability of the brain is critically modulated by a specific set of micro-circuits.

ABSTRACT…………………………………………………….i
中文摘要….……………………………………………………ii
Chapter 1 Introduction…………………………………….……1
Chapter 2 Materials and Methods………………………………5
Chapter 3 Basic Behavior and Signal Propagation……………16
Chapter 4 Neurons Manipulation and Systemically Result…...32
Chapter 5 Discussion………………………………………….43
Reference…………………...…………………………………48

1. Abbott, L. F., Varela, J. A., Sen, K. & Nelson, S. B. Synaptic Depression and Cortical Gain Control. Science 275, 221–224 (1997).
2. Shew, W. L., Yang, H., Petermann, T., Roy, R. & Plenz, D. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J. Neurosci. 29, 15595–15600 (2009).
3. Shew, W. L., Yang, H., Yu, S., Roy, R. & Plenz, D. Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches. J. Neurosci. 31, 55–63 (2011).
4. Frye, R. E. et al. Neuropathological mechanisms of seizures in autism spectrum disorder. Front. Neurosci. 10, 192 (2016).
5. Fu, H. et al. Tau Pathology Induces Excitatory Neuron Loss, Grid Cell Dysfunction, and Spatial Memory Deficits Reminiscent of Early Alzheimer’s Disease. Neuron doi:10.1016/j.neuron.2016.12.023
6. Howlett, I. C., Rusan, Z. M., Parker, L. & Tanouye, M. A. Drosophila as a Model for Intractable Epilepsy: Gilgamesh Suppresses Seizures in parabss1 Heterozygote Flies. G3 3, 1399–1407 (2013).
7. Ford, K. J. & Davis, G. W. Archaerhodopsin Voltage Imaging: Synaptic Calcium and BK Channels Stabilize Action Potential Repolarization at the Drosophila Neuromuscular Junction. J. Neurosci. 34, 14517–14525 (2014).
8. George, A. L. Inherited Channelopathies Associated with Epilepsy. Epilepsy Curr 4, 65–70 (2004).
9. Hekmat-Scafe, D. S., Lundy, M. Y., Ranga, R. & Tanouye, M. A. Mutations in the K+/Cl− Cotransporter Gene kazachoc (kcc) Increase Seizure Susceptibility in Drosophila. J. Neurosci. 26, 8943–8954 (2006).
10. Koepp, M. J., Caciagli, L., Pressler, R. M., Lehnertz, K. & Beniczky, S. Reflex seizures, traits, and epilepsies: from physiology to pathology. The Lancet Neurology doi:10.1016/S1474-4422(15)00219-7
11. Lee, J. & Wu, C.-F. Genetic Modifications of Seizure Susceptibility and Expression by Altered Excitability in Drosophila Na+ and K+ Channel Mutants. Journal of Neurophysiology 96, 2465–2478 (2006).
12. Petruccelli, E., Lansdon, P. & Kitamoto, T. Exaggerated Nighttime Sleep and Defective Sleep Homeostasis in a Drosophila Knock-In Model of Human Epilepsy. PLOS ONE 10, e0137758 (2015).
13. Schutte, R. J. et al. Knock-in model of Dravet syndrome reveals a constitutive and conditional reduction in sodium current. Journal of Neurophysiology 112, 903–912 (2014).
14. Schutte, S. S., Schutte, R. J., Barragan, E. V. & O’Dowd, D. K. Model systems for studying cellular mechanisms of SCN1A-related epilepsy. Journal of Neurophysiology 115, 1755–1766 (2016).
15. Sivakumar, S. S., Namath, A. G., Tuxhorn, I. E., Lewis, S. J. & Galan, R. F. Decreased heart rate and enhanced sinus arrhythmia during interictal sleep demonstrate autonomic imbalance in generalized epilepsy. Journal of Neurophysiology jn.01120.2015 (2016). doi:10.1152/jn.01120.2015
16. Hekmat-Scafe, D. S. et al. Seizure Sensitivity Is Ameliorated by Targeted Expression of K+–Cl− Cotransporter Function in the Mushroom Body of the Drosophila Brain. Genetics 184, 171–183 (2010).
17. Vattikonda, A., Raju, S. B., Banerjee, A., Deco, G. & Roy, D. Does the regulation of local excitation–inhibition balance aid in recovery of functional connectivity? A computational account. NeuroImage doi:10.1016/j.neuroimage.2016.05.002
18. Azzi, A. et al. Network Dynamics Mediate Circadian Clock Plasticity. Neuron 93, 441–450 (2017).
19. Tatti, R., Haley, M. S., Swanson, O. K., Tselha, T. & Maffei, A. Neurophysiology and Regulation of the Balance Between Excitation and Inhibition in Neocortical Circuits. Biological Psychiatry doi:10.1016/j.biopsych.2016.09.017
20. Tejeda, H. A. et al. Pathway- and Cell-Specific Kappa-Opioid Receptor Modulation of Excitation-Inhibition Balance Differentially Gates D1 and D2 Accumbens Neuron Activity. Neuron 93, 147–163 (2017).
21. Fortune, E. S. & Rose, G. J. Short-Term Synaptic Plasticity Contributes to the Temporal Filtering of Electrosensory Information. J. Neurosci. 20, 7122–7130 (2000).
22. Mi, Y., Katkov, M. & Tsodyks, M. Synaptic Correlates of Working Memory Capacity. Neuron 93, 323–330 (2017).
23. Boudreau, C. E. & Ferster, D. Short-Term Depression in Thalamocortical Synapses of Cat Primary Visual Cortex. J. Neurosci. 25, 7179–7190 (2005).
24. Chung, S., Li, X. & Nelson, S. B. Short-Term Depression at Thalamocortical Synapses Contributes to Rapid Adaptation of Cortical Sensory Responses In Vivo. Neuron 34, 437–446 (2002).
25. Kandaswamy, U., Deng, P.-Y., Stevens, C. F. & Klyachko, V. A. The Role of Presynaptic Dynamics in Processing of Natural Spike Trains in Hippocampal Synapses. J. Neurosci. 30, 15904–15914 (2010).
26. Rotman, Z., Deng, P.-Y. & Klyachko, V. A. Short-Term Plasticity Optimizes Synaptic Information Transmission. J. Neurosci. 31, 14800–14809 (2011).
27. Klyachko, V. A. & Stevens, C. F. Excitatory and Feed-Forward Inhibitory Hippocampal Synapses Work Synergistically as an Adaptive Filter of Natural Spike Trains. PLoS Biol 4, e207 (2006).
28. Saraswati, S., Adolfsen, B. & Littleton, J. T. Characterization of the role of the Synaptotagmin family as calcium sensors in facilitation and asynchronous neurotransmitter release. PNAS 104, 14122–14127 (2007).
29. Goudar, V. & Buonomano, D. V. A model of order-selectivity based on dynamic changes in the balance of excitation and inhibition produced by short-term synaptic plasticity. Journal of Neurophysiology 113, 509–523 (2015).
30. Vogels, T. P. & Abbott, L. F. Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons. J. Neurosci. 25, 10786–10795 (2005).
31. Vogels, T. P. & Abbott, L. F. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat Neurosci 12, 483–491 (2009).
32. Vogels, T. P., Sprekeler, H., Zenke, F., Clopath, C. & Gerstner, W. Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks. Science 334, 1569–1573 (2011).
33. Chiang, A.-S. et al. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Curr Biol (2010). doi:10.1016/j.cub.2010.11.056
34. Kaiser, M. Neuroanatomy: Connectome Connects Fly and Mammalian Brain Networks. Current Biology 25, R416–R418 (2015).
35. Shih, C.-T. et al. Connectomics-Based Analysis of Information Flow in the Drosophila Brain. Current Biology 25, 1249–1258 (2015).
36. Wang, X.-J. & Kennedy, H. Brain structure and dynamics across scales: in search of rules. Current Opinion in Neurobiology 37, 92–98 (2016).
37. Streit, A. K., Fan, Y. N., Masullo, L. & Baines, R. A. Calcium Imaging of Neuronal Activity in Drosophila Can Identify Anticonvulsive Compounds. PLOS ONE 11, e0148461 (2016).
38. Cotterill, E., Charlesworth, P., Thomas, C. W., Paulsen, O. & Eglen, S. J. A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks. Journal of Neurophysiology jn.00093.2016 (2016). doi:10.1152/jn.00093.2016
39. Haimovici, A., Tagliazucchi, E., Balenzuela, P. & Chialvo, D. R. Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome. Phys. Rev. Lett. 110, 178101 (2013).
40. Klaus, A., Yu, S. & Plenz, D. Statistical Analyses Support Power Law Distributions Found in Neuronal Avalanches. PLoS ONE 6, e19779 (2011).
41. Millman, D., Mihalas, S., Kirkwood, A. & Niebur, E. Self-organized criticality occurs in non-conservative neuronal networks during /`up/’ states. Nat Phys advance online publication, (2010).
42. Priesemann, V. et al. Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front Syst Neurosci 8, (2014).
43. Rybarsch, M. & Bornholdt, S. Avalanches in Self-Organized Critical Neural Networks: A Minimal Model for the Neural SOC Universality Class. PLOS ONE 9, e93090 (2014).
44. Tanaka, T., Kaneko, T. & Aoyagi, T. Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity. Neural Comput 21, 1038–1067 (2009).
45. Beggs, J. M. & Timme, N. Being critical of criticality in the brain. Front. Physio. 3, 163 (2012).
46. Brette, R. Computing with Neural Synchrony. PLoS Comput Biol 8, e1002561 (2012).
47. Hu, H. & Agmon, A. Properties of precise firing synchrony between synaptically coupled cortical interneurons depend on their mode of coupling. Journal of Neurophysiology 114, 624–637 (2015).
48. Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K. & Carlén, M. Prefrontal Parvalbumin Neurons in Control of Attention. Cell 164, 208–218 (2016).
49. Margineanu, D. G. Epileptic hypersynchrony revisited. Neuroreport 21, 963–967 (2010).
50. Chen, C. et al. Drosophila Ionotropic Receptor 25a mediates circadian clock resetting by temperature. Nature 527, 516–520 (2015).
51. Mazzoni, A. et al. On the Dynamics of the Spontaneous Activity in Neuronal Networks. PLoS ONE 2, e439 (2007).
52. Hernandez-Urbina, V. & Herrmann, J. M. Neuronal avalanches in complex networks. Cogent Physics 1150408 (2016). doi:10.1080/23311940.2016.1150408
53. Haimovici, A., Balenzuela, P. & Tagliazucchi, E. Dynamical Signatures of Structural Connectivity Damage to a Model of the Brain Posed at Criticality. Brain Connectivity 6, 759–771 (2016).
54. Gray, R. T. & Robinson, P. A. Stability and spectra of randomly connected excitatory cortical networks. Neurocomputing 70, 1000–1012 (2007).
55. Benayoun, M., Cowan, J. D., van Drongelen, W. & Wallace, E. Avalanches in a Stochastic Model of Spiking Neurons. PLoS Comput Biol 6, e1000846 (2010).
56. Hobbs, J. P., Smith, J. L. & Beggs, J. M. Aberrant neuronal avalanches in cortical tissue removed from juvenile epilepsy patients. J Clin Neurophysiol 27, 380–386 (2010).
57. Jirsa, V. K., Stacey, W. C., Quilichini, P. P., Ivanov, A. I. & Bernard, C. On the nature of seizure dynamics. Brain 137, 2210–2230 (2014).
58. Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52, 1059–1069 (2010).
59. Lee, Y.-H., Lin, Y.-N., Chuang, C.-C. & Lo, C.-C. SPIN: A Method of Skeleton-Based Polarity Identification for Neurons. Neuroinform 12, 487–507 (2014).
60. Huang, Y.-C. et al. A Single-Cell Level and Connectome-Derived Computational Model of the Drosophila Brain. Front. Neuroinform. 12, (2019).
61. Donlea, J. M., Pimentel, D. & Miesenböck, G. Neuronal Machinery of Sleep Homeostasis in Drosophila. Neuron 81, 860–872 (2014).
62. Ruta, V. et al. A dimorphic pheromone circuit in Drosophila from sensory input to descending output. Nature 468, 686–690 (2010).
63. Farrow, K., Haag, J. & Borst, A. Nonlinear, binocular interactions underlying flow field selectivity of a motion-sensitive neuron. Nat Neurosci 9, 1312–1320 (2006).
64. Krotki, M. A. J. Lobula Plate Tangential Cells in Drosophila melanogaster; Response Properties, Synaptic Organization & Input Channels.
65. Wilson, R. I. & Laurent, G. Role of GABAergic Inhibition in Shaping Odor-Evoked Spatiotemporal Patterns in the Drosophila Antennal Lobe. J. Neurosci. 25, 9069–9079 (2005).
66. Wilson, R. I., Turner, G. C. & Laurent, G. Transformation of Olfactory Representations in the Drosophila Antennal Lobe. Science 303, 366–370 (2004).
67. Tuthill, J. C., Nern, A., Rubin, G. M. & Reiser, M. B. Wide-Field Feedback Neurons Dynamically Tune Early Visual Processing. Neuron 82, 887–895 (2014).
68. Sheeba, V., Gu, H., Sharma, V. K., O’Dowd, D. K. & Holmes, T. C. Circadian- and Light-Dependent Regulation of Resting Membrane Potential and Spontaneous Action Potential Firing of Drosophila Circadian Pacemaker Neurons. Journal of Neurophysiology 99, 976–988 (2008).
69. Gouwens, N. W. & Wilson, R. I. Signal Propagation in Drosophila Central Neurons. J. Neurosci. 29, 6239–6249 (2009).
70. Nagel, K. I., Hong, E. J. & Wilson, R. I. Synaptic and circuit mechanisms promoting broadband transmission of olfactory stimulus dynamics. Nat Neurosci advance online publication, (2014).
71. McCarthy, E. v. et al. Synchronized Bilateral Synaptic Inputs to Drosophila melanogaster Neuropeptidergic Rest/Arousal Neurons. J Neurosci 31, 8181–8193 (2011).
72. Gu, H. & O’Dowd, D. K. Cholinergic Synaptic Transmission in Adult Drosophila Kenyon Cells In Situ. J. Neurosci. 26, 265–272 (2006).
73. Turner, G. C., Bazhenov, M. & Laurent, G. Olfactory Representations by Drosophila Mushroom Body Neurons. Journal of Neurophysiology 99, 734–746 (2008).
74. Weir, P. T., Schnell, B. & Dickinson, M. H. Central complex neurons exhibit behaviorally gated responses to visual motion in Drosophila. J Neurophysiol 111, 62–71 (2014).
75. Hu, A., Zhang, W. & Wang, Z. Functional feedback from mushroom bodies to antennal lobes in the Drosophila olfactory pathway. PNAS 107, 10262–10267 (2010).
76. Liu, W. W. & Wilson, R. I. Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system. PNAS 110, 10294–10299 (2013).
77. Root, C. M., Semmelhack, J. L., Wong, A. M., Flores, J. & Wang, J. W. Propagation of olfactory information in Drosophila. Proceedings of the National Academy of Sciences 104, 11826–11831 (2007).
78. Mann, K., Gordon, M. D. & Scott, K. A Pair of Interneurons Influences the Choice between Feeding and Locomotion in Drosophila. Neuron 79, 754–765 (2013).
79. Cocatre-Zilgien, J. H. & Delcomyn, F. Identification of bursts in spike trains. Journal of Neuroscience Methods 41, 19–30 (1992).
80. Steinmetz, P. N., Wait, S. D., Lekovic, G. P., Rekate, H. L. & Kerrigan, J. F. Firing Behavior and Network Activity of Single Neurons in Human Epileptic Hypothalamic Hamartoma. Front Neurol 4, (2013).
81. Yada, Y., Kanzaki, R. & Takahashi, H. State-Dependent Propagation of Neuronal Sub-Population in Spontaneous Synchronized Bursts. Front. Syst. Neurosci 28 (2016). doi:10.3389/fnsys.2016.00028
82. Ewell, L. A. et al. Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention. J. Neurosci. 35, 15635–15648 (2015).
83. Ince, R. A. A., Petersen, R. S., Swan, D. C. & Panzeri, S. Python for Information Theoretic Analysis of Neural Data. Front Neuroinformatics 3, (2009).
84. Kreuz, T., Mulansky, M. & Bozanic, N. SPIKY: a graphical user interface for monitoring spike train synchrony. Journal of Neurophysiology 113, 3432–3445 (2015).
85. Paz, J. T. & Huguenard, J. R. Microcircuits and their interactions in epilepsy: is the focus out of focus? Nat Neurosci 18, 351–359 (2015).
86. Lin, Y.-N., Chang, P.-Y., Hsiao, P.-Y. & Lo, C.-C. Polarity-specific high-level information propagation in neural networks. Front. Neuroinform 8, 27 (2014).
87. Chen, J.-Y., Chauvette, S., Skorheim, S., Timofeev, I. & Bazhenov, M. Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation. The Journal of Physiology 590, 3987–4010 (2012).
88. Ostojic, S. Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nature Neuroscience 17, 594 (2014).
89. Ueltzhöffer, K., Armbruster-Genç, D. J. N. & Fiebach, C. J. Stochastic Dynamics Underlying Cognitive Stability and Flexibility. PLoS Comput. Biol. 11, e1004331 (2015).
90. Salmasi, M., Loebel, A., Glasauer, S. & Stemmler, M. Short-term synaptic depression can increase the rate of information transfer at a release site. PLOS Computational Biology 15, e1006666 (2019).
91. Beare, R. et al. Altered structural connectivity in ADHD: a network based analysis. Brain Imaging Behav 11, 846–858 (2017).
92. Lyoo, Y. & Yoon, S. Brain Network Correlates of Emotional Aging. Sci Rep 7, 15576 (2017).
93. Malaia, E., Bates, E., Seitzman, B. & Coppess, K. Altered brain network dynamics in youths with autism spectrum disorder. Exp Brain Res 234, 3425–3431 (2016).
94. Watanabe, T. & Rees, G. Brain network dynamics in high-functioning individuals with autism. Nat Commun 8, 16048 (2017).
95. Rosa, M. J. et al. Sparse network-based models for patient classification using fMRI. Neuroimage 105, 493–506 (2015).
96. Lane, B. J., Kick, D. R., Wilson, D. K., Nair, S. S. & Schulz, D. J. Dopamine maintains network synchrony via direct modulation of gap junctions in the crustacean cardiac ganglion. eLife 7, e39368 (2018).
97. Gao, W.-J. & Goldman-Rakic, P. S. Selective modulation of excitatory and inhibitory microcircuits by dopamine. PNAS 100, 2836–2841 (2003).
98. Carhart-Harris, R. L. et al. Neural correlates of the LSD experience revealed by multimodal neuroimaging. PNAS 113, 4853–4858 (2016).
99. Ognjanovski, N. et al. Parvalbumin-expressing interneurons coordinate hippocampal network dynamics required for memory consolidation. Nat Commun 8, 15039 (2017).
100. Liu, M., Amey, R. C. & Forbes, C. E. On the Role of Situational Stressors in the Disruption of Global Neural Network Stability during Problem Solving. J Cogn Neurosci 29, 2037–2053 (2017).
101. Touroutoglou, A., Andreano, J. M., Barrett, L. F. & Dickerson, B. C. Brain network connectivity-behavioral relationships exhibit trait-like properties: Evidence from hippocampal connectivity and memory. Hippocampus 25, 1591–1598 (2015).
102. Ren, S.-Q. et al. Amyloid β causes excitation/inhibition imbalance through dopamine receptor 1-dependent disruption of fast-spiking GABAergic input in anterior cingulate cortex. Scientific Reports 8, 302 (2018).