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Biological Cybernetics

, Volume 113, Issue 5–6, pp 465–474 | Cite as

The case for emulating insect brains using anatomical “wiring diagrams” equipped with biophysical models of neuronal activity

  • Logan T. CollinsEmail author
Prospects

Abstract

Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress toward this goal might be accelerated by working toward an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make “virtual insects.” I argue that this could be realistically achievable within the next 20 years. I propose that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain. By demonstrating that WBE is possible via IBE, simulating mammalian brains and eventually the human brain may no longer be viewed as too radically ambitious to deserve substantial funding and resources. Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience, artificial intelligence, and robotics through studies performed using virtual insects.

Keywords

Connectomics Hodgkin–Huxley models Insects Whole-brain emulation 

Notes

Acknowledgements

The work was supported by the Arnold and Mabel Beckman Foundation under the Beckman Scholars Program. I thank Michael P. Saddoris for his constructive feedback on the manuscript.

Compliance with ethical standards

Conflict of interest

The author declares no competing financial interests.

References

  1. Acciai L, Soda P, Iannello G (2016) Automated neuron tracing methods: an updated account. Neuroinformatics 14(4):353–367.  https://doi.org/10.1007/s12021-016-9310-0 CrossRefPubMedGoogle Scholar
  2. Akata Z, Perronnin F, Harchaoui Z, Schmid C (2014) Good practice in large-scale learning for image classification. IEEE Trans Pattern Anal Mach Intell 36(3):507–520.  https://doi.org/10.1109/TPAMI.2013.146 CrossRefPubMedGoogle Scholar
  3. Alivisatos AP, Chun M, Church GM, Greenspan RJ, Roukes ML, Yuste R (2012) The brain activity map project and the challenge of functional connectomics. Neuron 74(6):970–974.  https://doi.org/10.1016/j.neuron.2012.06.006 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Ardin P, Peng F, Mangan M, Lagogiannis K, Webb B (2016) Using an insect mushroom body circuit to encode route memory in complex natural environments. PLoS Comput Biol 12(2):e1004683.  https://doi.org/10.1371/journal.pcbi.1004683 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bartol TM Jr, Bromer C, Kinney J, Chirillo MA, Bourne JN, Harris KM, Sejnowski TJ (2015) Nanoconnectomic upper bound on the variability of synaptic plasticity. ELife 4:e10778.  https://doi.org/10.7554/eLife.10778 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Bloch G, Hazan E, Rafaeli A (2013) Circadian rhythms and endocrine functions in adult insects. J Insect Physiol 59(1):56–69.  https://doi.org/10.1016/j.jinsphys.2012.10.012 CrossRefPubMedGoogle Scholar
  7. Borst A (2007) Correlation versus gradient type motion detectors: the pros and cons. Philos Trans R Soc B Biol Sci 362(1479):369–374CrossRefGoogle Scholar
  8. Brown AD, Chad JE, Kamarudin R, Dugan KJ, Furber SB (2018) SpiNNaker: event-based simulation—quantitative behavior. IEEE Trans Multi-Scale Comput Syst 4(3):450–462.  https://doi.org/10.1109/TMSCS.2017.2748122 CrossRefGoogle Scholar
  9. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186.  https://doi.org/10.1038/nrn2575 CrossRefPubMedGoogle Scholar
  10. Bürgers J, Pavlova I, Rodriguez-Gatica JE, Henneberger C, Oeller M, Ruland JA, Siebrasse JP, Kubitscheck U, Schwarz MK (2019) Light-sheet fluorescence expansion microscopy: fast mapping of neural circuits at super resolution. Neurophotonics 6(1):1–12.  https://doi.org/10.1117/1.NPh.6.1.015005 CrossRefGoogle Scholar
  11. Chakraborty R, Vepuri V, Mhatre SD, Paddock BE, Miller S, Michelson SJ, Delvadia R, Desai A, Vinokur M, Marenda DR (2011) Characterization of a Drosophila Alzheimer’s disease model: pharmacological rescue of cognitive defects. PLoS ONE 6(6):e20799.  https://doi.org/10.1371/journal.pone.0020799 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Chen F, Tillberg PW, Boyden ES (2015) Expansion microscopy. Science 347(6221):543–548.  https://doi.org/10.1126/science.1260088 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Chen Y, Wang H, Helbling EF, Jafferis NT, Zufferey R, Ong A, Ma K, Gravish N, Chirarattananon P, Kovac M (2017) A biologically inspired, flapping-wing, hybrid aerial-aquatic microrobot. Sci Robot 2(11):eaao5619CrossRefGoogle Scholar
  14. Clemens J, Hennig RM (2013) Computational principles underlying the recognition of acoustic signals in insects. J Comput Neurosci 35(1):75–85.  https://doi.org/10.1007/s10827-013-0441-0 CrossRefPubMedGoogle Scholar
  15. Clemens J, Wohlgemuth S, Ronacher B (2012) Nonlinear computations underlying temporal and population sparseness in the auditory system of the grasshopper. J Neurosci 32(29):10053–10062CrossRefGoogle Scholar
  16. Denk W, Briggman KL, Helmstaedter M (2012) Structural neurobiology: missing link to a mechanistic understanding of neural computation. Nat Rev Neurosci 13:351.  https://doi.org/10.1038/nrn3169 CrossRefPubMedGoogle Scholar
  17. Destexhe A, Contreras D, Steriade M, Sejnowski TJ, Huguenard JR (1996) In vivo, in vitro, and computational analysis of dendritic calcium currents in thalamic reticular neurons. J Neurosci 16(1):169–185CrossRefGoogle Scholar
  18. Donohue DE, Ascoli GA (2011) Automated reconstruction of neuronal morphology: an overview. Brain Res Rev 67(1):94–102.  https://doi.org/10.1016/j.brainresrev.2010.11.003 CrossRefPubMedGoogle Scholar
  19. Eichler K, Li F, Litwin-Kumar A, Park Y, Andrade I, Schneider-Mizell CM, Saumweber T, Huser A, Eschbach C, Gerber B, Fetter RD, Cardona A (2017) The complete connectome of a learning and memory centre in an insect brain. Nature 548:175.  https://doi.org/10.1038/nature23455 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Even N, Devaud J-M, Barron AB (2012) General stress responses in the honey bee. Insects 3(4):1271–1298.  https://doi.org/10.3390/insects3041271 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Farooqui T (2012) Review of octopamine in insect nervous systems. Open Access Insect Physiol 4:1–17CrossRefGoogle Scholar
  22. Fonseca MDC, Araujo BHS, Dias CSB, Archilha NL, Neto DPA, Cavalheiro E, Westfahl H, da Silva AJR, Franchini KG (2018) High-resolution synchrotron-based X-ray microtomography as a tool to unveil the three-dimensional neuronal architecture of the brain. Sci Rep 8(1):12074.  https://doi.org/10.1038/s41598-018-30501-x CrossRefPubMedPubMedCentralGoogle Scholar
  23. Franconville R, Beron C, Jayaraman V (2018) Building a functional connectome of the Drosophila central complex. ELife 7:e37017.  https://doi.org/10.7554/eLife.37017 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Furber S, Temple S, Brown A (2006) High-performance computing for systems of spiking neurons. In: Proceedings of AISB’06: adaptation in artificial and biological systems. Bristol, United Kingdom, pp 29–36Google Scholar
  25. Gao R, Asano SM, Upadhyayula S, Pisarev I, Milkie DE, Liu T-L, Singh V, Graves A, Huynh GH, Zhao Y, Betzig E (2019) Cortical column and whole-brain imaging with molecular contrast and nanoscale resolution. Science 363(6424):eaau8302.  https://doi.org/10.1126/science.aau8302 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gerstner W, Kistler WM, Naud R, Paninski L (2014) Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press, New YorkCrossRefGoogle Scholar
  27. Givon LE, Lazar AA (2016) Neurokernel: An open source platform for emulating the fruit fly brain. PLoS ONE 11(1):e0146581.  https://doi.org/10.1371/journal.pone.0146581 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Givon LE, Lazar AA, Yeh C-H (2017) Generating executable models of the Drosophila central complex. Front Behav Neurosci.  https://doi.org/10.3389/fnbeh.2017.00102 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Gorostiza EA (2018) Does cognition have a role in plasticity of “innate behavior”? Front Psychol.  https://doi.org/10.3389/fpsyg.2018.01502 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Günay C, Sieling FH, Dharmar L, Lin W-H, Wolfram V, Marley R, Baines RA, Prinz AA (2015) Distal spike initiation zone location estimation by morphological simulation of ionic current filtering demonstrated in a novel model of an identified Drosophila motoneuron. PLoS Comput Biol 11(5):e1004189.  https://doi.org/10.1371/journal.pcbi.1004189 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Handschuh S, Beisser CJ, Ruthensteiner B, Metscher BD (2017) Microscopic dual-energy CT (microDECT): a flexible tool for multichannel ex vivo 3D imaging of biological specimens. J Microsc 267(1):3–26.  https://doi.org/10.1111/jmi.12543 CrossRefPubMedGoogle Scholar
  32. Hauser F, Cazzamali G, Williamson M, Blenau W, Grimmelikhuijzen CJP (2006) A review of neurohormone GPCRs present in the fruitfly Drosophila melanogaster and the honey bee Apis mellifera. Prog Neurobiol 80(1):1–19.  https://doi.org/10.1016/j.pneurobio.2006.07.005 CrossRefPubMedGoogle Scholar
  33. Helmstaedter M, Briggman KL, Turaga SC, Jain V, Seung HS, Denk W (2013) Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500:168.  https://doi.org/10.1038/nature12346 CrossRefPubMedGoogle Scholar
  34. Herz AVM, Gollisch T, Machens CK, Jaeger D (2006) Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science 314(5796):80–85CrossRefGoogle Scholar
  35. Hines J (2018) Stepping up to summit. Comput Sci Eng 20(2):78–82.  https://doi.org/10.1109/MCSE.2018.021651341 CrossRefGoogle Scholar
  36. Holcman D, Yuste R (2015) The new nanophysiology: regulation of ionic flow in neuronal subcompartments. Nat Rev Neurosci 16:685.  https://doi.org/10.1038/nrn4022 CrossRefPubMedGoogle Scholar
  37. Huang Y-C, Wang C-T, Su T-S, Kao K-W, Lin Y-J, Chuang C-C, Chiang AS, Lo C-C (2019) A single-cell level and connectome-derived computational model of the Drosophila brain. Front Neuroinform.  https://doi.org/10.3389/fninf.2018.00099 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Indiveri G, Linares-Barranco B, Hamilton T, van Schaik A, Etienne-Cummings R, Delbruck T, Liu SC, Dudek P, Häfliger P, Renaud S, Schemmel J, Boahen K (2011) Neuromorphic silicon neuron circuits. Front Neurosci.  https://doi.org/10.3389/fnins.2011.00073 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Izhikevich EM (2004) Which model to use for cortical spiking neurons? IEEE Trans Neural Netw 15(5):1063–1070.  https://doi.org/10.1109/TNN.2004.832719 CrossRefPubMedGoogle Scholar
  40. Jimenez LO, Landgrebe DA (1998) Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans Syst Man Cybern Part C (Appl Rev) 28(1):39–54.  https://doi.org/10.1109/5326.661089 CrossRefGoogle Scholar
  41. Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S (2018) Extremely Scalable spiking neuronal network simulation code: from laptops to exascale computers. Front Neuroinform.  https://doi.org/10.3389/fninf.2018.00002 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Kakaria KS, de Bivort BL (2017) Ring attractor dynamics emerge from a spiking model of the entire protocerebral bridge. Front Behav Neurosci.  https://doi.org/10.3389/fnbeh.2017.00008 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Kleinfeld D, Bharioke A, Blinder P, Bock DD, Briggman KL, Chklovskii DB, Denk W, Helmstaedter M, Kaufhold JP, Lee WCA, Sakmann B (2011) Large-scale automated histology in the pursuit of connectomes. J Neurosci 31(45):16125–16138CrossRefGoogle Scholar
  44. Koene RA (2012) Fundamentals of whole brain emulation: state, transition, and update representations. Int J Mach Conscious 4(1):5–21.  https://doi.org/10.1142/S179384301240001X CrossRefGoogle Scholar
  45. Koene RA (2013) Uploading to substrate-independent minds. In: Chella A (ed) The transhumanist reader: classical and contemporary essays on the science, technology, and philosophy of the human future. World Scientific, California, USA, pp 146–156CrossRefGoogle Scholar
  46. Lambrecht BGA, Horchler AD, Quinn RD (2005) A small, insect-inspired robot that runs and jumps. In: Proceedings of the 2005 IEEE international conference on robotics and automation. pp 1240–1245.  https://doi.org/10.1109/ROBOT.2005.1570285
  47. Le Moël F, Stone T, Lihoreau M, Wystrach A, Webb B (2019) The central complex as a potential substrate for vector based navigation. Front Psychol.  https://doi.org/10.3389/fpsyg.2019.00690 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Lee CT, Amaro R (2018) Exascale computing: a new dawn for computational biology. Comput Sci Eng 20(5):18–25.  https://doi.org/10.1109/MCSE.2018.05329812 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Li PH, Lindsey LF, Januszewski M, Zheng Z, Bates AS, Taisz I, Jain V (2019) Automated reconstruction of a serial-section EM Drosophila brain with flood-filling networks and local realignment. BioRxiv.  https://doi.org/10.1101/605634 CrossRefGoogle Scholar
  50. Lim J, McCarthy C, Shaw D, Cole L, Barnes N (2006). Insect inspired robots. In: Proceedings of the Australasian conference on robotics and automation (ACRA)Google Scholar
  51. Liu T-L, Upadhyayula S, Milkie DE, Singh V, Wang K, Swinburne IA, Betzig E (2018) Observing the cell in its native state: imaging subcellular dynamics in multicellular organisms. Science.  https://doi.org/10.1126/science.aaq1392 CrossRefPubMedPubMedCentralGoogle Scholar
  52. MaBouDi H, Shimazaki H, Giurfa M, Chittka L (2017) Olfactory learning without the mushroom bodies: spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities. PLoS Computational Biol 13(6):e1005551.  https://doi.org/10.1371/journal.pcbi.1005551 CrossRefGoogle Scholar
  53. Marder E, Taylor AL (2011) Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14:133.  https://doi.org/10.1038/nn.2735 CrossRefPubMedPubMedCentralGoogle Scholar
  54. Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153.  https://doi.org/10.1038/nrn1848 CrossRefPubMedGoogle Scholar
  55. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Schürmann F (2015) reconstruction and simulation of neocortical microcircuitry. Cell 163(2):456–492.  https://doi.org/10.1016/j.cell.2015.09.029 CrossRefPubMedGoogle Scholar
  56. Marx V (2013) Brain mapping in high resolution. Nature 503:147.  https://doi.org/10.1038/503147a CrossRefPubMedGoogle Scholar
  57. McDougal R, Hines M, Lytton W (2013) Reaction-diffusion in the NEURON simulator. Front Neuroinform 7:28.  https://doi.org/10.3389/fninf.2013.00028 CrossRefPubMedPubMedCentralGoogle Scholar
  58. McKenna NJ, O’Malley BW (2002) Combinatorial control of gene expression by nuclear receptors and coregulators. Cell 108(4):465–474.  https://doi.org/10.1016/S0092-8674(02)00641-4 CrossRefPubMedGoogle Scholar
  59. Menzel R (1999) Memory dynamics in the honeybee. J Comp Physiol A 185(4):323–340.  https://doi.org/10.1007/s003590050392 CrossRefGoogle Scholar
  60. Menzel R (2012) The honeybee as a model for understanding the basis of cognition. Nat Rev Neurosci 13:758.  https://doi.org/10.1038/nrn3357 CrossRefPubMedGoogle Scholar
  61. Menzel R, Giurfa M (2001) Cognitive architecture of a mini-brain: the honeybee. Trends Cognit Sci 5(2):62–71.  https://doi.org/10.1016/S1364-6613(00)01601-6 CrossRefGoogle Scholar
  62. Menzel R, Kirbach A, Haass W, Fischer B, Fuchs J, Koblofsky M, Greggers U (2011) A common frame of reference for learned and communicated vectors in honeybee navigation. Curr Biol 21(8):645–650.  https://doi.org/10.1016/j.cub.2011.02.039 CrossRefPubMedGoogle Scholar
  63. Mizutani R, Takeuchi A, Uesugi K, Takekoshi S, Osamura RY, Suzuki Y (2010) Microtomographic analysis of neuronal circuits of human brain. Cereb Cortex 20(7):1739–1748.  https://doi.org/10.1093/cercor/bhp237 CrossRefPubMedGoogle Scholar
  64. Mizutani R, Takeuchi A, Uesugi K, Takekoshi S, Nakamura N, Suzuki Y (2011) Building human brain network in 3D coefficient map determined by X-ray microtomography. AIP Conf Proc 1365(1):403–406.  https://doi.org/10.1063/1.3625388 CrossRefGoogle Scholar
  65. Mizutani R, Saiga R, Takeuchi A, Uesugi K, Suzuki Y (2013) Three-dimensional network of Drosophila brain hemisphere. J Struct Biol 184(2):271–279.  https://doi.org/10.1016/j.jsb.2013.08.012 CrossRefPubMedGoogle Scholar
  66. Mizutani R, Saiga R, Ohtsuka M, Miura H, Hoshino M, Takeuchi A, Uesugi K (2016) Three-dimensional X-ray visualization of axonal tracts in mouse brain hemisphere. Sci Rep 6:35061.  https://doi.org/10.1038/srep35061 CrossRefPubMedPubMedCentralGoogle Scholar
  67. Murakami TC, Mano T, Saikawa S, Horiguchi SA, Shigeta D, Baba K, Ueda HR (2018) A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing. Nat Neurosci 21(4):625–637.  https://doi.org/10.1038/s41593-018-0109-1 CrossRefPubMedGoogle Scholar
  68. Nguyen CT, Phung H, Hoang PT, Nguyen TD, Jung H, Choi HR (2018) Development of an insect-inspired hexapod robot actuated by soft actuators. J Mech Robot 10(6):61016–61018.  https://doi.org/10.1115/1.4041258 CrossRefGoogle Scholar
  69. Oizumi M, Albantakis L, Tononi G (2014) From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLoS Comput Biol 10(5):e1003588.  https://doi.org/10.1371/journal.pcbi.1003588 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Orchard I, Lange AB (2012) Advances in insect physiology and endocrinology through genomics, peptidomics, and related technologies 1 Introduction to the virtual symposium on recent advances in understanding a variety of complex regulatory processes in insect physiology and endocrinol. Can J Zool 90(4):435–439.  https://doi.org/10.1139/z2012-015 CrossRefGoogle Scholar
  71. Pacureanu A, Maniates-Selvin J, Kuan AT, Thomas LA, Chen C-L, Cloetens P, Lee W-CA (2019) Dense neuronal reconstruction through X-ray holographic nano-tomography. BioRxiv.  https://doi.org/10.1101/653188 CrossRefGoogle Scholar
  72. Pahl M, Si A, Zhang S (2013) Numerical cognition in bees and other insects. Front Psychol.  https://doi.org/10.3389/fpsyg.2013.00162 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Palyanov A, Khayrulin S, Larson SD, Dibert A (2012) Towards a virtual C. elegans: a framework for simulation and visualization of the neuromuscular system in a 3D physical environment. Silico Biol 11(3–4):137–147.  https://doi.org/10.3233/ISB-2012-0445 CrossRefGoogle Scholar
  74. Park H-J, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342(6158):1238411CrossRefGoogle Scholar
  75. Petrović VM (2018) Artificial intelligence and virtual worlds—toward human-level AI agents. IEEE Access 6:39976–39988.  https://doi.org/10.1109/ACCESS.2018.2855970 CrossRefGoogle Scholar
  76. Rabinovich MI, Varona P, Selverston AI, Abarbanel HDI (2006) Dynamical principles in neuroscience. Rev Mod Phys 78(4):1213–1265.  https://doi.org/10.1103/RevModPhys.78.1213 CrossRefGoogle Scholar
  77. Reichardt W (1987) Evaluation of optical motion information by movement detectors. J Comp Physiol A 161(4):533–547.  https://doi.org/10.1007/BF00603660 CrossRefPubMedGoogle Scholar
  78. Reimann MW, Nolte M, Scolamiero M, Turner K, Perin R, Chindemi G, Markram H (2017) Cliques of neurons bound into cavities provide a missing link between structure and function. Front Comput Neurosci.  https://doi.org/10.3389/fncom.2017.00048 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Rowat P (2007) Interspike interval statistics in the stochastic Hodgkin–Huxley model: coexistence of gamma frequency bursts and highly irregular firing. Neural Comput 19(5):1215–1250.  https://doi.org/10.1162/neco.2007.19.5.1215 CrossRefPubMedGoogle Scholar
  80. Seelig JD, Jayaraman V (2015) Neural dynamics for landmark orientation and angular path integration. Nature 521:186.  https://doi.org/10.1038/nature14446 CrossRefPubMedPubMedCentralGoogle Scholar
  81. Service RF (2018) Design for U.S. exascale computer takes shape. Science 359(6376):617–618CrossRefGoogle Scholar
  82. Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, Hassabis D (2017) Mastering the game of Go without human knowledge. Nature 550:354.  https://doi.org/10.1038/nature24270 CrossRefPubMedGoogle Scholar
  83. Sjöström PJ, Rancz EA, Roth A, Häusser M (2008) Dendritic excitability and synaptic plasticity. Physiol Rev 88(2):769–840.  https://doi.org/10.1152/physrev.00016.2007 CrossRefPubMedGoogle Scholar
  84. Takemura S, Bharioke A, Lu Z, Nern A, Vitaladevuni S, Rivlin PK, Chklovskii DB (2013) A visual motion detection circuit suggested by Drosophila connectomics. Nature 500:175.  https://doi.org/10.1038/nature12450 CrossRefPubMedPubMedCentralGoogle Scholar
  85. Takemura S, Aso Y, Hige T, Wong A, Lu Z, Xu CS, Scheffer LK (2017a) A connectome of a learning and memory center in the adult Drosophila brain. ELife 6:e26975.  https://doi.org/10.7554/eLife.26975 CrossRefPubMedPubMedCentralGoogle Scholar
  86. Takemura S, Nern A, Chklovskii DB, Scheffer LK, Rubin GM, Meinertzhagen IA (2017b) The comprehensive connectome of a neural substrate for ‘ON’ motion detection in Drosophila. ELife 6:e24394.  https://doi.org/10.7554/eLife.24394 CrossRefPubMedPubMedCentralGoogle Scholar
  87. Tschopp FD, Reiser MB, Turaga SC (2018) A connectome based hexagonal lattice convolutional network model of the Drosophila visual system. ArXiv PreprintGoogle Scholar
  88. Ujfalussy BB, Makara JK, Lengyel M, Branco T (2018) Global and multiplexed dendritic computations under in vivo-like conditions. Neuron 100(3):579–592.e5.  https://doi.org/10.1016/j.neuron.2018.08.032 CrossRefPubMedPubMedCentralGoogle Scholar
  89. Ukani NH, Yeh C-H, Tomkins A, Zhou Y, Florescu D, Ortiz CL, Lazar AA (2019) The fruit fly brain observatory: from structure to function. BioRxiv.  https://doi.org/10.1101/580290 CrossRefGoogle Scholar
  90. van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB, Furber SB (2018) Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model. Front Neurosci.  https://doi.org/10.3389/fnins.2018.00291 CrossRefPubMedPubMedCentralGoogle Scholar
  91. van der Voet M, Nijhof B, Oortveld MAW, Schenck A (2014) Drosophila models of early onset cognitive disorders and their clinical applications. Neurosci Biobehav Rev 46:326–342.  https://doi.org/10.1016/j.neubiorev.2014.01.013 CrossRefPubMedGoogle Scholar
  92. Wiederman ZMB, Cazzolato BS, Grainger S, O’Carroll DC, Wiederman SD (2017) An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments. J Neural Eng 14(4):46030CrossRefGoogle Scholar
  93. Yu K-H, Beam AL, Kohane IS (2018) Artificial intelligence in healthcare. Nat Biomed Eng 2(10):719–731.  https://doi.org/10.1038/s41551-018-0305-z CrossRefPubMedGoogle Scholar
  94. Zheng Z, Lauritzen JS, Perlman E, Robinson CG, Nichols M, Milkie D, Bock DD (2018) A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174(3):730–743.e22.  https://doi.org/10.1016/j.cell.2018.06.019 CrossRefPubMedPubMedCentralGoogle Scholar
  95. Zjajo A, Hofmann J, Christiaanse GJ, van Eijk M, Smaragdos G, Strydis C, van Leuken R (2018) A real-time reconfigurable multichip architecture for large-scale biophysically accurate neuron simulation. IEEE Trans Biomed Circuits Syst 12(2):326–337.  https://doi.org/10.1109/TBCAS.2017.2780287 CrossRefPubMedGoogle Scholar
  96. Zou Y, Zhang W, Zhang Z (2016) Liftoff of an electromagnetically driven insect-inspired flapping-wing robot. IEEE Trans Robot 32(5):1285–1289.  https://doi.org/10.1109/TRO.2016.2593449 CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Psychology and NeuroscienceUniversity of Colorado, BoulderBoulderUSA

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