The case for emulating insect brains using anatomical “wiring diagrams” equipped with biophysical models of neuronal activity
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.
KeywordsConnectomics Hodgkin–Huxley models Insects Whole-brain emulation
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Tschopp FD, Reiser MB, Turaga SC (2018) A connectome based hexagonal lattice convolutional network model of the Drosophila visual system. ArXiv PreprintGoogle Scholar
- 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
- 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