Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Neuromorphic dendritic network computation with silent synapses for visual motion perception

Abstract

Neuromorphic technologies typically employ a point neuron model, neglecting the spatiotemporal nature of neuronal computation. Dendritic morphology and synaptic organization are structurally tailored for spatiotemporal information processing, such as visual perception. Here we report a neuromorphic computational model that integrates synaptic organization with dendritic tree-like morphology. Based on the physics of multigate silicon nanowire transistors with ion-doped sol–gel films, our model—termed dendristor—performs dendritic computation at the device and neural-circuit level. The dendristor offers the bioplausible nonlinear integration of excitatory/inhibitory synaptic inputs and silent synapses with diverse spatial distribution dependency, emulating direction selectivity, which is the feature that reacts to signal direction on the dendrite. We also develop a neuromorphic dendritic neural circuit—a network of interconnected dendritic neurons—that serves as a building block for the design of a multilayer network system that emulates three-dimensional spatial motion perception in the retina.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Neuromorphic dendrite model representation.
Fig. 2: Nonlinear synaptic integration in dendrites and the dendristor.
Fig. 3: Direction selectivity of the dendristor and the effect of a silent synapse.
Fig. 4: Neuromorphic neural circuit for direction selectivity and morphological variation of dendrites.
Fig. 5: Neuromorphic visual perception of motion in a 3D environment.

Similar content being viewed by others

Data availability

The data for this study are available via GitHub at https://github.com/eunhye8747/Dendristor.

Code availability

The codes used in this study are available via GitHub at https://github.com/eunhye8747/Dendristor.

References

  1. Zhang, W. et al. Neuro-inspired computing chips. Nat. Electron. 3, 371–382 (2020).

    Article  Google Scholar 

  2. Mehonic, A. & Kenyon, A. J. Brain-inspired computing needs a master plan. Nature 604, 255–260 (2022).

    Article  Google Scholar 

  3. Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The SpiNNaker project. Proc. IEEE 102, 652–665 (2014).

    Article  Google Scholar 

  4. Boybat, I. et al. Neuromorphic computing with multi-memristive synapses. Nat. Commun. 9, 2514 (2018).

    Article  Google Scholar 

  5. Yi, W. et al. Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018).

    Article  Google Scholar 

  6. Poirazi, P. & Papoutsi, A. Illuminating dendritic function with computational models. Nat. Rev. Neurosci. 21, 303–321 (2020).

    Article  Google Scholar 

  7. Ujfalussy, B. B., Makara, J. K., Lengyel, M. & Branco, T. Global and multiplexed dendritic computations under in vivo-like conditions. Neuron 100, 579–592.e5 (2018).

    Article  Google Scholar 

  8. Sidiropoulou, K., Pissadaki, E. K. & Poirazi, P. Inside the brain of a neuron. EMBO Rep. 7, 886–892 (2006).

  9. Bhalla, U. S. Molecular computation in neurons: a modeling perspective. Curr. Opin. Neurobiol. 25, 31–37 (2014).

  10. Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020).

    Article  Google Scholar 

  11. Zenke, F. et al. Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron 109, 571–575 (2021).

    Article  Google Scholar 

  12. Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).

    Article  Google Scholar 

  13. Cannistraci, C. V., Alanis-Lobato, G. & Ravasi, T. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Sci. Rep. 3, 1613 (2013).

    Article  Google Scholar 

  14. Cannistraci, C. V. Modelling self-organization in complex networks via a brain-inspired network automata theory improves link reliability in protein interactomes. Sci. Rep. 8, 15760 (2018).

    Article  Google Scholar 

  15. London, M. & Häusser, M. Dendritic computation. Annu. Rev. Neurosci. 28, 503–535 (2005).

    Article  Google Scholar 

  16. Boahen, K. Dendrocentric learning for synthetic intelligence. Nature 612, 43–50 (2022).

  17. Wybo, W. A. M., Torben-Nielsen, B., Nevian, T. & Gewaltig, M. O. Electrical compartmentalization in neurons. Cell Rep. 26, 1759–1773.e7 (2019).

    Article  Google Scholar 

  18. Jarvis, S., Nikolic, K. & Schultz, S. R. Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study. PLoS Comput. Biol. 14, e1006027 (2018).

    Article  Google Scholar 

  19. Vlasits, A. L. et al. A role for synaptic input distribution in a dendritic computation of motion direction in the retina. Neuron 89, 1317–1330 (2016).

    Article  Google Scholar 

  20. Iascone, D. M. et al. Whole-neuron synaptic mapping reveals spatially precise excitatory/inhibitory balance limiting dendritic and somatic spiking. Neuron 106, 566–578.e8 (2020).

    Article  Google Scholar 

  21. Ju, N. et al. Spatiotemporal functional organization of excitatory synaptic inputs onto macaque V1 neurons. Nat. Commun. 11, 697 (2020).

    Article  Google Scholar 

  22. Jones, I. S. & Kording, K. P. Might a single neuron solve interesting machine learning problems through successive computations on its dendritic tree? Neural Comput. 33, 1554–1571 (2021).

    Article  MathSciNet  Google Scholar 

  23. Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, e22901 (2017).

    Article  Google Scholar 

  24. Ujfalussy, B. B., Makara, J. K., Branco, T. & Lengyel, M. Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits. eLife 4, e10056 (2015).

    Article  Google Scholar 

  25. Tran-Van-Minh, A. et al. Contribution of sublinear and supralinear dendritic integration to neuronal computations. Front. Cell Neurosci. 9, 67 (2015).

    Article  Google Scholar 

  26. Tzilivaki, A., Kastellakis, G. & Poirazi, P. Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nat. Commun. 10, 3664 (2019).

    Article  Google Scholar 

  27. Li, S. et al. Dendritic computations captured by an effective point neuron model. Proc. Natl Acad. Sci. USA 116, 15244–15252 (2019).

    Article  Google Scholar 

  28. Goetz, L., Roth, A. & Häusser, M. Active dendrites enable strong but sparse inputs to determine orientation selectivity. Proc. Natl Acad. Sci. USA 118, e2017339118 (2021).

    Article  Google Scholar 

  29. Takahashi, N., Oertner, T. G., Hegemann, P. & Larkum, M. E. Active cortical dendrites modulate perception. Science 354, 1587–1590 (2016).

    Article  Google Scholar 

  30. Vaney, D. I., Sivyer, B. & Taylor, W. R. Direction selectivity in the retina: symmetry and asymmetry in structure and function. Nat. Rev. Neurosci. 13, 194–208 (2012).

    Article  Google Scholar 

  31. Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 1307–1312 (2010).

    Article  Google Scholar 

  32. Taylor, W. R., He, S., Levick, W. R. & Vaney, D. I. Dendritic computation of direction selectivity by retinal ganglion cells. Science 289, 2347–2350 (2000).

    Article  Google Scholar 

  33. Mauss, A. S., Vlasits, A., Borst, A. & Feller, M. Visual circuits for direction selectivity. Annu. Rev. Neurosci. 40, 211–230 (2017).

    Article  Google Scholar 

  34. Frank, A. C. et al. Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat. Commun. 9, 422 (2018).

    Article  Google Scholar 

  35. Kaifosh, P. & Losonczy, A. Mnemonic functions for nonlinear dendritic integration in hippocampal pyramidal circuits. Neuron 90, 622–634 (2016).

    Article  Google Scholar 

  36. Gidon, A. & Segev, I. Principles governing the operation of synaptic inhibition in dendrites. Neuron 75, 330–341 (2012).

    Article  Google Scholar 

  37. Malgaroli, A. Silent synapses: I can’t hear you! Could you please speak aloud? Nat. Neurosci. 2, 3–5 (1999).

    Article  Google Scholar 

  38. Kerchner, G. A. & Nicoll, R. A. Silent synapses and the emergence of a postsynaptic mechanism for LTP. Nat. Rev. Neurosci. 9, 813–825 (2008).

    Article  Google Scholar 

  39. Vincent-Lamarre, P., Lynn, M. & Béïque, J. C. The eloquent silent synapse. Trends Neurosci. 41, 557–559 (2018).

    Article  Google Scholar 

  40. Kaiser, J. et al. Emulating dendritic computing paradigms on analog neuromorphic hardware. Neuroscience 489, 290–300 (2022).

    Article  Google Scholar 

  41. Li, X. et al. Power-efficient neural network with artificial dendrites. Nat. Nanotechnol. 15, 776–782 (2020).

    Article  Google Scholar 

  42. Wan, C. J. et al. Flexible metal oxide/graphene oxide hybrid neuromorphic transistors on flexible conducting graphene substrates. Adv. Mater. 28, 5878–5885 (2016).

    Article  Google Scholar 

  43. He, Y. et al. Spatiotemporal information processing emulated by multiterminal neuro-transistor networks. Adv. Mater. 31, e1900903 (2019).

    Article  Google Scholar 

  44. Qian, C., Kong, Lan, Yang, J., Gao, Y. & Sun, J. Multi-gate organic neuron transistors for spatiotemporal information processing. Appl. Phys. Lett. 110, 083302 (2017).

    Article  Google Scholar 

  45. Zhu, L. Q., Cai, J. C., Ren, Z. Y., Xiong, W. & Wan, Q. in Neuromorphic Devices for Brain‐Inspired Computing: Artificial Intelligence, Perception and Robotics (eds Wan, Q. & Shi, Y.) Ch. 3 (Wiley, 2022).

  46. Baek, E. et al. Intrinsic plasticity of silicon nanowire neurotransistors for dynamic memory and learning functions. Nat. Electron. 3, 398–408 (2020).

    Article  Google Scholar 

  47. Wang, D. et al. Recent advanced applications of ion-gel in ionic-gated transistor. npj Flex. Electron. 5, 13 (2021).

  48. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7, 621–627 (2004).

    Article  Google Scholar 

  49. Vervaeke, K., Lorincz, A., Nusser, Z. & Silver, R. A. Gap junctions compensate for sublinear dendritic integration in an inhibitory network. Science 335, 1624–1628 (2012).

    Article  Google Scholar 

  50. Liu, G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites. Nat. Neurosci. 7, 373–379 (2004).

    Article  Google Scholar 

  51. Grienberger, C., Chen, X. & Konnerth, A. Dendritic function in vivo. Trends Neurosci. 38, 45–54 (2015).

    Article  Google Scholar 

  52. Branco, T., Clark, B. A. & Häusser, M. Dendritic discrimination of temporal input sequences in cortical neurons. Science 329, 1671–1675 (2010).

    Article  Google Scholar 

  53. Hanse, E., Seth, H. & Riebe, I. AMPA-silent synapses in brain development and pathology. Nat. Rev. Neurosci. 14, 839–850 (2013).

    Article  Google Scholar 

  54. Xu, W., Löwel, S. & Schlüter, O. M. Silent synapse-based mechanisms of critical period plasticity. Front. Cell Neurosci. 14, 213 (2020).

    Article  Google Scholar 

  55. Zhang, Y., Zhao, J., Wu, W., Muscoloni, A. & Cannistraci, C. V. Ultra-sparse network advantage in deep learning via Cannistraci-Hebb brain-inspired training with hyperbolic meta-deep community-layered epitopology. In The 12th International Conference on Learning Representations (2024).

  56. Welchman, A. E. The human brain in depth: how we see in 3D. Annu. Rev. Vis. Sci. 2, 345–376 (2016).

  57. Bird, A. D., Jedlicka, P. & Cuntz, H. Dendritic normalisation improves learning in sparsely connected artificial neural networks. PLoS Comput. Biol. 17, e1009202 (2021).

    Article  Google Scholar 

  58. Lavzin, M., Rapoport, S., Polsky, A., Garion, L. & Schiller, J. Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature 490, 397–401 (2012).

    Article  Google Scholar 

  59. Smith, S. L., Smith, I. T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).

    Article  Google Scholar 

  60. Whritner, J. A. Visual Perception of Motion in the 3D Environment. PhD thesis, The Univ. of Texas at Austin (2022).

  61. Beniaguev, D., Segev, I. & London, M. Single cortical neurons as deep artificial neural networks. Neuron 109, 2727–2739.e3 (2021).

    Article  Google Scholar 

  62. Dominguez-Sanchez, A., Cazorla, M. & Orts-Escolano, S. Pedestrian movement direction recognition using convolutional neural networks. IEEE Trans. Intell. Transp. Syst. 18, 3540–3548 (2017).

    Article  Google Scholar 

  63. Sun, Y. et al. Evaluating performance, power and energy of deep neural networks on CPUs and GPUs. in Theoretical Computer Science (eds Cai, Z. et al.) 196–221 (Springer, 2021).

  64. Liu, D., Yu, H. & Chai, Y. Low‐power computing with neuromorphic engineering. Adv. Intell. Syst. 3, 2000150 (2021).

  65. Chen, G. et al. Event-based neuromorphic vision for autonomous driving: a paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Process Mag. 37, 34–49 (2020).

    Article  Google Scholar 

  66. Bian, S. et al. ColibriUAV: an ultra-fast, energy-efficient neuromorphic edge processing UAV-platform with event-based and frame-based cameras. In 2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI) 287–292 (2023).

  67. Pei, J. et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572, 106–111 (2019).

    Article  Google Scholar 

  68. Hines, M. L. & Carnevale, N. T. Neuron: a tool for neuroscientists. Neuroscientist 7, 123–135 (2001).

    Article  Google Scholar 

  69. Akar, N. A. et al. Arbor Library v0.8. Zenodo https://doi.org/10.5281/zenodo.1459678 (2022).

  70. Pagkalos, M., Chavlis, S. & Poirazi, P. Introducing the Dendrify framework for incorporating dendrites to spiking neural networks. Nat. Commun. 14, 131 (2023).

    Article  Google Scholar 

Download references

Acknowledgements

E.B. and L.S. are funded by the National Nature Science Foundation of China (no. 62088102). E.B., S.S. and Z.R. are funded by STI2030–Major Projects 2021ZD0200300. S.S. is supported by a grant from Guoqiang Institute, Tsinghua University (2019GQB0001). C.V.C. is funded by the Zhou Yahui Chair Professorship award of Tsinghua University, the starting funding of the Tsinghua Laboratory of Brain and Intelligence (THBI) and the National High-Level Talent Program of the Ministry of Science and Technology of China. C.V.C. thanks A. Malgaroli for introducing and inspiring his research on silent synapses when he was a master student.

Author information

Authors and Affiliations

Authors

Contributions

E.B. and C.V.C. developed the concept of the dendristor and the neuromorphic visual motion perception system. C.V.C. devised the role of the silent synapses and the visual motion perception in dendritic computation. E.B. fabricated and measured the dendritic transistors and developed the NDNCs. S.S. clarified the silent synaptic function for modelling and advised on the biocomputational emulation. E.B. and C.V.C. designed the computational experiments and E.B. realized the computational experiments using LTspice. C.-K.B. fabricated and supported the electrical analysis of the Si nanowire transistors. E.B., Z.R., L.S. and C.V.C. analysed the data and results. E.B., Z.R., L.S. and C.V.C. designed the figures and E.B. realized them. E.B., Z.R., L.S. and C.V.C. wrote the paper and all authors reviewed it. C.V.C. and L.S. supervised the project.

Corresponding authors

Correspondence to Eunhye Baek, Luping Shi or Carlo Vittorio Cannistraci.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Electronics thanks Paschalis Gkoupidenis, Rui Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Sections 1–14 and Figs. 1–14.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baek, E., Song, S., Baek, CK. et al. Neuromorphic dendritic network computation with silent synapses for visual motion perception. Nat Electron 7, 454–465 (2024). https://doi.org/10.1038/s41928-024-01171-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-024-01171-7

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics