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Neuromorphic dendritic network computation with silent synapses for visual motion perception

Nature Electronics volume 7, pages 454–465 (2024)Cite this article

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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.

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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.

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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.

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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

  1. Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instruments, Tsinghua University, Beijing, China

    Eunhye Baek, Zhao Rong & Luping Shi

  2. Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, Beijing, China

    Eunhye Baek & Carlo Vittorio Cannistraci

  3. Tsinghua University-China Electronics Technology HIK Group Co. Joint Research Center for Brain-inspired Computing, Beijing, China

    Eunhye Baek, Zhao Rong & Luping Shi

  4. Center for Neural Computation and Social Interactions, Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, Beijing, China

    Sen Song

  5. IDG/McGovern Institute for Brain Research at Tsinghua University, Beijing, China

    Sen Song, Zhao Rong & Luping Shi

  6. School of Biomedical Engineering, Tsinghua University, Beijing, China

    Sen Song & Carlo Vittorio Cannistraci

  7. Future IT Innovation Laboratory, Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea

    Chang-Ki Baek

  8. Department of Computer Science, Tsinghua University, Beijing, China

    Carlo Vittorio Cannistraci

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  1. Eunhye Baek
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  2. Sen Song
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  3. Chang-Ki Baek
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  4. Zhao Rong
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  6. Carlo Vittorio Cannistraci
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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.

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

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

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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

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