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Neural coding of temperature with a DNA-based spiking chemical neuron

Nature Chemical Engineering volume 1, pages 510–521 (2024)Cite this article

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Abstract

Complex organisms perceive their surroundings with sensory neurons that encode physical stimuli into spikes of electrical activities. The past decades have seen a throve of computing approaches taking inspiration from neurons, including reports of DNA-based chemical neurons that mimic artificial neural networks with chemical reactions. Yet, they lack the physical sensing and temporal coding of sensory biological neurons. Here we report a thermosensory chemical neuron based on DNA and enzymes that spikes with chemical activity when exposed to cold. Surprisingly, this chemical neuron shares deep mathematical similarities with a toy model of a cold nociceptive neuron: they follow a similar bifurcation route between rest and oscillations and avoid artefacts associated with canonical bifurcations (such as irreversibility, damping or untimely spiking). We experimentally demonstrate this robustness by encoding—digitally and analogically—thermal messages into chemical waveforms. This chemical neuron could pave the way for implementing the third generation of neural network models (spiking networks) in DNA and opens the door for associative learning.

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Fig. 1: Cold sensation by a chemical neuron.
Fig. 2: Bifurcation routes of our chemical neuron and a toy model of a thermosensory biological neuron.
Fig. 3: Response of canonical dynamical systems as well as chemical and biological neurons to periodic stimulations.
Fig. 4: Modulation of 10-bit spike trains with thermal messages.
Fig. 5: Response of the chemical neuron to noisy temperature stimuli.
Fig. 6: [Exonuclease]–temperature phase diagrams of the chemical neurons.

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Data availability

Data supporting the findings of this study are available within the Article and its Supplementary Information. Source data are provided with this paper.

Code availability

The Mathematica notebooks used for the simulations performed in this study are available in the Supplementary Information.

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Acknowledgements

We thank K. Aihara, T. Kohno, I. Kawamata, Y. Sato, A. Estevez-Torres, J.-C. Galas and R. Brette for discussion on the manuscript. We thank FEMTO-ST (CNRS, Besancon) for the fabrication of the silicon microchambers in the frame of the RENATECH network. Neurons and ionic channels in Fig. 1 were adapted from BioRender.com. This research was supported by JSPS KAKENHI grant no. JP19KK0261 (N.A.-K.), JP20K12061 (N.A.-K. and A.J.G.), CNRS MITI Interdisciplinary Program on Biomimetism (G.G., T.L. and A.J.G.), PEPR MoleculArxiv grant no. ANR-22-PEXM-0002 (A.J.G. and Y.R.) and ERC Union Horizon 2020 grant no. 770940 (N.L.-D.).

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Authors and Affiliations

  1. LIMMS, IRL 2820 CNRS–Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    N. Lobato-Dauzier, A. Baccouche, T. Fujii, S. H. Kim & A. J. Genot

  2. Center for Interdisciplinary AI and Data Science, Ochanomizu University, Tokyo, Japan

    N. Lobato-Dauzier & N. Aubert-Kato

  3. Laboratoire Jean Perrin, UMR 8237 CNRS, Sorbonne Université, Institut de Biologie Paris-Seine, Paris, France

    N. Lobato-Dauzier

  4. Laboratoire Gulliver, UMR 7083 CNRS, ESPCI Paris, PSL Research University, Paris, France

    G. Gines & Y. Rondelez

  5. Laboratoire IMS, UMR5218 CNRS, Bordeaux INP, University of Bordeaux, Talence, France

    T. Levi

  6. Department of Information Sciences, Ochanomizu University, Tokyo, Japan

    N. Aubert-Kato

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  1. N. Lobato-Dauzier
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  2. A. Baccouche
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Contributions

Conceptualization: N.L.-D., A.B., G.G., T.L., Y.R., N.A.-K., A.J.G. Methodology: N.L.-D., A.J.G. Investigation: N.L.-D. Visualization: N.L.-D. Funding acquisition: G.G., T.L., N.A.-K., A.J.G. Supervision: T.F., S.H.K., N.A.-K., A.J.G. Writing (original draft): N.L.-D., N.A.-K., A.J.G.

Corresponding authors

Correspondence to N. Aubert-Kato or A. J. Genot.

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Competing interests

T.F., Y.R. and G.G. have filed a patent on the PEN DNA toolbox (patent no. WO2017141067A1, filed in Europe, Japan, Lithuania, USA, Canada).

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Nature Chemical Engineering thanks Lucia Marucci, Chunhai Fan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Sections 1–9, Figs. 1–28, Tables 1–3, theoretical models and discussion.

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Supplementary Data 1 (download JPG )

Image of the barcoding of exonuclease concentration in encapsulated chemical neurons.

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Chemical neuron bifurcation path.

Supplementary Video 2 (download MP4 )

Biological neuron bifurcation path.

Supplementary Video 3 (download MP4 )

Encapsulated chemical neurons in a temperature gradient.

Supplementary Code 2

Mathematica notebook for chemical neuron toy model.

Supplementary Code 2

Mathematica notebook for biological neuron toy model.

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Lobato-Dauzier, N., Baccouche, A., Gines, G. et al. Neural coding of temperature with a DNA-based spiking chemical neuron. Nat Chem Eng 1, 510–521 (2024). https://doi.org/10.1038/s44286-024-00087-5

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