Treffer: Bioinspired spiking architecture enables energy constrained touch encoding.
Title:
Bioinspired spiking architecture enables energy constrained touch encoding.
Authors:
Ortone A; The BioRobotics Institute, Sant'Anna School of Advanced Studies, Pisa, Italy.; Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Pisa, Italy., Filosa M; The BioRobotics Institute, Sant'Anna School of Advanced Studies, Pisa, Italy.; Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Pisa, Italy.; Interdisciplinary Research Center Health Science, Sant'Anna School of Advanced Studies, Pisa, Italy., Indiveri G; Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland., Desoli G; STMicroelectronics, Cornaredo, Italy., Mazzoni A; The BioRobotics Institute, Sant'Anna School of Advanced Studies, Pisa, Italy.; Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Pisa, Italy., Oddo CM; The BioRobotics Institute, Sant'Anna School of Advanced Studies, Pisa, Italy. calogero.oddo@santannapisa.it.; Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Pisa, Italy. calogero.oddo@santannapisa.it.; Interdisciplinary Research Center Health Science, Sant'Anna School of Advanced Studies, Pisa, Italy. calogero.oddo@santannapisa.it.
Source:
Nature communications [Nat Commun] 2026 Jan 28. Date of Electronic Publication: 2026 Jan 28.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : Nature Pub. Group
References:
Robles-De-La-Torre, G. The importance of the sense of touch in virtual and real environments. Ieee Multimed. 13, 24–30 (2006).
Dijkerman, H. C. & De Haan, E. H. Somatosensory processing subserving perception and action: Dissociations, interactions, and integration. Behav. Brain Sci. 30, 224–230 (2007).
Abraira, V. E. & Ginty, D. D. The sensory neurons of touch. neuron 79, 618–639 (2013).
Turecek, J., Lehnert, B. P. & Ginty, D. D. The encoding of touch by somatotopically aligned dorsal column subdivisions. Nature 612, 310–315 (2022).
Vallbo, A. B. & Johansson, R. S. & others. Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum. Neurobiol. 3, 3–14 (1984).
Vallbo, A., Olausson, H., Wessberg, J. & Kakuda, N. Receptive field characteristics of tactile units with myelinated afferents in hairy skin of human subjects. J. Physiol. 483, 783–795 (1995).
Johansson, R. S. & Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).
Mukherjee, R., Ganguly, P. & Dahiya, R. Bioinspired distributed energy in robotics and enabling technologies. Adv. Intell. Syst. 5, 2100036 (2023).
Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).
Liu, F. et al. Printed synaptic transistor–based electronic skin for robots to feel and learn. Sci. Robot. 7, eabl7286 (2022).
Yang, J. C. et al. Electronic skin: recent progress and future prospects for skin-attachable devices for health monitoring, robotics, and prosthetics. Adv. Mater. 31, 1904765 (2019).
Wang, M. et al. Artificial skin perception. Adv. Mater. 33, 2003014 (2021).
Osborn, L. E. et al. Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Sci. Robot. 3, eaat3818 (2018).
Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).
Bragança, S. et al.) 641–650 (Springer International Publishing, 2019) https://doi.org/10.1007/978-3-030-14730-3_68.
Jung, B., Kim, B., Koo, J. C., Choi, H. R. & Moon, H. Joint torque sensor embedded in harmonic drive using order tracking method for robotic application. IEEEASME Trans. Mechatron. 22, 1594–1599 (2017).
Orekhov, A. L., Johnston, G., Abah, C., Choset, H. & Simaan, N. Towards collaborative robots with sensory awareness: preliminary results using multi-modal sensing. in Proceedings of the IEEE ICRA workshop on Physical human-robot interaction: a design focus 1–5 (IEEE, 2019).
Kaltenbrunner, M. et al. An ultra-lightweight design for imperceptible plastic electronics. Nature 499, 458–463 (2013).
Nawrocki, R. A., Matsuhisa, N., Yokota, T. & Someya, T. 300-nm imperceptible, ultraflexible, and biocompatible e-skin fit with tactile sensors and organic transistors. Adv. Electron. Mater. 2, 1500452 (2016).
Xiang, S. et al. Liquid-metal-based dynamic thermoregulating and self-powered electronic skin. Adv. Funct. Mater. 31, 2100940 (2021).
Dahiya, R. et al. Large-area soft e-skin: the challenges beyond sensor designs. Proc. IEEE 107, 2016–2033 (2019).
Zhao, C., Park, J., Root, S. E. & Bao, Z. Skin-inspired soft bioelectronic materials, devices and systems. Nat. Rev. Bioeng. 2, 671–690 (2024).
Massari, L. et al. Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin. Nat. Mach. Intell. 4, 425–435 (2022).
Taunyazov, T., Koh, H. F., Wu, Y., Cai, C. & Soh, H. Towards effective tactile identification of textures using a hybrid touch approach. in 2019 International Conference on Robotics and Automation (ICRA) 4269–4275 (IEEE, 2019).
Yan, Y. et al. Soft magnetic skin for super-resolution tactile sensing with force self-decoupling. Sci. Robot. 6, eabc8801 (2021).
Park, K. et al. A biomimetic elastomeric robot skin using electrical impedance and acoustic tomography for tactile sensing. Sci. Robot. 7, eabm7187 (2022).
Bai, N. et al. A robotic sensory system with high spatiotemporal resolution for texture recognition. Nat. Commun. 14, 7121 (2023).
Hu, Z. et al. Machine learning for tactile perception: advancements, challenges, and opportunities. Adv. Intell. Syst. 5, 2200371 (2023).
Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).
Chen, L. et al. Spike timing–based coding in neuromimetic tactile system enables dynamic object classification. Science 384, 660–665 (2024).
Yuan, R. et al. A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system. Nat. Commun. 13, 3973 (2022).
Han, J.-K. et al. Self-powered artificial mechanoreceptor based on triboelectrification for a neuromorphic tactile system. Adv. Sci. 9, 2105076 (2022).
Taunyazov, T., Chua, Y., Gao, R., Soh, H. & Wu, Y. Fast texture classification using tactile neural coding and spiking neural network. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 9890–9895 (IEEE, 2020).
Rongala, U. B., Mazzoni, A. & Oddo, C. M. Neuromorphic artificial touch for categorization of naturalistic textures. IEEE Trans. Neural Netw. Learn. Syst. 28, 819–829 (2015).
Liu, F. et al. Neuro-inspired electronic skin for robots. Sci. Robot. 7, eabl7344 (2022).
Baek, E. et al. Neuromorphic dendritic network computation with silent synapses for visual motion perception. Nat. Electron. 7, 454–465 (2024).
Hill, K. O. & Meltz, G. Fiber Bragg grating technology fundamentals and overview. J. Light. Technol. 15, 1263–1276 (1997).
Pereira Resende da Costa, A. C., Filosa, M., Barbosa Soares, A. & Oddo, C. M. Type II mechanoreceptors and cuneate spiking neuronal network enable touch localization on a large-area e-skin. Nat. Mach. Intell. 7, 1278–1291 (2025).
Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12, 106–122 (2018).
Zendrikov, D., Solinas, S. & Indiveri, G. Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems. Neuromorphic Comput. Eng. 3, 034002 (2023).
Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).
Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).
Lepora, N. F. et al. Tactile superresolution and biomimetic hyperacuity. IEEE Trans. Robot. 31, 605–618 (2015).
Tsividis, Y. Event-driven data acquisition and digital signal processing—A tutorial. IEEE Trans. Circuits Syst. II Express Briefs 57, 577–581 (2010).
Rongala, U. B. et al. Intracellular dynamics in cuneate nucleus neurons support self-stabilizing learning of generalizable tactile representations. Front. Cell. Neurosci. 12, 210 (2018).
Parker, G. A. & Smith, J. M. Optimality theory in evolutionary biology. Nature 348, 27–33 (1990).
Jones, H. E., Andolina, I. M., Oakely, N. M., Murphy, P. C. & Sillito, A. M. Spatial summation in lateral geniculate nucleus and visual cortex. Exp. Brain Res. 135, 279–284 (2000).
Arthur, R., Pfeiffer, R. & Suga, N. Properties of ‘two-tone inhibition’in primary auditory neurones. J. Physiol. 212, 593–609 (1971).
Zhao, J., Monforte, M., Indiveri, G., Bartolozzi, C. & Donati, E. Learning inverse kinematics using neural computational primitives on neuromorphic hardware. npj Robot 1, 1 (2023).
Donati, E. & Valle, G. Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 15, 556 (2024).
Li, G., Liu, S., Wang, L. & Zhu, R. Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition. Sci. Robot. 5, eabc8134 (2020).
Elaskar, J. et al Integrated high-speed wavelength tracking on a silicon chip. J. Light. Technol. 43, 3388–3396 (2024).
Euler, T., Haverkamp, S., Schubert, T. & Baden, T. Retinal bipolar cells: elementary building blocks of vision. Nat. Rev. Neurosci. 15, 507–519 (2014).
Souffi, S., Nodal, F. R., Bajo, V. M. & Edeline, J.-M. When and how does the auditory cortex influence subcortical auditory structures? New insights about the roles of descending cortical projections. Front. Neurosci. 15, 690223 (2021).
Serre, T., Oliva, A. & Poggio, T. A feedforward architecture accounts for rapid categorization. Proc. Natl. Acad. Sci. USA 104, 6424–6429 (2007).
Maheswaranathan, N. et al. Interpreting the retinal neural code for natural scenes: From computations to neurons. Neuron 111, 2742–2755 (2023).
Rao, Y.-J. In-fibre Bragg grating sensors. Meas. Sci. Technol. 8, 355 (1997).
Werbos, P. J. Backpropagation through time: what it does and how to do it. Proc. IEEE 78, 1550–1560 (1990).
Wu, Y., Deng, L., Li, G., Zhu, J. & Shi, L. Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks. Front. Neurosci. 12, 331 (2018).
Shrestha, S. B. & Orchard, G. Slayer: spike layer error reassignment in time. Adv. Neural Inf. Process. Syst. 31, 1419–1428 (2018).
Eshraghian, J. K. et al. Training spiking neural networks using lessons from deep learning. Proc. IEEE 111, 1016–1054 (2023).
Neftci, E. O., Mostafa, H. & Zenke, F. Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36, 51–63 (2019).
Hammouamri, I., Khalfaoui-Hassani, I. & Masquelier, T. Learning delays in spiking neural networks using dilated convolutions with learnable spacings. (ICLR, 2023).
Bartolozzi, C., Indiveri, G. & Donati, E. Embodied neuromorphic intelligence. Nat. Commun. 13, 1024 (2022).
Rathi, N. et al. Exploring neuromorphic computing based on spiking neural networks: algorithms to hardware. ACM Comput. Surv. 55, 1–49 (2023).
Mayr, C., Hoeppner, S. & Furber, S. Spinnaker 2: a 10 million core processor system for brain simulation and machine learning. ArXiv Prepr. ArXiv191102385 (2019).
Pei, J. et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572, 106–111 (2019).
Orchard, G. et al. Efficient neuromorphic signal processing with loihi 2. in 2021 IEEE Workshop on Signal Processing Systems (SiPS) 254–259 (IEEE, 2021).
Pehle, C. et al. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci. 16, 795876 (2022).
Yao, M. et al. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat. Commun. 15, 4464 (2024).
Frenkel, C., Bol, D. & Indiveri, G. Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence. Proceedings of the IEEE 111, 623–652 (2023).
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).
Mead, C. How we created neuromorphic engineering. Nat. Electron. 3, 434–435 (2020).
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).
Chicca, E., Stefanini, F., Bartolozzi, C. & Indiveri, G. Neuromorphic electronic circuits for building autonomous cognitive systems. Proc. IEEE 102, 1367–1388 (2014).
Brette, R. & Gerstner, W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637–3642 (2005).
Ortone, A. et al. Code Ocean capsule, Bioinspired spiking architecture enables energy constrained touch encoding (2025).
Dijkerman, H. C. & De Haan, E. H. Somatosensory processing subserving perception and action: Dissociations, interactions, and integration. Behav. Brain Sci. 30, 224–230 (2007).
Abraira, V. E. & Ginty, D. D. The sensory neurons of touch. neuron 79, 618–639 (2013).
Turecek, J., Lehnert, B. P. & Ginty, D. D. The encoding of touch by somatotopically aligned dorsal column subdivisions. Nature 612, 310–315 (2022).
Vallbo, A. B. & Johansson, R. S. & others. Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum. Neurobiol. 3, 3–14 (1984).
Vallbo, A., Olausson, H., Wessberg, J. & Kakuda, N. Receptive field characteristics of tactile units with myelinated afferents in hairy skin of human subjects. J. Physiol. 483, 783–795 (1995).
Johansson, R. S. & Flanagan, J. R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).
Mukherjee, R., Ganguly, P. & Dahiya, R. Bioinspired distributed energy in robotics and enabling technologies. Adv. Intell. Syst. 5, 2100036 (2023).
Kim, Y. et al. A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018).
Liu, F. et al. Printed synaptic transistor–based electronic skin for robots to feel and learn. Sci. Robot. 7, eabl7286 (2022).
Yang, J. C. et al. Electronic skin: recent progress and future prospects for skin-attachable devices for health monitoring, robotics, and prosthetics. Adv. Mater. 31, 1904765 (2019).
Wang, M. et al. Artificial skin perception. Adv. Mater. 33, 2003014 (2021).
Osborn, L. E. et al. Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Sci. Robot. 3, eaat3818 (2018).
Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).
Bragança, S. et al.) 641–650 (Springer International Publishing, 2019) https://doi.org/10.1007/978-3-030-14730-3_68.
Jung, B., Kim, B., Koo, J. C., Choi, H. R. & Moon, H. Joint torque sensor embedded in harmonic drive using order tracking method for robotic application. IEEEASME Trans. Mechatron. 22, 1594–1599 (2017).
Orekhov, A. L., Johnston, G., Abah, C., Choset, H. & Simaan, N. Towards collaborative robots with sensory awareness: preliminary results using multi-modal sensing. in Proceedings of the IEEE ICRA workshop on Physical human-robot interaction: a design focus 1–5 (IEEE, 2019).
Kaltenbrunner, M. et al. An ultra-lightweight design for imperceptible plastic electronics. Nature 499, 458–463 (2013).
Nawrocki, R. A., Matsuhisa, N., Yokota, T. & Someya, T. 300-nm imperceptible, ultraflexible, and biocompatible e-skin fit with tactile sensors and organic transistors. Adv. Electron. Mater. 2, 1500452 (2016).
Xiang, S. et al. Liquid-metal-based dynamic thermoregulating and self-powered electronic skin. Adv. Funct. Mater. 31, 2100940 (2021).
Dahiya, R. et al. Large-area soft e-skin: the challenges beyond sensor designs. Proc. IEEE 107, 2016–2033 (2019).
Zhao, C., Park, J., Root, S. E. & Bao, Z. Skin-inspired soft bioelectronic materials, devices and systems. Nat. Rev. Bioeng. 2, 671–690 (2024).
Massari, L. et al. Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin. Nat. Mach. Intell. 4, 425–435 (2022).
Taunyazov, T., Koh, H. F., Wu, Y., Cai, C. & Soh, H. Towards effective tactile identification of textures using a hybrid touch approach. in 2019 International Conference on Robotics and Automation (ICRA) 4269–4275 (IEEE, 2019).
Yan, Y. et al. Soft magnetic skin for super-resolution tactile sensing with force self-decoupling. Sci. Robot. 6, eabc8801 (2021).
Park, K. et al. A biomimetic elastomeric robot skin using electrical impedance and acoustic tomography for tactile sensing. Sci. Robot. 7, eabm7187 (2022).
Bai, N. et al. A robotic sensory system with high spatiotemporal resolution for texture recognition. Nat. Commun. 14, 7121 (2023).
Hu, Z. et al. Machine learning for tactile perception: advancements, challenges, and opportunities. Adv. Intell. Syst. 5, 2200371 (2023).
Schuman, C. D. et al. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2, 10–19 (2022).
Chen, L. et al. Spike timing–based coding in neuromimetic tactile system enables dynamic object classification. Science 384, 660–665 (2024).
Yuan, R. et al. A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system. Nat. Commun. 13, 3973 (2022).
Han, J.-K. et al. Self-powered artificial mechanoreceptor based on triboelectrification for a neuromorphic tactile system. Adv. Sci. 9, 2105076 (2022).
Taunyazov, T., Chua, Y., Gao, R., Soh, H. & Wu, Y. Fast texture classification using tactile neural coding and spiking neural network. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 9890–9895 (IEEE, 2020).
Rongala, U. B., Mazzoni, A. & Oddo, C. M. Neuromorphic artificial touch for categorization of naturalistic textures. IEEE Trans. Neural Netw. Learn. Syst. 28, 819–829 (2015).
Liu, F. et al. Neuro-inspired electronic skin for robots. Sci. Robot. 7, eabl7344 (2022).
Baek, E. et al. Neuromorphic dendritic network computation with silent synapses for visual motion perception. Nat. Electron. 7, 454–465 (2024).
Hill, K. O. & Meltz, G. Fiber Bragg grating technology fundamentals and overview. J. Light. Technol. 15, 1263–1276 (1997).
Pereira Resende da Costa, A. C., Filosa, M., Barbosa Soares, A. & Oddo, C. M. Type II mechanoreceptors and cuneate spiking neuronal network enable touch localization on a large-area e-skin. Nat. Mach. Intell. 7, 1278–1291 (2025).
Moradi, S., Qiao, N., Stefanini, F. & Indiveri, G. A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circuits Syst. 12, 106–122 (2018).
Zendrikov, D., Solinas, S. & Indiveri, G. Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems. Neuromorphic Comput. Eng. 3, 034002 (2023).
Wan, W. et al. A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022).
Ambrogio, S. et al. An analog-AI chip for energy-efficient speech recognition and transcription. Nature 620, 768–775 (2023).
Lepora, N. F. et al. Tactile superresolution and biomimetic hyperacuity. IEEE Trans. Robot. 31, 605–618 (2015).
Tsividis, Y. Event-driven data acquisition and digital signal processing—A tutorial. IEEE Trans. Circuits Syst. II Express Briefs 57, 577–581 (2010).
Rongala, U. B. et al. Intracellular dynamics in cuneate nucleus neurons support self-stabilizing learning of generalizable tactile representations. Front. Cell. Neurosci. 12, 210 (2018).
Parker, G. A. & Smith, J. M. Optimality theory in evolutionary biology. Nature 348, 27–33 (1990).
Jones, H. E., Andolina, I. M., Oakely, N. M., Murphy, P. C. & Sillito, A. M. Spatial summation in lateral geniculate nucleus and visual cortex. Exp. Brain Res. 135, 279–284 (2000).
Arthur, R., Pfeiffer, R. & Suga, N. Properties of ‘two-tone inhibition’in primary auditory neurones. J. Physiol. 212, 593–609 (1971).
Zhao, J., Monforte, M., Indiveri, G., Bartolozzi, C. & Donati, E. Learning inverse kinematics using neural computational primitives on neuromorphic hardware. npj Robot 1, 1 (2023).
Donati, E. & Valle, G. Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 15, 556 (2024).
Li, G., Liu, S., Wang, L. & Zhu, R. Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition. Sci. Robot. 5, eabc8134 (2020).
Elaskar, J. et al Integrated high-speed wavelength tracking on a silicon chip. J. Light. Technol. 43, 3388–3396 (2024).
Euler, T., Haverkamp, S., Schubert, T. & Baden, T. Retinal bipolar cells: elementary building blocks of vision. Nat. Rev. Neurosci. 15, 507–519 (2014).
Souffi, S., Nodal, F. R., Bajo, V. M. & Edeline, J.-M. When and how does the auditory cortex influence subcortical auditory structures? New insights about the roles of descending cortical projections. Front. Neurosci. 15, 690223 (2021).
Serre, T., Oliva, A. & Poggio, T. A feedforward architecture accounts for rapid categorization. Proc. Natl. Acad. Sci. USA 104, 6424–6429 (2007).
Maheswaranathan, N. et al. Interpreting the retinal neural code for natural scenes: From computations to neurons. Neuron 111, 2742–2755 (2023).
Rao, Y.-J. In-fibre Bragg grating sensors. Meas. Sci. Technol. 8, 355 (1997).
Werbos, P. J. Backpropagation through time: what it does and how to do it. Proc. IEEE 78, 1550–1560 (1990).
Wu, Y., Deng, L., Li, G., Zhu, J. & Shi, L. Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks. Front. Neurosci. 12, 331 (2018).
Shrestha, S. B. & Orchard, G. Slayer: spike layer error reassignment in time. Adv. Neural Inf. Process. Syst. 31, 1419–1428 (2018).
Eshraghian, J. K. et al. Training spiking neural networks using lessons from deep learning. Proc. IEEE 111, 1016–1054 (2023).
Neftci, E. O., Mostafa, H. & Zenke, F. Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36, 51–63 (2019).
Hammouamri, I., Khalfaoui-Hassani, I. & Masquelier, T. Learning delays in spiking neural networks using dilated convolutions with learnable spacings. (ICLR, 2023).
Bartolozzi, C., Indiveri, G. & Donati, E. Embodied neuromorphic intelligence. Nat. Commun. 13, 1024 (2022).
Rathi, N. et al. Exploring neuromorphic computing based on spiking neural networks: algorithms to hardware. ACM Comput. Surv. 55, 1–49 (2023).
Mayr, C., Hoeppner, S. & Furber, S. Spinnaker 2: a 10 million core processor system for brain simulation and machine learning. ArXiv Prepr. ArXiv191102385 (2019).
Pei, J. et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572, 106–111 (2019).
Orchard, G. et al. Efficient neuromorphic signal processing with loihi 2. in 2021 IEEE Workshop on Signal Processing Systems (SiPS) 254–259 (IEEE, 2021).
Pehle, C. et al. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci. 16, 795876 (2022).
Yao, M. et al. Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip. Nat. Commun. 15, 4464 (2024).
Frenkel, C., Bol, D. & Indiveri, G. Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence. Proceedings of the IEEE 111, 623–652 (2023).
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990).
Mead, C. How we created neuromorphic engineering. Nat. Electron. 3, 434–435 (2020).
Indiveri, G. et al. Neuromorphic silicon neuron circuits. Front. Neurosci. 5, 73 (2011).
Chicca, E., Stefanini, F., Bartolozzi, C. & Indiveri, G. Neuromorphic electronic circuits for building autonomous cognitive systems. Proc. IEEE 102, 1367–1388 (2014).
Brette, R. & Gerstner, W. Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637–3642 (2005).
Ortone, A. et al. Code Ocean capsule, Bioinspired spiking architecture enables energy constrained touch encoding (2025).
Entry Date(s):
Date Created: 20260128 Latest Revision: 20260128
Update Code:
20260129
DOI:
10.1038/s41467-026-68858-7
PMID:
41605933
Database:
MEDLINE
Weitere Informationen
The sense of touch is essential for safe interactions with the external world, enabling humans to rapidly detect and localize physical stimuli. Biological systems achieve these abilities through hundreds of thousands of mechanoreceptors distributed across the skin and efficiently processing vast streams of tactile information. Replicating these affordances in autonomous systems is crucial for advancing robotics. However, current tactile sensing solutions face critical challenges, including excessive wiring, high energy demnds of AI computing, and limitations in scalability and parallel processing. Here, we present a modular artificial tactile system combining a Fiber Bragg Grating-based e-skin with a spiking neural network (SNN) that mimics the early stages of the human somatosensory system. Our architecture achieves up to 10× localization super-resolution, improving localization accuracy by 32% over state-of-the-art deep learning methods and effectively generalizing to multitouch and dynamic conditions. Crucially, when implemented on a neuromorphic chip, the SNN demonstrates robustness to the constrained resolution and mismatches of analog neurons, bolstering highly parallel and sub-mWatt hardwired computation. Bioinspired connectivity is shown to functionally influence tactile processing, offering mechanistic insights in a framework that bridges physiological hypotheses, modeling, and validation in a real-world tactile scenario. These results demonstrate a scalable, energetically sustainable solution for touch perception, with immediate applications in autonomous systems requiring safe human interaction and operation in dynamic environments.
(© 2026. The Author(s).)
Competing interests: The authors declare the following competing interests. This research was conducted through a collaboration between academic and industrial partners. The PhD position of A.O. was co-funded by STMicroelectronics. C.M.O. served as the scientific responsible for the funding agreement between Sant’Anna School of Advanced Studies, Pisa and STMicroelectronics and has reported consulting and advisory activities with STMicroelectronics. C.M.O. discloses a patent filed on the developed artificial skin and collaborative robot arm integrating FBG transducers (application number IT201900003657A1). G.D. is currently employed by STMicroelectronics. All remaining authors declare no competing interests.