ESR15 - Neuromorphic Embedded Processing for Touch

Identification of neural network architectures for reproducing biological receptive field responses and implementation in neuromorphic hardware.

Overview

Objectives

Identification of neural network architectures for reproducing biological receptive field responses and implementation in neuromorphic hardware. Design, simulation, fabrication and characterization of neuromorphic circuits for spike-based computation of tactile sensory data. Evaluation of network performances with methods used in computational neuroscience to infer the information content about the stimulus (see Research Theme 6). Integration with sensors developed in Research Themes 13 and 14.

Expected Results

I will design and develop novel neuromorphic circuits and systems for emulating biological tactile sensing. To this end the modeling work performed in WP1 will be adapted to the analog VLSI implementation; furthermore, suitable interfaces to the sensors developed in WP3 will be designed. The final embedded system, targeted at reproducing biological RF responses, will substantially advance the state-of-the-art.

Secondments

IIT-CNCS

learn SNN models
UOG

integration of RF hardware with nanowire FET devices
OSSUR

integration on prosthetic device

Supervisors

E. Chicca
R. Dahiya
S. Panzeri
Á. Alexandersson

Michele Mastella

The comparison between different approaches, followed by humans and by nature in problem solving, always charmed me. I have always wanted to merge these two worlds in order to create new structures. I, therefore, decided to pursue my idea in the neuromorphic engineering area.

Neutouch for me

Neutouch collects together different ingredients for a potential great recipe: lot of students with different expertise, distributed across Europe and willing to collaborate in a shared project.

Info

Research Topics

Neuromorphic Circuit, Spiking Neural Network
Institution

University of Groningen
Background

B.Sc. Electronic engineering (Politecnico di Milano, 2013-2016)
M.Sc. Electronic engineering (Politecnico di Milano, 2016-2019)

Thesis

Neuromorphic Embedded Processing for Touch

Abstract

Touch, one of our primary senses, allows us to explore and understand the physical world around us. It involves the detection of mechanical stimuli, such as pressure, vibration, and texture, through specialized receptors distributed across our skin. Robots and prostheses struggle to replicate the nuanced sense of touch found in humans. This limitation degrades their ability to perform tasks reliant on tactile perception, such as object manipulation and texture discrimination, hindering their overall functionality.

For this reason, there is a critical need to enhance these technologies with advanced tactile sensing capabilities to improve their versatility and enable more natural interactions.

In this thesis, we demonstrate how, using neuromorphic principles inspired by neuroscientific literature, we can address these limitations. In the first part of the thesis, we investigate how taking inspiration from mechanoreceptors can inform the design of novel sensors capable of encoding pressure into spike patterns. Following this, we explore the decoding of these signals using networks composed only of spiking neurons and synapses, drawing inspiration from biological findings. The resulting architectures show dynamics qualitatively resembling evidence from neuroscientific experiments. Finally, we demonstrate how the networks we designed can be translated onto CMOS hardware for future deployments in real-world agents.

Our results underscore the importance of leveraging neuroscientific literature to inform the design of future technologies for tactile perception in artificial agents.

This paradigm promises extremely powerful given the shared constraints between artificial systems and their biological counterparts.

This work lays down a compelling example of drawing inspiration from biology to enhance the design of artificial agents with tactile capabilities. As such, the results presented in this thesis pave the way for further research endeavors informed by the
blueprint of natural systems, encouraging neuromorphic engineers to explore the knowledge available in biology to inform their own designs. Embracing biological principles not only facilitates the development of more efficient and effective artificial agents but also fosters a deeper understanding of the natural world.

Publications

Mastella, M., Tiemens, T., & Chicca, E. (2024). Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition. arXiv preprint arXiv:2403.09723.

Mastella, M., & Chicca, E. (2021, May). A hardware-friendly neuromorphic spiking neural network for frequency detection and fine texture decoding. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.

Mastella, M., Greatorex, H., Tiemens, T., Cotteret, M., Richter, O., & Chicca, E. (2024, October). Event-Driven Frequency Decomposition with Spiking Phase-Locked Loops. In 2024 58th Asilomar Conference on Signals, Systems, and Computers (pp. 1517-1521). IEEE.

Greatorex, H., Richter, O., Mastella, M., Cotteret, M., Klein, P., Fabre, M., ... & Chicca, E. (2025). A neuromorphic processor with on-chip learning for beyond-CMOS device integration. Nature communications, 16(1), 6424.

Greatorex, H., Richter, O., Mastella, M., Cotteret, M., Klein, P., Fabre, M., ... & Chicca, E. (2024). TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration. arXiv preprint arXiv:2410.15854.

Sengupta, D., Mastella, M., Chicca, E., & Kottapalli, A. G. P. (2022). Skin-inspired flexible and stretchable electrospun carbon nanofiber sensors for neuromorphic sensing. ACS applied electronic materials, 4(1), 308-315.

Cipollini, D., Greatorex, H., Mastella, M., Chicca, E., & Schomaker, L. (2025). Fused-MemBrain: a spiking processor combining CMOS and self-assembled memristive networks. Neuromorphic Computing and Engineering, 5(2), 024002.

Mastella, M., Greatorex, H., Cotteret, M., Janotte, E., Soares Girao, W., Richter, O., & Chicca, E. (2023, August). Synaptic normalisation for on-chip learning in analog CMOS spiking neural networks. In Proceedings of the 2023 International Conference on Neuromorphic Systems (pp. 1-4).

Dabbous, A., Mastella, M., Natarajan, A., & Chicca, E. (2021, May). Artificial bio-inspired tactile receptive fields for edge orientation classification. In 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.

Richter, O., Greatorex, H., Hucko, B., Cotteret, M., Soares Girao, W., Janotte, E., ... & Chicca, E. (2023, August). A subthreshold second-order integration circuit for versatile synaptic alpha kernel and trace generation. In Proceedings of the 2023 International Conference on Neuromorphic Systems (pp. 1-4).

Cotteret, M., Richter, O., Mastella, M., Greatorex, H., Janotte, E., Girão, W. S., ... & Chicca, E. (2023, May). Robust spiking attractor networks with a hard winner-take-all neuron circuit. In 2023 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.

Greatorex, H., Mastella, M., Cotteret, M., Richter, O., & Chicca, E. (2025). Event-based vision for egomotion estimation using precise event timing. arXiv preprint arXiv:2501.11554.

Greatorex, H., Mastella, M., Richter, O., Cotteret, M., Girão, W. S., Janotte, E., & Chicca, E. (2025). A scalable event-driven spatiotemporal feature extraction circuit. arXiv preprint arXiv:2501.10155.

Janotte, E., Mastella, M., Chicca, E., & Bartolozzi, C. (2021). Touch in robots: A neuromorphic approach. ERCIM News, (125), 34-51.

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