morphic e-skin that produces a sparse spiking output, to solve the engineering problem of data bandwidth for robotic e-skin. The signal can be then processed by traditional perception modules. However, the spike-based encoding calls for the implementation of spiking neural networks for extracting information. Spiking neural networks The spiking asynchronous nature of neuromorphic tactile encoding paves the way to the use of spiking neural networks (SNNs) to infer information about the tactile stimulus. SNNs use neuron and synapse models that more closely match the behaviour of biology, using spatio-temporal sequences of spike to encode and decode information. Synaptic strength and the connectivity of the networks are shaped by experience, through learning. Examples of neuromorphic tactile systems that couple event-driven sensing with SNN have been developed for orientation detection, where the coincident activations of several event-driven sensors is used to understand the angle at which a bar, pressed on the skin, is tilted [2]. Different sensors’ outputs are joined together and connected to a neuron, when the neuron spikes it signals that those sensors were active together. Another example is the recog-
nition of textures, which can be done by recreating a spiking neural network that senses frequencies [3]. A novel architecture, composed only of neurons and synapses organised in a recurrent fashion, can spot these frequencies and signal the texture of a given material.
These two novel paradigms have several advantages that will result in a structure capable of exploring the environment with bioinspired and efficient architectures. This combined approach can greatly enhance the world of autonomous agents.
These networks can be built in neuromorphic mixed-mode subthreshold CMOS technology, to emulate SNNs using the same technological processes of traditional chips, but exploiting an extremely low-power region of the transistor’s behaviour. By embedding the networks directly on silicon using this strategy, we aim for low power consumption, enabled by the neuron’s ability to consume energy only when active and to interface directly with event-driven data.
Link: [L1] https://neutouch.eu/
Conclusion Our goal is to equip autonomous agents, such as robots or prosthesis, with the sense of touch, using the neuromorphic approach both for sensing and processing. We will employ eventdriven sensors to capture temporal information in stimuli and to encode it in spikes and spiking neural networks to process data in real time, with low power consumption. The network will be implemented on a silicon technology, delivering the first neuromorphic chip for touch.
References: [1] C. Bartolozzi, L. Natale, L., Nori, G. Metta: “Robots with a sense of touch”, Nature Materials 15, 921–925 (2016). [2] A. Dabbous, et. al.: “Artificial Bioinspired Tactile Receptive Fields for Edge Orientation Classification”, ISCAS (2021) [in press]. [3] M. Mastella, E. Chicca: “A Hardware-friendly Neuromorphic Spiking Neural Network for Frequency Detection and Fine Texture Decoding”, ISCAS (2021) [in press]. Please contact: Ella Janotte, Italian Institute of Technology, iCub facility, Genoa, Italy. ella.janotte@iit.it Michele Mastella, BICS Lab, Zernike Inst Adv Mat, University of Groningen, Netherlands m.mastella@rug.nl
uncovering Neuronal Learning Principles through Artificial Evolution by Henrik D. Mettler (University of Bern), Virginie Sabado (University of Bern), Walter Senn (University of Bern), Mihai A. Petrovici (University of Bern and Heidelberg University) and Jakob Jordan (University of Bern) Despite years of progress, we still lack a complete understanding of learning and memory. We leverage optimisation algorithms inspired by natural evolution to discover phenomenological models of brain plasticity and thus uncover clues to the underlying computational principles. We hope this accelerates progress towards deep insights into information processing in biological systems with immanent potential for the development of powerful artificial learning machines. What is the most powerful computing device in your home? Maybe your laptop or smartphone spring to mind, or possibly your new home automation system. Think again! With a power consumption of just 10W, our brains can extract complex information from a high-throughput stream of sensory inputs like no other known system. But what enables this squishy mass of ERCIM NEWS 125 April 2021
intertwined cells to perform the required computations? Neurons, capable of exchanging signals via short electrical pulses called “action potentials” or simply “spikes”, are the main carriers and processors of information in the brain. It is the organised activity of several billions of neurons arranged in intricate networks, that underlies the sophisticated behaviour
of humans and other animals. However, as physics has long known, the nature of matter is determined by the interaction of its constituents. For neural networks, both biological and artificial, the interaction between neurons is mediated by synapses, and it is their evolution over time that allows sophisticated behaviour to be learned in the first place. 35
