Carlo Ricciardi
Full Professor of Experimental Physics at Politecnico di Torino
IAS SEMINAR #18/2026
Turin / OGR / Mezzanino / October 21, 2026 / 04:00 – 06:00 pm CEST
Physical Computing with Self-Organizing Memristive Networks
EVENT&WEBINAR
Unlike artificial neural networks, which are largely built upon abstract mathematical models, physical neural networks exploit the intrinsic dynamics and physical laws of their underlying hardware to perform computation. A central question is therefore what computational capabilities can emerge from these physical systems, and under which conditions their brain-inspired properties can provide an actual advantage over more conventional computing approaches.
The seminar will address this question through self-organizing memristive networks, discussing their distinctive brain-like properties, including complex spatiotemporal dynamics, criticality, and emergent behavior, as well as recent results on their use for physical reservoir and in-materia computing.
The topic naturally connects different fields. From a fundamental perspective, physical neural networks provide physicists and AI researchers with brain-inspired substrates on which computational models and learning paradigms can be implemented and explored. From a technological perspective, they offer a route toward neuromorphic devices that could complement conventional CMOS hardware, particularly for edge AI and dedicated AI computing architectures.
CARLO RICCIARDI
Carlo Ricciardi is Full Professor of Experimental Physics at Politecnico di Torino, where he leads the NaMeS research group (https://names.polito.it). His research focuses on bio-inspired nanodevices and unconventional computing, with particular emphasis on memristive devices, self-organizing nanowire networks, neuromorphic and in-materia computing. He has authored more than 150 peer-reviewed publications and has been a Visiting Scientist at UC Berkeley and a Visiting Professor at EPFL.
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