Fernando CORINTO – Projects

Professor of Electrical Engineering
Dept. of Electronics and Telecommunications
Politecnico di Torino
10129 Torino (Italy)
Ph.: +39 011 564 4199
Fax: +39 011 564 4099
E-mail: fernando.corinto@polito.it

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Memristor Spiking Neural Networks with Oscillatory Neurons and Event-Driven Synaptic Crossbars for Neuromorphic Vision Recognition

(within the frame of the 12th Executive Programme of Scientific and Technological Cooperation for the years 2019 – 2021)

Spiking Neural Networks (SNNs) are inspired from natural cognitive computing that actually happens in human brain, where neurons generate spikes collectively in response to the sensory inputs. Based on this spiking event-driven processing for brain-like cognition, SNNs open up new horizons for developing neural and synaptic models with an exponential capacity that may be beyond the current cognitive capacity of the state-of-the-art deep learning.

The project, carried out in collaboration with Prof. K.S. Min (Kookmin University, Seoul, Korea), aims to develop new architectures and learning algorithm of memristor spiking neural networks exploiting oscillatory memristor neurons and synaptic memristor crossbars. The target application of the memristor spiking neural networks developed in this project is neuromorphic vision sensor, where the image information is received as a series of asynchronous events for achieving better energy efficiency and finer temporal resolution than the conventional framed-based system.

[1] Gianluca Zoppo, Francesco Marrone, Fernando Corinto, “A Continuous-time Learning Rule for Memristor–based Recurrent Neural Networks”, atti della conferenza ICECS 2019, Genova, Italia – 27-29 Nov. 2019, DOI: 10.1109/ICECS46596.2019.8964918

[2] Gianluca Zoppo, Francesco Marrone, Fernando Corinto, “Equilibrium Propagation for Memristor-based Recurrent Neural Networks”, Frontiers in Neuroscience-Neuromorphic Engineering – URL=https://www.frontiersin.org/article/10.3389/fnins.2020.00240, DOI=10.3389/fnins.2020.00240, ISSN=1662-453X

This work is supported by the Ministero degli Affari Esteri e della Cooperazione Internazionale (MAECI) under the project n. PGR00823.