Matteo Cartiglia
Position:
PhD Student
Email:
camatteo@ini.uzh.ch
Work phone:
I am a PhD student at INI interested in two main lines of research. Firstly I am developing spiking neural network models that can be trained using biologically plausible
learning rules instead of the state-of-art back-propagation algorithm. Secondly, I am interested in interfacing event-based computation with biological neurons by creating a new class of recording devices.
Supervisor
Giacomo Indiveri
Publications
2023
Arianna Rubino, Matteo Cartiglia, Melika Payvand, Giacomo Indiveri
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
,
2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)
, 2023
2022
Matteo Cartiglia, Arianna Rubino, Shyam Narayanan, Charlotte Frenkel, Germain Haessig, Giacomo Indiveri, Melika Payvand
Stochastic dendrites enable online learning in mixed-signal neuromorphic processing systems
,
2022 IEEE International Symposium on Circuits and Systems (ISCAS)
, 2022
2021
Dennler, Nik and Haessig, Germain and Cartiglia, Matteo and Indiveri, Giacomo
Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks
,
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
1-4, 2021
2020
Cartiglia, M; Haessig, G; Indiveri G
An error-propagation spiking neural network compatible with neuromorphic processors
,
2020 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
, 2020
pdf
2018
Cartiglia, M.; Kreiser, R. & Sandamirskaya, Y.
A Neuromorphic approach to path integration: a head direction spiking neural network with visually-driven reset
,
IEEE Symposium for Circuits and Systems, ISCAS
, 2018
pdf
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