| KEYWORD |
Spiking neural networks for neural signal decoding
Parole chiave INTELLIGENZA ARTIFICIALE, NEUROMORPHIC COMPUTING, RETI NEURALI SPIKING, ROBOTICA, SIGNAL PROCESSING
Riferimenti STEFANO DI CARLO, ALESSANDRO SAVINO
Riferimenti esterni Alessio Carpegna, Paolo Viviani, Alberto Scionti
Gruppi di ricerca DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci
Tipo tesi RICERCA SPERIMENTALE
Descrizione The work fits in the context of the B-Cratos H2020-FET project (https://www.b-cratos.eu), where a machine learning-based methodology is being developed to translate electric signals recorded by brain-implanted electrodes, into meaningful commands for a robotic hand, while tactile feedback from an electronic skin is sent back to the brain to provide sensory stimulation.
This thesis aims to investigate the suitability of spiking neural networks for classification of neural signals recorded by microelectrodes. The work will involve the analysis of datasets both coming from within the project and from literature, and the implementation of a spiking neural network for classification of recorded signals.
The duration of the thesis work is expected to be around 9 months, adjustable based on the specific needs and skills.
Conoscenze richieste MS students in for Computer Engineering; Experience with at least one between Python and C/C++
Valuable skills: knowledge of Deep Learning algorithms and data science frameworks (Keras, Pytorch, Pandas)
Note Send CV to paolo.viviani@linksfoundation.com, alberto.scionti@linksfoundation.com or stefano.dicarlo@polito.it specifying the thesis code and title.
Scadenza validita proposta 26/01/2024
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