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Spiking neural networks for neural signal decoding

keywords ARTIFICIAL INTELLIGENCE, NEUROMORPHIC COMPUTING, ROBOTICS, SIGNAL PROCESSING, SPIKING NEURAL NETWORKS

Reference persons STEFANO DI CARLO, ALESSANDRO SAVINO

External reference persons Alessio Carpegna, Paolo Viviani, Alberto Scionti

Research Groups DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci

Thesis type EXPERIMENTAL RESEARCH

Description 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.

Required skills 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)

Notes Send CV to paolo.viviani@linksfoundation.com, alberto.scionti@linksfoundation.com or stefano.dicarlo@polito.it specifying the thesis code and title.


Deadline 26/01/2024      PROPONI LA TUA CANDIDATURA




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