Spiking neural networks for neural signal decoding
External reference persons Alessio Carpegna, Paolo Viviani, Alberto Scionti
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 firstname.lastname@example.org, email@example.com or firstname.lastname@example.org specifying the thesis code and title.
Deadline 26/01/2024 PROPONI LA TUA CANDIDATURA