Thesis Project Form
Title (tentative): Optimizing SNN parameters for biomimetic stimulation of in vivo neural networks| Thesis advisor(s): Chiappalone Michela, Federico Barban | E-mail: |
| Address: Via Opera Pia 13, 16145 Genova | Phone: |
Description
Motivation and application domain
Impairment from stroke is one of the most common causes of adult disability. Rehabilitation from these sensorimotor deficits relies on re-establishing functional connectivity, including sensorimotor integration [1]. Electroceutical is an emerging field that exploits the modulation of brain signals to treat specific symptoms and it has been proposed as a rehabilitation therapy to restore lost capabilities after stroke.
The student will explore the effects of a novel kind of neurostimulation driven by an FPGA-based Spiking Neural Network (SNN) on in vivo animal models (rats).
The student will explore the effects of a novel kind of neurostimulation driven by an FPGA-based Spiking Neural Network (SNN) on in vivo animal models (rats).
General objectives and main activities
The SNN [2] is used to generate the spike train, which will trigger the stimulation intra-cortically delivered to the anesthetized rats.
The main objective of the thesis is to develop tools and methods to evaluate the activity of both the biological neural network (BNN, resulting from the in vivo recordings on adult anesthetized rats) and the artificial neural network (FPGA-based SNN).
The differences between pre and post stimulation need to be evaluated and the intra-stimulation ongoing activity must be analyzed. Then, the student will develop an algorithm able to tune the activity of the SNN in order to better mimic the one of the BNN.
[1] Averna et al. Cerebral Cortex, November 2021; [2] Beaubois et al. bioRxiv, September 2023
The main objective of the thesis is to develop tools and methods to evaluate the activity of both the biological neural network (BNN, resulting from the in vivo recordings on adult anesthetized rats) and the artificial neural network (FPGA-based SNN).
The differences between pre and post stimulation need to be evaluated and the intra-stimulation ongoing activity must be analyzed. Then, the student will develop an algorithm able to tune the activity of the SNN in order to better mimic the one of the BNN.
[1] Averna et al. Cerebral Cortex, November 2021; [2] Beaubois et al. bioRxiv, September 2023
Training Objectives (technical/analytical tools, experimental methodologies)
The student will learn:
- To develop analytical tools using Matlab and Python.
- To design and develop machine learning algorithms.
- To develop analytical tools using Matlab and Python.
- To design and develop machine learning algorithms.
Place(s) where the thesis work will be carried out: DIBRIS and LiSTechLab (joint Lab UNIGE-San Martino)
Additional information
Pre-requisite abilities/skills: Programming skills, signal processing, statistics, attitude to computational work
Curriculum: Neuroengineering
Maximum number of students: 1