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B031373 - COMPUTATIONAL METHODS FOR BIOENGINEERING
Main information
Teaching Language
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Sustainable Development Goals 2030
Academic Year 2021-22
Coorte 2021 - Second Cycle Degree in INFORMATICS ENGINEERING
Course year
First year - Second Semester
Belonging Department
Information Engineering (DINFO)
Course Type
Single education field course
Scientific Area
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Credits
6
Teaching Hours
48
Teaching Term
28/02/2022 ⇒ 17/06/2022
Attendance required
No
Type of Evaluation
Final Grade
Course program
show
Lectureship
Mutuality
Course teached as:
B026343 - BIOINGEGNERIA PER LE NEUROSCIENZE
Second Cycle Degree in INGEGNERIA BIOMEDICA
B026343 - BIOINGEGNERIA PER LE NEUROSCIENZE
Second Cycle Degree in INGEGNERIA BIOMEDICA
Teaching Language
Italian
Suggested readings (Search our library's catalogue)
Jian Feng - Computational Neuroscience A Comprehensive Approach (2003, Chapman & Hall)
Laura Astolfi - Estimation of Cortical Connectivity in Humans Advanced Signal Processing Techniques;
Karim G. Oweiss - Statistical Signal Processing for Neuroscience and Neurotechnology (2010, Academic Press)
Cerutti, Marchesi - Advanced Methods of Biomedical Signal Processing (IEEE Press Series on Biomedical Engineering) (2011, Wiley-IEEE Press)
Laura Astolfi - Estimation of Cortical Connectivity in Humans Advanced Signal Processing Techniques;
Karim G. Oweiss - Statistical Signal Processing for Neuroscience and Neurotechnology (2010, Academic Press)
Cerutti, Marchesi - Advanced Methods of Biomedical Signal Processing (IEEE Press Series on Biomedical Engineering) (2011, Wiley-IEEE Press)
Learning Objectives
The course aims to introduce students to the main technologies
bioengineering to support the study of Neuroscience:
Neuronal models
Nonlinear systems
Spiketrains
Artificial intelligence based on neural networks
bioengineering to support the study of Neuroscience:
Neuronal models
Nonlinear systems
Spiketrains
Artificial intelligence based on neural networks
Prerequisites
Statistics;
Signal processing;
Physiology of Nervous system;
Signal processing;
Physiology of Nervous system;
Teaching Methods
The course will be conducted mainly using lecture slides and classroom exercises.
Type of Assessment
The verification of learning of the skills acquired by the student during the course
will focus on investigating the following:
- the ability to analyze the engineering aspects of mathematical models for Neuroscience
- the ability to perform a literature search on the state of the art
related to the specific problem under study
- the ability to implement a chain of analysis for neural areas and networks
- Develop a critical spirit such that they can deal with a
wide range of problems in the head of computational models for Neuroscience
will focus on investigating the following:
- the ability to analyze the engineering aspects of mathematical models for Neuroscience
- the ability to perform a literature search on the state of the art
related to the specific problem under study
- the ability to implement a chain of analysis for neural areas and networks
- Develop a critical spirit such that they can deal with a
wide range of problems in the head of computational models for Neuroscience
Course program
General aspects and links by dataset;
Recalls Aleatory Variables;
Recalls Stochastic processes and PCA - ICA;
Nervous System (SN) Recalls;
Neuronal Dynamics;
Information Theory;
Structure and functions of the SNS;
Analysis methods for continuous neuronal data and Spkitrain;
From stimuli to neural response;
Single Neuron Model;
Neural Network Models;
Learning and Plasticity;
Spiketrain Analysis;
Nonlinear Systems;
Volterra Series;
Wiener Series;
Poisson-Wiener Series;
Autonomic Nervous System;
Electrodermal System ;
System
Eyetracking;
Nonlinear analysis methods for time series of physiological data;
Point process for Spiketrains analysis;
Causality;
Connectivity;
Dynamical Causal Modelling ;
Introduction to CNN-Deep Learning-Reinforcement Learning-Continual Learning-Contrast Learning;
Recalls Aleatory Variables;
Recalls Stochastic processes and PCA - ICA;
Nervous System (SN) Recalls;
Neuronal Dynamics;
Information Theory;
Structure and functions of the SNS;
Analysis methods for continuous neuronal data and Spkitrain;
From stimuli to neural response;
Single Neuron Model;
Neural Network Models;
Learning and Plasticity;
Spiketrain Analysis;
Nonlinear Systems;
Volterra Series;
Wiener Series;
Poisson-Wiener Series;
Autonomic Nervous System;
Electrodermal System ;
System
Eyetracking;
Nonlinear analysis methods for time series of physiological data;
Point process for Spiketrains analysis;
Causality;
Connectivity;
Dynamical Causal Modelling ;
Introduction to CNN-Deep Learning-Reinforcement Learning-Continual Learning-Contrast Learning;
Sustainable Development Goals 2030
3, 4