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)
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
Prerequisites
Statistics;
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
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;