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Computational neuroscience I

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Computational neuroscience I

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Academic year 2023/2024

Course ID
NEU0278A
Teachers
Michele Caselle (Lecturer)
Andrea Signori (Lecturer)
Year
2nd year
Teaching period
First semester
Type
Elective
Credits/Recognition
2
Course disciplinary sector (SSD)
FIS/02 - theoretical physics, mathematical models and methods
Delivery
Formal authority
Language
English
Attendance
Optional
Type of examination
Oral
Type of learning unit
modulo
Modular course
Computational neuroscience (NEU0278)
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Sommario del corso

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Course objectives

The goal of this part of the course will be to offer a general  introduction to Computational Neuroscience, with a particular attention to the use and application of tools typical of Statistical Mechanics and Theoretical Physics to Neuroscience.

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Results of learning outcomes

Knowledge and Understanding:

At the end of course the student will reach a good knowledge of the most advanced results in Network Theory and in inference methods applied to Computational Neuroscience.


Applying Knowledge and Understanding:

The knowledge acquired during the lectures will be applied to the solution of simple problems in network theory and bayesian statistics

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Program

 

The course deals with the following items:

1) Introduction to network theory. In particular we shall discuss:

- General properties of networks: degree distribution, heterogeneous versus homogeneus networks, definitions of distance, density, clustering and assortativity of networks.

- Community detection in networks

- Introduction to the Erdos-Renyi model

- Introduction to the Barabasi model.

2) Introduction to statistical mechanics with applications to information theory:

- Shannon entropy

- KL divegence and mutual information

- Minimum description Length

3) Introduction to Artificial Neural Netwoks,

- Mutilayer Perceptrons: properties, learning rules and training procedure

- Introduction to the Hopfield Model: Hebb’s rule, associative memories

 

 

 

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Course delivery

In presence                                                

                                                                                                                                                   

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Learning assessment methods

Oral exam                                                        

                                                                                                     

                                  

Suggested readings and bibliography

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.

Network Science by Albert Laszlo Barabasi,

 

Notes and articles provided by the teacher during the course

 

 



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