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Computational neuroscience I
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Computational neuroscience I
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Academic year 2025/2026
- Course ID
- NEU0278A
- Teachers
- Michele Caselle (Lecturer)
Andrea Signori (Lecturer) - Degree course
- [1301M22] Biotechnology for Neuroscience
- Year
- 2nd year
- Teaching period
- First semester
- Type
- Elective
- Credits/Recognition
- 2
- Course disciplinary sector (SSD)
- 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
Exposition of an oral presentation about a topic of choice inherent to the course program.
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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