| First cycle degree courses | Second cycle degree courses | Single cycle degree courses |
| School of Psychology | ||
| COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY | ||
Course unit
COMPUTATIONAL NEUROSCIENCE
PSP7078038, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
COMPUTATIONAL NEUROSCIENCE
PSP7078038, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2018/19
Information on the course unit
| Degree course |
Second cycle degree in COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY PS1932, Degree course structure A.Y. 2017/18, A.Y. 2019/20 |
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|---|---|---|
| Number of ECTS credits allocated | 2.0 | |
| Type of assessment | Evaluation | |
| Course unit English denomination | COMPUTATIONAL NEUROSCIENCE | |
| Department of reference | Department of General Psychology | |
| E-Learning website | https://elearning.unipd.it/scuolapsicologia/course/view.php?idnumber=2019-PS1932-000ZZ-2018-PSP7078038-N0 | |
| Mandatory attendance | No | |
| Language of instruction | English | |
| Branch | PADOVA | |
| Single Course unit | The Course unit CANNOT be attended under the option Single Course unit attendance | |
| Optional Course unit | The Course unit is available ONLY for students enrolled in COGNITIVE NEUROSCIENCE AND CLINICAL NEUROPSYCHOLOGY |
Lecturers
| Teacher in charge | IVILIN PEEV STOIANOV |
|---|
ECTS: details
| Type | Scientific-Disciplinary Sector | Credits allocated | |
|---|---|---|---|
| Other | -- | -- | 2.0 |
Course unit organization
| Period | First semester |
|---|---|
| Year | 2nd Year |
| Teaching method | frontal |
| Type of hours | Credits | Teaching hours |
Hours of Individual study |
Shifts |
|---|---|---|---|---|
| Lecture | 2.0 | 14 | 36.0 | No turn |
| Start of activities | 07/10/2019 |
|---|---|
| End of activities | 18/01/2020 |
| Show course schedule | 2019/20 Reg.2017 course timetable |
Examination board
Examination board not defined
| Prerequisites: | The course requires basic knowledge in Neuropsychology, Neuroscience, advance math (Linear Algebra, Calculus, Probability Theory), Artificial Intelligence and Machine Learning, programming knowledge, including experience with Matlab. |
| Target skills and knowledge: | The course aims to develop the practical ability to investigate the principles of the functioning of the nervous system at neuron-, function- and system-level and the cognitive processes it supports through practical neurocomputational modeling work. The students will also acquire knowledge of several fundamental models, including Deep Networks and Reinforcement Learning. |
| Examination methods: | The assessment consists of implementing a neurocomputational project aiming to investigate a specific cognitive process, documented in a short report. The work will be conducted in small groups at home. The reports should be delivered and discussed by the end of the exam session. |
| Assessment criteria: | The evaluation criteria include adequate understanding and ability to perform and interpret a neurocomputational modeling analysis of cognitive processes. |
| Course unit contents: | 1) Fundamentals of neural coding, neural computations, and the neurocomputational modeling method for analysis of neural processes.
2-3) Introduction to scientific programming in Matlab. 4-5) Modeling sensory functions with unsupervised learning and Deep Belief Networks. Theoretical introduction, implementation, learning sample data, analysis. 6-7) [optional] Modeling executive functions with reinforcement learning. Introduction, implementation, learning sample behavior, analysis. |
| Planned learning activities and teaching methods: | The course includes 7 lectures, 2 hours each. The lectures include theoretical introduction, practical programming and computational simulations, discussion of homeworks. The students will work in small groups. The simulations will be done in the Matlab integrated development environment for scientific computing and include programming activity. |
| Additional notes about suggested reading: | Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Peter Dayan and Larry Abbott (MIT Press).
- online matlab cources: https://matlabacademy.mathworks.com/ (follow "Matlab fundamentals" and focus on coding data in arrays, structures, indexing in arrays, array operations, graphical visualization) - teaching material will be provided on the web page https://github.com/stoianov/CN2019 - Sample scientific papers that use the neurocomputational modelling method for analysis of neural processes: Stoianov, I., & Zorzi, M. (2012). Emergence of a “visual number sense” in hierarchical generative models. Nature Neuroscience, 15(2), 194–196. doi: 10.1038/nn.2996. Testolin, A., Stoianov, I., De Filippo De Grazia, M., & Zorzi, M. (2013). Deep unsupervised learning on a desktop PC? A primer for cognitive scientists. Frontiers in Psychology, 4, 251. Zorzi, M., Testolin, A., & Stoianov, I. (2013). Modeling language and cognition with deep unsupervised learning: A tutorial overview. Frontiers in Psychology, 4(August), 515. doi: 10.3389/fpsyg.2013.00515. |
| Textbooks (and optional supplementary readings) |
|
- Laboratory
- Problem based learning
- Case study
- Interactive lecturing
- Working in group
- Loading of files and pages (web pages, Moodle, ...)
- Students peer review
