First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Psychology
Course unit
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
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
Mandatory attendance No
Language of instruction English
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

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 of
Individual study
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
Board From To Members of the board
2 2019 01/10/2019 30/09/2020 STOIANOV IVILIN PEEV (Presidente)
TESTOLIN ALBERTO (Membro Effettivo)
ZORZI MARCO (Membro Effettivo)

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: (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

- 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)

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Loading of files and pages (web pages, Moodle, ...)
  • Students peer review

Innovative teaching methods: Software or applications used
  • Matlab

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