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. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

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. 2018/19
N0
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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
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
Laboratory 2.0 14 36.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
1 2018-1 01/10/2018 30/09/2019 STOIANOV IVILIN PEEV (Presidente)
TESTOLIN ALBERTO (Membro Effettivo)
ZORZI MARCO (Membro Effettivo)

Syllabus
Prerequisites: The course requires basic knowledge of Neuropsychology, Neuroscience, advance math (linear algebra, mathematical analysis, statistics), Artificial Intelligence / Machine Learning, programming knowledge, and Matlab.
Target skills and knowledge: The course aims to develop the practical ability to analyze the functioning of the cognitive processes at the neural level through neurocomputational modeling. The students will also acquire theoretical knowledge of several popular computational models, namely, deep belief networks and reinforcement learning.
Examination methods: The assessment consists of implementing a neurocomputational modeling project aiming to investigate a specific cognitive process, documented in a 4-page report. The work is conducted in small groups at home. The reports should be delivered no later than one week before the exam and discussed during it.
Assessment criteria: The evaluation criteria include adequate understanding and ability to realize neurocomputational modeling analysis of cognitive processes.
Course unit contents: Content:

1) Fundamentals of neural computations and the neurocomputational modeling method (levels of analysis of a cognitive process; principles of neural computations; the neurocomputational modeling method; instruments)

2) Case 1: Modeling sensory functions with Deep Belief Networks and unsupervised learning. Part 1, Theoretical aspects, model implementation, and training on a sample dataset.

3) Modeling sensory functions. Part 2, Analysis of the model

4) Case 2: Modeling executive functions with reinforcement learning. Part 1, Theoretical aspects, implementation, and learning a sample behavior.

5) Modeling executive functions. Part 2, Analysis.
Planned learning activities and teaching methods: The course includes 5 lectures, each consisting of a theoretical introduction, practical programming and computational simulations, and discussion of homeworks. The students 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: * Matlab, crash cousce
* Neural computations
* Cognitive modelling (McClelland)
* Stoianov, Zorzi, Nature Neurosc
* Stoianov et al., Goal-directed behavior
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Interactive lecturing
  • Working in group
  • Questioning
  • Problem solving
  • Work-integrated learning
  • Students peer review

Innovative teaching methods: Software or applications used
  • Matlab

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