First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
ICT FOR INTERNET AND MULTIMEDIA
Course unit
COMPUTATIONAL NEUROSCIENCE
INP7080712, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
ICT FOR INTERNET AND MULTIMEDIA
IN2371, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Degree course track ICT FOR LIFE AND HEALTH [004PD]
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL NEUROSCIENCE
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN2371-004PD-2018-INP7080712-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ALBERTO TESTOLIN 000000000000
Other lecturers MATTEO GADALETA ING-INF/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7080712 COMPUTATIONAL NEUROSCIENCE ALBERTO TESTOLIN IN2371
INP7080712 COMPUTATIONAL NEUROSCIENCE ALBERTO TESTOLIN IN2371
INP7080712 COMPUTATIONAL NEUROSCIENCE ALBERTO TESTOLIN IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines ING-INF/05 Data Processing Systems 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 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 A.A. 2018/2019 01/10/2018 15/03/2020 TESTOLIN ALBERTO (Presidente)
GADALETA MATTEO (Membro Effettivo)
BADIA LEONARDO (Supplente)
ERSEGHE TOMASO (Supplente)
ZANELLA ANDREA (Supplente)
ZORZI MICHELE (Supplente)

Syllabus
Prerequisites: The course relies on preliminary knowledge of mathematical analysis, linear algebra and probability theory. Basic notions of biology and neuroscience will also help. Basic programming skills are required.
Target skills and knowledge: The course covers the theory and practice of computational modeling of brain function, in particular focusing on artificial neural network models. The theoretical discussion of various types of neural networks and learning algorithms is followed by examples of applications in cognitive neuroscience, and complemented by hands-on practice in the computer lab.
Examination methods: Evaluation of knowledge and abilities acquired will consist on a project assignment, which will be discussed during the oral exam. The project will consist on a software implementation of one or more computational models and analyses discussed during the course, along with a short essay in which the student will describe and discuss the project implementation.
Assessment criteria: The evaluation is based on the understanding of course topics and the acquisition of the proposed concepts and methodologies.
Course unit contents: The course will cover the following topics:
1. Introduction: computational and mathematical modeling of neural systems; levels of analysis in system neuroscience.
2. Principles of neural encoding: recording neuronal responses; spike trains, firing rates, local field potentials; tuning functions and receptive fields; efficient encoding principle and information compression.
3. Single-neuron modeling: integrate-and-fire models; the Hodgkin-Huxley model; multi-compartment models.
4. Network modeling: neural network architectures (feed-forward / recurrent); network dynamics (linear / non-linear, deterministic / stochastic), network representations (localistic / distributed / sparse coding).
5. Learning, memory and plasticity: synaptic plasticity rules (Hebb rule, LTP, LTD, STDP); basic concepts in machine learning and artificial neural networks.
6. Supervised learning: perceptron, delta rule; multi-layer networks and error backpropagation.
7. Partially recurrent neural networks: backpropagation through time, predictive coding.
8. Unsupervised learning: clustering and dimensionality reduction; competitive networks and self-organizing maps; associative memories and Hopfield networks; Boltzmann machines.
9. Deep learning: hierarchical models; top-down processing; convolutional neural networks.
10. Reinforcement learning: exploration-exploitation; Temporal-Difference (TD) learning, conditioning and dopamine circuits.
11. Case studies from neurocognitive modeling.
12. Large-scale brain organization: structural and functional properties of brain networks, oscillations and spontaneous brain activity; cognitive architectures.
Planned learning activities and teaching methods: Teaching is based on frontal lectures covering the theory and practice classes on neural network modeling in the computer lab. Interactive teaching techniques will be used, including think-pair-share and interactive discussions of a few minutes on open questions. This will enforce interactive learning and the ability to reflect on things.
Additional notes about suggested reading: All topics will be covered during the lectures. Slides will be made available on elearning. Students' notes must be integrated with the reference books and with further material (mostly scientific articles) provided by the teacher on the elearning platform.
Textbooks (and optional supplementary readings)
  • Dayan, P., and L. F. Abbott, Theoretical neuroscience. --: MIT Press, 2001. Cerca nel catalogo
  • O'Reilly, R. C., and Y. Munakata MIT press,, Computational explorations in cognitive neuroscience. --: MIT Press, 2000. Cerca nel catalogo
  • Hertz, J., Krogh, A., and Palmer, R. G., Introduction To The Theory Of Neural Computation. --: Westview Press, 1991. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Working in group
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab
  • implementation examples

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure