First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
Course unit
SCP8082718, 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
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL NEUROSCIENCE
Website of the academic structure
Department of reference Department of Physics and Astronomy
Mandatory attendance No
Language of instruction English
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

Teacher in charge ALBERTO TESTOLIN ING-INF/03

Course unit code Course unit name Teacher in charge Degree course code

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 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 48 102.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2018 course timetable

Examination board
Board From To Members of the board
1 A.A. 2019/2020 01/10/2019 15/03/2021 TESTOLIN ALBERTO (Presidente)

Prerequisites: The course relies on preliminary knowledge of mathematical analysis, linear algebra and probability theory. Familiarity with machine learning concepts is desired, though not mandatory. Python programming skills are required.
Target skills and knowledge: The course covers the theory and practice of artificial neural networks, highlighting their relevance both for artificial intelligence applications and for modeling human cognition and brain function. Theoretical discussion of various types of neural networks and learning algorithms is complemented by hands-on practices in the computer lab (PyTorch framework).
Examination methods: Evaluation of knowledge and abilities acquired will consist on an individual project assignment, which will be discussed during the oral exam. The project will require 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 results. The oral exam will also include general theoretical questions related to the course content.
Assessment criteria: The evaluation is based on the understanding of course topics and the acquisition of the proposed concepts and methodologies.
Course unit contents: 1. Introduction: computational and mathematical modeling of neural systems; basics of neuroscience; levels of analysis in system neuroscience.
2. Single-neuron modeling: morphology, neuro-electronics, principles of synaptic transmission; integrate-and-fire models; the Hodgkin-Huxley model.
3. Principles of neural encoding: recording neuronal responses; spike trains, firing rates, local field potentials; tuning functions and receptive fields; efficient encoding principles and information compression.
4. Network modeling: neural network architectures; localistic, distributed, and sparse representations; examples from the visual system.
5. Learning, memory and plasticity: synaptic plasticity in biological systems (Hebb rule, LTP, LTD, STDP); synaptic plasticity in artificial neural networks and overview of machine learning basics.
6. Supervised learning: perceptron, delta rule, error backpropagation.
7. Supervised deep learning: advanced optimization methods for training multi-layer networks; convolutional architectures; transfer learning and multi-task learning.
8. Recurrent neural networks: backpropagation through time, long short-term memory networks.
9. Unsupervised learning: competitive networks; self-organizing maps; associative memories and Hopfield networks; autoencoders and Boltzmann machines.
10. Unsupervised deep learning: hierarchical generative models; generative adversarial networks.
11. Reinforcement learning: exploration-exploitation dilemma; temporal-difference learning; conditioning and dopamine circuits; deep reinforcement learning.
12. Case studies from neurocognitive modeling: visual perception; space coding; semantic cognition; complementary learning systems; hippocampus and experience replay.
13. Large-scale brain organization: structural and functional properties of brain networks; neuronal oscillations and spontaneous brain activity; neuromorphic hardware.
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 critically reflect on the concepts discussed.
Additional notes about suggested reading: All topics will be covered during the lectures. Slides will be made available on e-learning. Students' notes must be integrated with the reference books and with further material (mostly scientific articles) provided by the teacher on the e-learning platform.
Textbooks (and optional supplementary readings)
  • Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning. --: MIT Press, 2016. Electronic version freely available online Cerca nel catalogo
  • Dayan, P., and L. F. Abbott, Theoretical neuroscience. --: MIT Press, 2001. Electronic version freely available online Cerca nel catalogo
  • Hertz, J., Krogh, A., and Palmer, R. G., Introduction To The Theory Of Neural Computation. --: Westview Press, 1991. Cerca nel catalogo