
Course unit
COMPUTATIONAL NEUROSCIENCE
INP7080712, A.A. 2018/19
Information concerning the students who enrolled in A.Y. 2017/18
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Educational activities in elective or integrative disciplines 
INGINF/05 
Data Processing Systems 
6.0 
Course unit organization
Period 
First semester 
Year 
2nd Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
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)

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 handson 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. Singleneuron modeling: integrateandfire models; the HodgkinHuxley model; multicompartment models.
4. Network modeling: neural network architectures (feedforward / recurrent); network dynamics (linear / nonlinear, 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; multilayer networks and error backpropagation.
7. Partially recurrent neural networks: backpropagation through time, predictive coding.
8. Unsupervised learning: clustering and dimensionality reduction; competitive networks and selforganizing maps; associative memories and Hopfield networks; Boltzmann machines.
9. Deep learning: hierarchical models; topdown processing; convolutional neural networks.
10. Reinforcement learning: explorationexploitation; TemporalDifference (TD) learning, conditioning and dopamine circuits.
11. Case studies from neurocognitive modeling.
12. Largescale 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 thinkpairshare 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.

O'Reilly, R. C., and Y. Munakata MIT press,, Computational explorations in cognitive neuroscience. : MIT Press, 2000.

Hertz, J., Krogh, A., and Palmer, R. G., Introduction To The Theory Of Neural Computation. : Westview Press, 1991.

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)

