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bionics-engineering:computational-neuroscience:cns2016

Applied Brain Science - Computational Neuroscience (CNS)

Lectures and Materials for Academic Year 2015/16

Date Room Topic References & Additional Material
1 29/2/16 (11.30-13.30) SI 3 Introduction to the course Lecture 1
2 02/3/16 (14.30-17.30) C44 Neural Modeling Lecture 2
3 07/3/16 (11.30-13.30) SI 3 Lab1 Implementing Spiking Neurons using Izhikevich's Model
4 09/3/16 (15.30-18.30) SI 3 Neuron-Astrocyte models Lecture 3
5 14/3/16 (11.30-13.30) SI 3 Lab2 Implementing a Spiking Neural Network
6 16/3/16 (15.30-18.30) SI 3 In-vitro Models Lecture 4: Statistics for In-vistro neuro-astrocyte culture
7 21/3/16 (11.30-13.30) SI 3 Lab3 Implementing a Spiking Neuron-Astrocyte Network
8 04/04/16 (11.30-13.30) SI 3 Computation of Touch and Vision sensory input Lecture 5
9 06/04/16 (15.30-18.30) SI 3 Introduction to Unsupervised and Representation Learning Lecture 6
References:
[DAYAN] Sect. 8.1-8.3
[PANINSKI] Sect 19.1, 19.2.1, 19.3.1, 19.3.2
10 11/04/16 (11.30-13.30) SI 3 Associative Memories I - Hopfield Networks Lecture 7
References:
[DAYAN] Sect. 7.4 (Associative Memory part)
[PANINSKI] Sect. 17.1, 17.2
11 13/04/16 (15.30-18.30) SI 3 Lab 4 Hebbian learning and Hopfield networks (Assignment 4)
12 18/04/16 (11.30-13.30) SI 3 Associative Memories II - Stochastic networks and Boltzmann machines Lecture 8
References:
[DAYAN] Sect. 7.6

Further readings:
[1] A clean and clear introduction to RBM
13 20/04/16 (15.30-18.30) SI 3 Lab 5 Boltzmann machines (Assignment 5)
25/04/16 (11.30-13.30) SI 3 No class due to Italian national holiday
27/04/16 (15.30-18.30) SI 3 No class
14 02/05/16 (11.30-13.30) SI 3 Representation learning and deep learning models Lecture 9
References:
[DAYAN] Sect. 10.1

Further Readings:
[2] A classic divulgative paper from the initiator of Deep Learning
[3] Recent review paper
[4] A freely available book on deep learning from Microsoft RC
15 04/05/16 (15.30-18.30) SI 3 Lecture: Adaptive Resonance Theory (ART)
Lab 6
Lecture 10
Deep RBM (Optional Assignment 6)

Futher Readings:
A gentle introduction to ART networks (with coding examples) can be found here
16 09/05/16 (11.30-13.30) SI 3 Introduction to RNN: tasks and basic models Lecture and info multifiles
17 11/05/16 (15.30-18.30) SI 3 Introduction to RNN: properties and taxonomy; intro to learning by BPTT Lecture and info multifiles (also RNN learning)
18 16/05/16 (11.30-13.30) SI 3 Introduction to RNN: learning by RTRL Lecture and info multifiles (RNN learning) plus blackboard notes
19 18/05/16 (15.30-18.30) SI 3 Introduction to RNN: LAB 1 - learning with IDNN and RNN Info and assignment multifiles (see "RNN - Lab1" section)
20 23/05/16 (11.30-13.30) SI 3 Introduction to RNN: Reservoir Computing Lecture and info multifiles (ESN)
21 25/05/16 (15.30-18.30) SI 3 Introduction to RNN: LAB 2 - learning with ESN Info and assignment multifiles (see "RNN - Lab2" section). NEW: See also the new "Upgrade" section for further clarification
bionics-engineering/computational-neuroscience/cns2016.txt · Ultima modifica: 21/02/2017 alle 16:25 (20 mesi fa) da Davide Bacciu