Master of Science in Bionics Engineering

Instructors: Alessio Micheli (email) - Davide Bacciu (email)

Additional web page: http://www.di.unipi.it/~micheli/DID/CNS.htm

(21/02/2017) Course Didawiki updated with course information and first lesson for academic year 2016/17.

Note for Computer Science Students

In academic year 2016/2017, “Machine Learning: neural networks and advanced models” (AA2) (Master programme in Computer Science - Corso di Laurea Magistrale in Informatica) is borrowed from CNS.

Weekly Schedule

The course is held on the second term. The preliminary schedule for A.A. 2016/17 is provided in table below.

First lecture: Wednsday 01/03/2017

Objectives

The content of the Computational Neuroscience course includes:

• bio-inspired neural modelling, spiking and reservoir computing neural networks;
• advanced computational neural models for learning;
• architectures and learning methods for dynamical/recurrent neural networks for temporal data and the analysis of their properties;
• the role of computational neuroscience in real-world applications (by case studies).

Textbook and Teaching Materials

The official textbooks of the course are the following:

[IZHI] E.M. Izhikevich
Dynamical Systems in Neuroscience:  The Geometry of Excitability and Bursting
The MIT press, 2007

[DAYAN] P. Dayan and L.F. Abbott
Theoretical Neuroscience
The MIT press, 2001

[NN] Simon O. Haykin
Neural Networks and Learning Machines
(3rd Edition), Prentice Hall, 2009

Additionally:

for the part of the course on bio-inspired neural modelling, it is also useful the book freely available online:

[GERSTNER] W. Gerstner and W.M. Kistler, Spiking Neuron Models: Single
Neurons, Population, Plasticity. Cambridge University Press, 2002

for the second module of the course (Unsupervised and Representation Learning), it will be referenced material from a book freely available online:

[PANINSKI] W. Gerstner, W.M. Kistler, R. Naud and L. Paninski 
Neuronal Dynamics: From single neurons to networks and models of cognition
Cambridge University Press, 2014

Statistics for In-vitro neuro-astrocyte culture

Academic Year 2015/2016

[1] Geoffrey Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Technical Report 2010-003, Department of Computer Science, University of Toronto, 2010

[2] G.E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks.Science 313.5786 (2006): 504-507.

3] Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 35(8) (2013): 1798-1828.

[4] L. Deng and D. Yu. Deep Learning Methods and Applications, 2014

[5] W. Maass, Liquid state machines: motivation, theory, and applications. Computability in context: computation and logic in the real world (2010): 275-296.

See other references in the slide notes.