OVERVIEW

Computational Neuroengineering is an advanced course offered to students of the master's degree in Computer Science aimed at providing the tools and methods for modeling the nervous system at different scales, from single neurons to complex neuronal networks

AIMS AND CONTENT

LEARNING OUTCOMES

Understanding the computational mechanisms of the human brain is one of the greatest challenges of this century. Together with the experimental part, a great effort has been provided thanks to large-scale simulations, as well as by the development of theoretical models at different scales of observation.This course is centered on the use of computational techniques to model biological neural networks, but also to understand the brain and its function through a variety of theoretical constructs and computer science analogies.

AIMS AND LEARNING OUTCOMES

Specifically, the emphasis is on neural information processing at “network level”, in developing quantitative models, as well as in formalizing new paradigms of computation and data representation. The course will provide insights about how the developing of in silico models, as well as of neuromorphic computational engines – based on the brain's circuitry – can contribute a better understand the coding strategies used by the “biological” brain to process incoming stimuli, and produce cognitive and/or motor outputs.

PREREQUISITES

Advanced knowledge of mathematics, mathematical analysis, physics; analysis of electrophysiological signals; signal processing

TEACHING METHODS

Combination of traditional lectures, classroom discussions.

SYLLABUS/CONTENT

Neuron models: i) Biophysical model of neurons: passive and Hodgkin and Huxley models; ii) Reduced neuron models: Integrate-and-fire (IF) and Izhikevich models
Synaptic transmission and plasticity: i) Phenomenological models; ii) Dynamical models; iii) Spike Timing Dependent Plasticity (STDP).
Network models: i) overview of different strategies (firing vs spiking) to model large-scale neuronal dynamics; ii) Meta-networks; iii) Balanced networks and syn-fire chains; iv) Role of the connectivity in the emerging dynamics; v) overview of the graph theory and metrics for characterizing a network; vi) different kind of connectivity; functional vs structural connectivity; vii) interplay between connectivity and dynamics.
Computational paradigms: i) Coding and decoding information; ii) Feed-forward and recurrent networks, lateral inhibition.
Multidimensional data processing and representation: i) The case study of early sensory systems: receptive fields, tuning curves, population activity, read-out mechanisms; ii) Efficient coding and reduction of dimensionality; iii) Optimal decoding methods.
Computational synthesis of brain information processing: models of “perceptual engines”, potentialities and design examples.

RECOMMENDED READING/BIBLIOGRAPHY

Slides available on Aulaweb.

• Methods in Neuronal Modeling, Koch and Segev, MIT press, 1999.
• Spiking Neuron Models, Gerstner and Kistler, Cambridge press, 2002.
• Computational Modeling Methods for Neuroscientists, De Schutter, MIT press, 2010.
• Theoretical Neuroscience, Dayan and Abbott, MIT press, 2001. 

TEACHERS AND EXAM BOARD

Exam Board

PAOLO MASSOBRIO (President)

SILVIO PAOLO SABATINI (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral exam about all the topics of the teaching with the addition of a journal club.

ASSESSMENT METHODS

Oral exam about the advanced techniques for modeling neuronal structures from single neuron up to large-scale neuronal networks.