COMPUTATIONAL NEUROENGINEERING

COMPUTATIONAL NEUROENGINEERING

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iten
Codice
90533
ANNO ACCADEMICO
2019/2020
CFU
6 cfu al 1° anno di 10852 COMPUTER SCIENCE (LM-18) GENOVA
SETTORE SCIENTIFICO DISCIPLINARE
ING-INF/06
LINGUA
Inglese
SEDE
GENOVA (COMPUTER SCIENCE )
periodo
2° Semestre
materiale didattico

OBIETTIVI E CONTENUTI

OBIETTIVI FORMATIVI

Learning computational techniques for the modeling of biological neural networks and understanding the brain and its function through a variety of theoretical constructs and computer science analogies.

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

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.

Modalità didattiche

Lectures and practicals

PROGRAMMA/CONTENUTO

  • 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.

TESTI/BIBLIOGRAFIA

Materiale disponibile su aulaweb o distribuito a lezione (copia dei lucidi e note).

Ulteriori riferimenti:

Methods in Neuronal Modeling, Koch and Segev, MIT press, 1999.

Spiking Neuron Models, Gerstner and Kistler, Cambridge press, 2002.

Dynamical systems in neuroscience. Izhikevich, MIT press, 2007.

P. Dayan and L.F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 200

DOCENTI E COMMISSIONI

Ricevimento: Previo appuntamento via e-mail.

Ricevimento: Lunedì                 11:00-13:00 Giovedì                16:00-17:00 Ufficio: pad. E, Via Opera Pia 13 (III piano) Lab: “Bioengineering - SyNaPSI”,  pad. E, Via Opera Pia 13, (I piano)  

Commissione d'esame

SILVIO PAOLO SABATINI (Presidente)

PAOLO MASSOBRIO (Presidente)

LEZIONI

Modalità didattiche

Lectures and practicals

ESAMI

Modalità d'esame

Esame orale e discussione progetto

Calendario appelli

Data Ora Luogo Tipologia Note
14/02/2020 09:00 GENOVA Esame su appuntamento
24/07/2020 09:00 GENOVA Esame su appuntamento
18/09/2020 09:00 GENOVA Esame su appuntamento
12/02/2021 09:00 GENOVA Esame su appuntamento