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COMPUTATIONAL NEUROENGINEERING

CODICE 90533
ANNO ACCADEMICO 2021/2022
CFU
  • 6 cfu al 1° anno di 10852 COMPUTER SCIENCE (LM-18) - GENOVA
  • SETTORE SCIENTIFICO DISCIPLINARE ING-INF/06
    LINGUA Inglese
    SEDE
  • GENOVA
  • PERIODO 2° Semestre
    MATERIALE DIDATTICO AULAWEB

    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.

    MODALITA' 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

    Commissione d'esame

    PAOLO MASSOBRIO (Presidente)

    SILVIO PAOLO SABATINI (Presidente Supplente)

    LEZIONI

    Orari delle lezioni

    L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

    ESAMI

    MODALITA' D'ESAME

    Esame orale e discussione progetto

    Calendario appelli

    Data Ora Luogo Tipologia Note
    11/02/2022 09:00 GENOVA Esame su appuntamento
    22/07/2022 09:00 GENOVA Esame su appuntamento
    16/09/2022 09:00 GENOVA Esame su appuntamento
    10/02/2023 09:00 GENOVA Esame su appuntamento