CODICE 90533 ANNO ACCADEMICO 2018/2019 CFU 6 cfu anno 1 COMPUTER SCIENCE 10852 (LM-18) - GENOVA SETTORE SCIENTIFICO DISCIPLINARE ING-INF/06 LINGUA Inglese SEDE GENOVA PERIODO 2° Semestre MATERIALE DIDATTICO AULAWEB OBIETTIVI E CONTENUTI OBIETTIVI FORMATIVI Students will initially learn that the computational mechanisms of the human brain are one of the greatest challenges of this century and that a great effort has been provided thanks to large-scale simulations and the development of theoretical models at different scales of observation. Students will then be introduced to the usage of computational techniques to model biological neural networks and will understand the brain and its function through a variety of theoretical constructs and computer science analogies. Students will be provided with 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 understanding of the coding strategies used by the “biological” brain to process incoming stimuli, and produce cognitive and/or motor outputs. 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 PAOLO MASSOBRIO Ricevimento: Previo appuntamento via e-mail. SILVIO PAOLO SABATINI 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 PAOLO MASSOBRIO (Presidente) SILVIO PAOLO SABATINI (Presidente) LEZIONI Orari delle lezioni L'orario di questo insegnamento è consultabile all'indirizzo: Portale EasyAcademy ESAMI MODALITA' D'ESAME Esame orale e discussione progetto Calendario appelli Data appello Orario Luogo Tipologia Note 15/02/2019 09:00 GENOVA Esame su appuntamento 26/07/2019 09:00 GENOVA Esame su appuntamento 20/09/2019 09:00 GENOVA Esame su appuntamento 14/02/2020 09:00 GENOVA Esame su appuntamento