CODE 80575 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 BIOENGINEERING 11159 (LM-21) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/06 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW Computational Neuroscience is an advanced course offered to students in the last year of the master's degree in Bioengineering aimed at providing the tools and methods for modeling the nervous system at different scales, from single neurons to complex neuronal networks. In particular, transmembrane ion channels, single neurons, synapses and neuron networks will be studied and analyzed using different modeling strategies. AIMS AND CONTENT LEARNING OUTCOMES Il corso offre agli studenti i metodi e le tecniche per la modellizzazione del sistema nervoso, dal singolo neurone, alle sinapsi, fino a reti di neuroni di grosse dimensionalità. Si porrà l'enfasi sulla relazione esistente tra i modi con cui i neuroni emettono potenziali d'azione (pattern) e modello utilizzato. AIMS AND LEARNING OUTCOMES Goal of the teaching is to provide the theoretical contents for modeling neuronal structures at different scale, from single neuron up to large-scale complex networks. For this reason, the course will be focused on how to model and simulate the electrophysiological activity of neuronal structures. Based on the tools offered, the objectives of the course are: - model neuronal networks with particular patterns of electrophysiological activity - choose the correct neuron models based on experimental needs - solve advanced theoretical problems of neuronal computation - choose the best computational strategy based on the required problem PREREQUISITES Advanced knowledge of mathematics, mathematical analysis; analysis of electrophysiological signals; neurophysiology TEACHING METHODS Combination of traditional lectures, classroom discussion, and lab activities. SYLLABUS/CONTENT Biophysical Model of Neurons Brief introduction on equivalent membrane circuit and membrane electric properties Passive models and propagation equation Hodgkin and Huxley (HH) model and dynamics From HH to multichannel neuron models Role of neuron morphology and dendritic tree in the electrophysiological patterns Reduced models: from multi-compartments to 2-3 compartments neurons Calcium dynamics Neuronal dynamics, excitability threshold, oscillations Mathematical background of non-linear systems and portrait analysis Hodgkin and Huxley model Morris-Lecar model Fitzhug-Nagumo model From bio-inspired to abstracted point neurons The family of integrate-and-fire (IF) neurons Leaky-Integrate-and-Fire (LIF) Exponential-Integrate-and-Fire (EIF) Quadratic-Integrate-and-Fire (QIF) Advantages and limitations of IF models The Izhikevich model Stochastic models Poissonian process Renewal process The synaptic transmission and plasticity Exponential synapse Alpha function synapse Dynamical models Desthexhe model Markovian models Modeling the synaptic plasticity Hebbian rule Depression, Facilitation, Augmentation (short-term plasticity) Long Term Potentiation/Depression Spike Timing Dependent Plasticity (STDP) Network Models Firing Rate Model Spiking Model Point vs. multicompartmental networks Balanced networks Network architecture Networks dynamics Interplay between dynamics and connectivity Different kind of connectivities Building a graph Properties of a graph Functional, Structural, Effective connectivity 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. Dynamical systems in neuroscience,. Izhikevich, MIT press, 2007. Computational Modeling Methods for Neuroscientists, De Schutter, MIT press, 2010. Theoretical Neuroscience, Dayan and Abbott, MIT press, 2001. TEACHERS AND EXAM BOARD PAOLO MASSOBRIO Ricevimento: On demand, by e-mail contact at: paolo.massobrio@unige.it. Office direct phone number: 010-335-2761. Office: Building E, Via Opera Pia 13 (III floor) LESSONS LESSONS START https://corsi.unige.it/11159/p/studenti-orario Class schedule The timetable for this course is available here: Portale EasyAcademy EXAMS EXAM DESCRIPTION Written exam about all the topics of the teaching. Exams will be during the months of January, February, June, July, and September. No others exams will be provided during the year. ASSESSMENT METHODS Written exam about the advanced techniques for modeling neuronal structures from single neuron up to large-scale neuronal networks.
