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 The course provides an overview of the tools, methods, and strategies used in the field of computational neuroscience for modeling the nervous system at different scales, from single neurons to complex neuronal networks. Particular emphasis is placed on the different approaches that can be followed to mathematically describe nervous structures on the basis of the experimental insights. AIMS AND CONTENT LEARNING OUTCOMES The course offers to students the methodologies, strategies, and tools to model single neurons, synapses, and large-scale neuronal networks. Particular emphasis will be given to the interplay between exhibited patterns of electrophysiological activity and the kind of used model. AIMS AND LEARNING OUTCOMES To provide advanced concepts of computational neuroscience and highlight the possible role of the bioengineer in developing new: detailed single neuron models synaptic models for chemical synapses models of medium- large-scale neuronal networks pointing out the role of the complexity of the underlying topology To provide possible strategies and highlight the possible role of the bioengineer in solving problems like: choosing the correct models based on experimental needs theoretical problems of neuronal computation choosing the best computational strategies based on the required problem To critically evaluate: the possible limitations of the developed models the main biomedical applications of the developed models both for basic and applied science the need for sharing models to the scientific community PREREQUISITES Advanced knowledge of mathematics and physics, analysis of electrophysiological signals; neurophysiology. TEACHING METHODS lectures in the classroom 10 hours of exercitations to develop models supervised by a tutor SYLLABUS/CONTENT Biophysical Model of Neurons: Passive models and propagation equation; Hodgkin and Huxley (HH) model; From HH to multi-channels 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 and its role in the bursting activity Morris-Lecar and Fitzhug-Nagumo models From bio-inspired to abstracted point neurons: The family of integrate-and-fire (IF) neurons, LIF, EIF, QIF, aEIF, advantages and limitations of IF models. Izhikevich model, role of the parameters model for the genesis of different firing patterns Synaptic transmission and plasticity. exponential and alpha function synapses. Destexhe model Yamada-Zucker model Markovian models. Synaptic plasticity models: Hebbian rule, Spike Timing Dependent Plasticity (STDP), Long term potentiation and depression Network Models. Spiking Models: point vs multi-compartment networks network topologies (scale-free, small-world, random) interplay between connectivity and dynamics RECOMMENDED READING/BIBLIOGRAPHY Slide and other teaching materials will be provided Additional materials will be made avaiable on the course aualweb page 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 The exam will consist in a written exam about all the topics presented during the lectures. An optional project is a plus. Exams will be during the months of January, February, June, July, and September. No others exams will be provided during the year. ASSESSMENT METHODS The aim of the exam will be to evaluate the level of the learning outcomes of the course reached by the student.
