AIMS AND CONTENT

TEACHING METHODS

Combination of traditional lectures, classroom discussion, and lab activities.

SYLLABUS/CONTENT

1.    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
2.    Simple Neuronal dynamics, excitability threshold, oscillations. Mathematical background of non-linear systems and portrait analysis; HH model; Hidmarsh-Rose model; Morris-Lecar and Fitzhug-Nagumo models
3.    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
4.    Synaptic transmission and plasticity. Static Models of synapses: exponential and alpha function synapses. Dynamical models: Destexhe model, Yamada-Zucker model, Markovian models. Synaptic plasticity models: Hebbian rule, Spike Timing Dependent Plasticity (STDP), Long term potentiation and depression
5.    Network Models. Firing Rate Models, mathematical derivation and applications. Spiking Models: point vs multi-compartment networks, network topologies (scale-free, small-world, random), interplay between connectivity and dynamics

TEACHERS AND EXAM BOARD

Exam Board

PAOLO MASSOBRIO (President)

SERGIO MARTINOIA

LESSONS

Class schedule

COMPUTATIONAL NEUROSCIENCE

EXAMS

ASSESSMENT METHODS

Oral exam + practical project (4 hours) using a simulator for developing neuronal models  

Exam schedule