OBIETTIVI FORMATIVI

Neurons: advanced biophysical modeling and computer simulation techniques. Synapses: Phenomenological models vs biophysical models; Exponential synapses at one and two time constants; Synaptic plasticity; Neuron networks: simplified models; Role of connectivity in network dynamics

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

Fornire gli strumenti teorici e di simulazione per modellare l'attività elettrofisiologica generata da strutture neurali. Sulla base degli strumenti offerti, gli obiettivi del corso sono:
Essere in grado di modellizzare reti di neuroni con particolari pattern di attività elettrofisiologica
Essere in grado di scegliere i modelli di neuroni corretti sulla base delle esigenze sperimentali
Essere in grado di risolvere problemi teorici avanzati di computazione neuronale
Essere in grado di sapere scegliere la strategia computazionale migliore in base al problema richiesto

PREREQUISITI

Conoscenze avanzate di matematica, analisi matematica; analisi dei segnali elettrofisiologici; neurofisiologia

MODALITA' DIDATTICHE

Lezioni teoriche frontali in aula. Esercitazioni da svolgere in aula sui vari argomenti del corso.

PROGRAMMA/CONTENUTO

• 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

TESTI/BIBLIOGRAFIA

Materiale distribuito e note disponibili su 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.