AIMS AND CONTENT

LEARNING OUTCOMES

• Neuronal models
• Synapses and synaptic plasticity models
• Network models

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. At the end of the course, the bioengineering student will have strong skills to deal with neuroengineering issues.

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

Exam Board

PAOLO MASSOBRIO (President)

SERGIO MARTINOIA

SILVIO PAOLO SABATINI (President Substitute)

LESSONS

Class schedule

COMPUTATIONAL NEUROSCIENCE

EXAMS

EXAM DESCRIPTION

Oral exam about all the topics of the teaching.
Exams will be during the months of December, January, February, June, July, and September. No others exams will be provided during the year. After the exam, the student has 1 week to decide if the mark is fine or not. After this 1 week, the exam will be signed with the assigned mark.
If a student accomplishes a positive grade (greater or equal to 18/30), but he is not satisfied, he can give only once the exam, and in this occasion, the exam will be signed.

ASSESSMENT METHODS

Oral exam about the basic and advanced techniques for modeling neuronal structures from single neuron up to large-scale neuronal networks.

Exam schedule