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

Exam Board

PAOLO MASSOBRIO (President)

SERGIO MARTINOIA

SILVIO PAOLO SABATINI (President Substitute)

LESSONS

LESSONS START

https://corsi.unige.it/11159/p/studenti-orario

Class schedule

The timetable for this course is available here: 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.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.