Course Overview

Computational neuroscience is a research area in rapid development, comprehending several mathematical and computational techniques to model neurons and networks of neurons at different levels of detail. It is having growing importance in neuroscience, strengthening the interface between this science and so-called exact sciences. Given the interdisciplinary nature of the Physics Applied to Medicine and Biology Graduate Program, it is important that such a course exists to give the student an opportunity to learn the main methods and tools of computational neuroscience.

Learning Achievement

To offer an introduction to the mathematical and computational methods used in theoretical studies in neuroscience.

Competence

Course prerequisites

Grading Philosophy

Lists of exercises (40% of final grade); Computer projects (60% of final grade).

Course schedule

1. Presentation of computational neuroscience. 2. Basic elements of neuroscience: neurons, synapses and neural circuits. 3. Neuron models I. integrate-and-fire model and its extensions. 4. Neuron models II. the Hodgkin-Huxley model. 5. Neuron models III. Conductance-based and compartmental models. 6. Neuron models IV. Reduced models and phase space analysis. 7. Models of synapses. 8. Models of networks of neurons.

Course type

Online Course Requirement

Instructor

Antonio Carlos Roque da Silva Filho

Other information

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