Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
Students will learn an end-to-end perspective on analysis of brain signals, from neurobiological microcircuit mechanisms and electrophysiological recordings to modelling, measurement, and analysis of the signals. The students will be able to:
1. Describe basic neurobiological mechanisms governing collective neuronal activity.
2. Describe key concepts of complex dynamic systems and apply computational modelling methods to simulate these phenomena.
3. Describe and analyse the biophysical signal generation mechanisms at micro-, meso-, and macro-scale electrophysiological recordings, and interpret the measurability of specific forms and features of the neuronal activity.
4. Apply analysis methods to real and simulated data to study collective neuronal activity, design and implement new analyses, and evaluate the reliability and informativeness of the outcomes.
Schedule: 14.01.2021 - 15.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Matias Palva
Teacher in charge (applies in this implementation): Matias Palva
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Neuronal oscillations and brain dynamics, electrophysiological measurement methods, neuronal modelling and time series analysis approaches, approaches to linking neurophysiological understanding with psychological/behavioral measures for functional inferences. Python-programming based exercises with simulation and analysis methods and application of the analyses to real and simulated data.
Assessment Methods and Criteria
Examination. Exercises. Project work and presentation.
Lectures 20-24 h and project presentations 2 h. Exercise sessions 20-24 h. Homework related to exercise sessions 20 h. Project work and preparation for project presentation 30 h. Writing learning diaries 10 h. Preparation for exam 30 h. Exam 3 h.
To be specified in MyCourses at the start of the course.
Required: NBE-E4210 or similar skills, Python programming. Recommended: NBE-E4000, NBE-E4050.