6 ECTS credits
180 h study time
Offer 1 with catalog number 4023345FNR for all students in the 2nd semester at a (F) Master - specialised level.
Position of the course
This course teaches the principles of computational neuroscience and applies these principles in practica, which cover the implementation and study of neuronal models. The course starts from how numerical models have been developed to describe experimental neuroscience data and to study the spiking properties of single neurons within the brain. Afterwards, the dynamics of neurons within a population or network are discussed to cover higher-level properties such as learning, adaptation or plasticity. The exercises/practica are based on realistic datasets and problem sets and teach an important computational skillset to those with an interest in neuroscience or in developing neuronal/brain-inspired technologies.
Contents
1. Theoretical basis
Hodgkin-Huxley neuronal models: successes and limitations
From experiment to model: spiking statistics, refractoriness, PSTH, PLV
Neuronal codes: neurons in a network (plasticity, receptive fields, rate/synchrony)
Neuronal plasticity and learning: Hebbian’s rule, unsupervised learning
2. Application Oriented:
Programming numerical methods in Python/Matlab.
Hands-on experience with classical neuronal (network) models.
Keywords Computational neuroscience, Neuronal modeling, Neural codes, Spiking models and networks
Initial competences Signals and Systems, Mathematics & Statistics, Python or Matlab programming skills.
Conditions for credit contract Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract This course unit cannot be taken via an exam contract
Teaching methods lectures, exercises, practica, self-reliant study activities
Extra information on the teaching methods The course covers four topic modules which will last 3-4 weeks each. Each topic will be introduced during a lecture, after which small exercises are given and a practicum exercise is introduced (at UGent). The practica (with local UGent or VUB support) will involve programming exercises and neuronal model simulations and can be completed in student pairs. A written report needs to be prepared for each of the four practica.
Learning materials and price Slides, (e-)book chapters, publicly available publications/code repositories.
References Neuronal Dynamics - from single neurons to networks and models of cognition (W. Gerstner, W.M. Kistler, R. Naud and L. Paninski), Cambridge Univ. Press. 2014
1. Knowledge of how neuronal models can be adopted to simulate or understand experimental neuroscience. 2. Model and understand neuronal characteristics of single unit neuron models (adaptation, refractoriness, spiking probabilities, PSTH, PLV). 3. Understand the limitations and parameter choices of descriptive neuronal population models (synchrony, coding principles, inhibition/excitation, receptive fields) 4. Knowledge of how neural plasticity and learning can be modelled. 5. Python/Matlab programming skills to implement and evaluate neuronal models.
The final grade is composed based on the following categories:
Other Exam determines 100% of the final mark.
Within the Other Exam category, the following assignments need to be completed:
Evaluation methods end-of-term evaluation and continuous assessment via reports
Examination methods in case of periodic evaluation during the first examination period Written examination, open book examination, oral examination
Examination methods in case of periodic evaluation during the second examination period Written examination, open book examination, oral examination
Examination methods in case of permanent evaluation Assignment
Possibilities of retake in case of permanent evaluation Examination during the second examination period is possible in modified form
Extra information on the examination methods
During examination period: oral open-book exam, graded project reports, contribution to tasks.
Calculation of the examination mark
Activities/Reports during the semester (and feedback discussion) count for 60 %
Final written/oral exam counts for 40%.
This offer is part of the following study plans:
Master of Biomedical Engineering: Startplan
Master of Biomedical Engineering: Profile Radiation Physics
Master of Biomedical Engineering: Profile Biomechanics and Biomaterials
Master of Biomedical Engineering: Profile Sensors and Medical Devices
Master of Biomedical Engineering: Profile Neuro-Engineering
Master of Biomedical Engineering: Standaard traject (NIEUW)