Overview of Modules
Cognitive Neuroscience: Perception and Action
The module covers the theoretical foundations and practical applications of neurocognitive processes in the fields of perception and action. The neurophysiology of cortical sensory processing across all sensory modalities (vision, somatosensory, auditory, olfaction, gustatory) will be discussed. Theoretical foundations and important empirical findings (e.g., general and biological psychology) are taught using selected examples. Students will get an overview of the use of selected (neuro-)cognitive methods in interaction with (computational) process models and their practical applications. Central theoretical concepts, empirical findings and practical applications of (neuro-)cognitive methods in the fields of cognitive neuroscience will be discussed. Based on this knowledge, students develop specific questions (e.g. questions on object recognition or decision making), to combine these with selected (neuro-)cognitive methods (e.g. ratings, reaction time measurement, oculo- and pupillometry, EEG, fMRI, fNIRS, non-invasive neuromodulation methods/tDCS/rTMS) according to the principle "methods must fit the questions". A focus is on the evaluation and interpretation of empirical investigations.
Cognitive Neuroscience: Information Processing and Consciousness
The module starts with an introduction of cellular physiology in the nervous system as basis for information processing, especially with regard to micro- and macroanatomical organizational principles of cortical information processing and representation. Theories and empirical studies of signal processing in neuronal networks as the basis of cognitive processes such as learning and memory processes will be introduced. This includes in particular computer simulation models and neurocognitive methods. The module further covers theoretical foundations and practical applications of neurocognitive processes in the fields of working memory, mental imagery, long-term memory, and emotion processing. Theories and empirical data on the realization of human consciousness functions are introduced and discussed, as well as disturbances of consciousness in psychopathologies and the study of induced consciousness alterations.
Affective and Social Neuroscience
In this module, selected examples of the theoretical foundations and practical applications of neurocognitive methods in affective and social neuroscience will be discussed in depth. Students will critically discuss relevant studies as well as interpret their results. By the end of the module the students will have expanded their knowledge of neurocognitive, emotional and motivational psychology as well as have acquired the theoretical and methodological knowledge in order to perform research into a variety of affective and social processes within a large variety of contexts.
Neurocognitive Methods and Data Analysis
The module is divided into two thematic blocks. (1) Introduction to Magneto/Electroencephalography, i. e., basic aspects of neurophysiology and M/EEG signal generation, recording and analysis will be taught. (2) Introduction to Functional Magnetic Resonance Imaging, i.e., basic aspects of fMRI signal generation, acquisition and analysis will be discussed.
Probabilistic and Statistical Modelling
Four blocks of topics are covered. (1) A preliminary mathematics course is designed to refresh and deepen elementary mathematical knowledge acquired in school and undergraduate study. (2) The General Linear Model (GLM) is a unifying view of a number of statistical methods and models and a basic example of in-depth frequentist and Bayesian reasoning. In this topic block, the distribution theory of the GLM is covered along with a number of applications. (3) Anatomical localization of cognitive processes is usually achieved by applying the GLM to fMRI data. In this topic block, special features of this procedure (e.g., control of type 1 error rate, psychophysiological interactions, etc.) will be addressed. (4) In a topic block on advanced methods of neuroimaging data analyses, approaches of biophysical modeling or multivariate analysis based on machine learning approaches will be discussed.
Introduction to Programming
An introduction to programming with Python and Matlab will be given. Basic programming concepts are introduced and practiced with the help of example tasks. Applications of programming in neurocognitive research are trained with example data and experimental protocols. Experimental, data-analytical, and simulation-based applications will be introduced and practiced using small projects.
Computational Cognitive Neuroscience
Current topics in computational cognitive neuroscience will be covered, such as Markov decision processes, partially-observable Markov decision processes, reinforcement learning, drift-diffusion reaction-time models, biophysical network models, and neural networks.
Neurocognitive Methods Practical
The module is a continuation of the Neurocognitive Methods and Programming module. In this module students will practice data collection and data analysis methods with concrete examples thereby establishing the theoretical background and practical applications of neurocognitive processes. There is a special focus on the standardized methods (SPM, FSL) which will be used to explore the univariate and multivariate methods of fMRI and EEG data analysis as well as methods for the analysis of the structural and functional connectivity. Thus, through this module, students will learn the active application of these methods as well as the interpretation of their results. After completing this module students will have gained practical knowledge of experimental design and implementation in the areas of social, cognitive and affective neuroscience and will be familiar with the software FSL and SPM as well as knowing what situations are best dealt with by which.The module is a two week long intensive and interactive lecture series with an oral examination at the end.
In the module Research Experience, students complete an internship in either a domestic or foreign research institution of their choice under the supervision of an experienced scientist. There are an incredibly diverse range of fields from which to choose which cover the entire range of neuroscience research. During the internship students are involved in the research process including work on the experimental design, implementing the experiment and analysing the data. With this module students will have further expanded their previous methodological skills and developed specific skills relevant to their research project as well as have gained valuable experience of what working within a research group or institution involves. This module may overlap with the Master Thesis project however it is not required to do so.
In this module students give a short presentation as well as write an exposé to present their Master Thesis proposal ideas. The students meet as a group two or thee times in February with the committee members and present their thesis project ideas. The students each give a 20 minute presentation wherein they present the background information that is relevant to their topic, the specific question they which to address and the methods they will use. After the presentation there is a 20 minute question and answer period wherein the committee members and other students can comment on the project as well as address any methodological problems that may be present. The exposé is more or less the presentation in written form and will serve as a basis for the introduction and methods section of the thesis. The students submit their exposé to their supervisors for grading and the grade from the expose and the presentation are combined to form a final grade for the module.
In this module students complete their Master's Thesis project. The MCNB program offers students a number of options for their theses projects with the requirement that students gain some expertise within a neuroscientific domain. The projects can, but do not have to, take advantage of a neuroimaging technique such as EEG or fMRI. Students should discuss their ideas with professors of the MCNB program before committing to a project to ensure that their project fulfills the MCNB program MSc thesis requirements.