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Good command of the English language. Master students of Computational Neuroscience can recognize this course for their Individual Studies. Links: Link to the lecturer's website Li. Course description: This course is intended as bridge for students enrolled in Computational Neuroscience. The aim is to provide the basics in neurophysiology. The module provides an overview of the current state of brain research and a summary of the fundamental biological background necessary for the design and implementation of models. After completing the module, participants should understand the general architecture of the mammalian brain with its major components and areas including circuitry, the major cell types and their function and the basic physiological principles that govern brain function.
Participating students will be given an introduction to state-of-the-art research approaches in various disciplines of neuroscience including behavioral neuroscience, electrophysiology and imaging techniques. The emphasis of the course is on imparting the absolutely necessary basics required for modeling biologically relevant information systems.
The course begins with a basic introduction to cells and neurons, the basic physiology of nerve cells and basic anatomy of the brain including the specific circuitry of major subregions such as the neocortex, hippocampus, limbic system, cerebellum and the basal ganglia. After this introduction, specific biologically based topics of interest to computational neuroscientist are treated including sensory transduction and different modalities, learning and memory, biological constraints on coding in the brain, large-scale approaches to understanding the brain, neuroscience in the laboratory and behavioural neuroscience.
Time is given at the mid-point and end of the course for revision and discussions of relevant topics of interest to the students. Course structure: Two weeks before the start of the winter semester, 2x30 hrs en block. Target group: This module is elective for students of the Master and Doctoral program Computational Neuroscience.
Course certificates 4 ECTS will be granted to students who successfully pass the final test.
Links: Link to the Moodle course page. The password will be given in the first lecture. Link to the lecturer's website. Course description: Participants should learn basic concepts, their theoretical foundation, and the most common models used in computational neuroscience. The module also provides the relevant basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting form these approaches so far.
After completing the module, participants should understand strengths and limitations of the different models. Participating students will learn to appropriately choose the theoretical methods for modeling neural systems.
They will learn how to apply these methods while taking into account the neurobiological findings, and they should be able to critically evaluate results obtained. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems.
Contents include: Hodgkin-Huxley model, Channel models, Synapse models, Single-compartment neuron models, Models of dendrites and axons, Models of synaptic plasticity and learning, Network models, Phase-space analysis of neuron and network models linear stability analysis, phase portraits, bifurcation theory. Target group: This module is compulsory for students of the Master program Computational Neuroscience, compulsory elective or elective for the specialization Computational Neuroscience and Artificial Intelligence generally for advanced Diploma students or master students.
Requirements: Mathematical knowledge: Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers worth 24 credit points. Basic programming skills. Course certificates: Students who have successfully passed the analytical and programming tutorials are admitted to the oral exam. The duration of an individual oral examination is 30 minutes. After the oral exam, students will be granted 12 ECTS credit points. For students not enrolled in the Master of Computational Neuroscience, additionally also varying combinations of the above components are possible.
Please enquire details of the lecturer.
During the course, I expect to have in-depth discussions on how to integrate experimental and theoretical approaches with other participants, where my current work can contribute to stimulate new ideas. Effective: Spring Quarter. Open to declared NPB majors only. I am interested in neuronal mechanisms and computational models relevant to learning, goal-directed behaviour and economic decision making. Up to know I've been looking mostly at a single cell level but broadening more and more to functions that can arise from networks of interacting neurons.
Links: Link to the Moodle course page the password will be given in the first lecture Link to the course homepage Link to the lecturers' webpage: Kempter , Lindner. Course description: Students will gain knowledge about the most important methods for experimental acquisition of neural data and the respective analytical methods, they will learn about the different fields of application, the advantages and disadvantages of the different methods and will become familiar with the respective raw data.
They will be enabled to choose the most appropriate analysis method and apply them to experimental data. Analysis of neural data 2nd semester : firing rates, spike statistics, spike statistics and the neural code, neural encoding, neural decoding, discrimination and population decoding, information theory, statistical analysis of EEG data, spatial filters, classification, adaptive classifiers.
Lecturers: 1st semester: Prof. John-Dylan Haynes, Prof. Michael Brecht, Prof. Gabriel Curio, and Dr. Vadim Nikulin. Petra Ritter 2nd semester: Prof.
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural In this book you will find that complex math and derivations are often either. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems Peter Dayan and and spike statistics code and data code giuliettasprint.konfer.eu
Richard Kempter, Prof. Benjamin Blankertz. Target group: This module is compulsory for students of the Master program Computational Neuroscience, compulsory elective or elective for the specialization Computational Neuroscience, Artificial Intelligence, and Signal Processing generally for advanced Diploma students or master students. Course certificates: Students who have successfully passed the lab sessions 1st semester and the analytical tutorial 2nd semester are admitted to the oral exam.
For students not enrolled in the Master of Computational Neuroscience, additionally also following combinations of module components can be taken:. Course description: Participants learn basic concepts, their theoretical foundation, and the most common algorithms used in machine learning and artificial intelligence. After completing the module, participants should understand strengths and limitations of the different paradigms, should be able to correctly and successfully apply methods and algorithms to real world problems, should be aware of performance criteria, and should be able to critically evaluate results obtained with those methods.
Participants should also be able to modify algorithms to new tasks at hand as well as to develop new algorithms according to the paradigms presented in this course. Contents include: Artificial neural networks: Connectionist neurons, the multilayer perceptron, radial basis function networks, learning by empirical risk minimization, gradient-based optimization, overfitting and underfitting.
Learning theories and support vector machines: statistical learning, learning by structural risk minimization. Probabilistic methods: Bayesian inference and neural networks, generative models.
Projections methods. Stochastic optimization. Clustering and embedding. Target group: This module is compulsory for students enrolled in the Master program Computational Neuroscience. Module components are compulsory elective or elective for students of other Master and Diploma programs of Berlin universities, who wish to specialize in Machine Learning and Artificial Intelligence, and who fulfill the prerequisites see below. The final grade is determined by the grade obtained in the oral examination. Students who successfully pass the oral exam will be awarded 12 ECTS credit points.
Course description: Participants should learn the basic concepts and most important topics in the Cognitive Neurosciences. In addition, they should know the state-of-the-art models in these domains and their theoretical foundations. After completing the module, participants should understand strengths and limitations of the different modeling approaches e.
Participants should also be able to modify models of cognitive processes as well as to apply existing models to novel experimental paradigms, situations or data. Contents include: Auditory and visual system, natural image statistics and sensory processing, motor system, psychology and neuroscience of attention, memory systems, executive control, decision making, science of free will and consciousness.
Data modeling and essential statistics, psychometric methods, signal detection theory, models of visual processing, models of visual attention, models of executive function.
Signal processing, sensory and cognitive modeling using Python. Lecturers: Prof.
Henning Sprekeler.