Mathematics Prep-Course for Computational Neuroscience

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.

Course description:

This course is intended as a refreshment of mathematical tools of analysis, linear algebra and statistics which will be necessary for the CNS students in the first year.

Students will acquire broad mathematical knowledge of functions in one resp. several real variables, in linear algebra, in differential equations, in probability theory and statistics, as needed for Computational Neuroscience. Basic mathematical skills for the analysis and approximation of functions, solutions of differential equations and signals, for solving linear systems and systems of ordinary differential equations will be refreshed. Participants will learn to apply mathematical foundations to the modeling and analysis of neural data and to apply basic mathematical techniques to problems in Computational Neuroscience with guided assistance.

Lecturers:

Tilo Schwalger

Course structure:

Time: Lecture 9:15 -12 AM, Tutorial 2-5 PM

Location: BCCN Berlin, Lecture Hall (lecture) and Seminar Room (tutorial)

Target group:

This module is elective for students of the master program Mathematics and Computational Neuroscience (generally for advanced Diploma students or master students).

Please enroll per Email to graduateprograms@bccn-berlin.de

Requirements:

Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points). Good command of the English language.

Course certificates:

Students who successfully passed the written exam, will be awarded 4 ECTS. Master students of Computational Neuroscience can recognize this course for their Individual Studies.

Links:

Link to the lecturer's website Li

A link will be provided to the program.