The scale and complexity of biological data are fast expanding as biotechnology develops. How are we going to process and interpret large-scale data? Using example-based approaches in the context of behavioural neuroscience (e.g., sleep, movement, learning, decision-making), this course aims to bridge biology and computational analysis techniques. Through lectures and hands-on sessions, students will learn about modern methodology in neuroscience (e.g., electrophysiology, optogenetics, calcium imaging) and how to analyze neural data sets. Students will be introduced to programming in MATLAB tailored for neuroscience, signal processing, image processing, statistical analysis, and machine learning techniques. By developing practical skills through various neural data types (e.g., EEG, EMG, Ca2+ Imaging), this course equips students with the skills to handle neural data, basic computational approaches in neuroscience, and a quantitative understanding of brain functionality.
The scale and complexity of biological data are fast expanding as biotechnology develops. How are we going to process and interpret large-scale data? Using example-based approaches in the context of behavioural neuroscience (e.g., sleep, movement, learning, decision-making), this course aims to bridge biology and computational analysis techniques. Through lectures and hands-on sessions, students will learn about modern methodology in neuroscience (e.g., electrophysiology, optogenetics, calcium imaging) and how to analyze neural data sets. Students will be introduced to programming in MATLAB tailored for neuroscience, signal processing, image processing, statistical analysis, and machine learning techniques. By developing practical skills through various neural data types (e.g., EEG, EMG, Ca2+ Imaging), this course equips students with the skills to handle neural data, basic computational approaches in neuroscience, and a quantitative understanding of brain functionality.
