Signals and States: Quantitative Time-Series Analysis in Neuroscience and Biology
Description
Modern neuroscience and biology increasingly rely on large-scale, time-resolved datasets, including electrophysiology, calcium and longitudinal imaging, quantitative behavior, physiological measurements, and multimodal recordings.
Signals and States provides doctoral students with a conceptual and practical framework for analysing such data. The course focuses on how to formulate well-posed scientific questions, select appropriate quantitative approaches, interpret high-dimensional structure and temporal dynamics, and critically assess what can and cannot be inferred from complex datasets.
Through lectures, discussion, and hands-on practical sessions, participants will work with topics including high-dimensional representations, dimensionality reduction, neural and biological dynamics, state transitions, quantitative behavior, encoding and decoding, model validation, and the distinction between association, prediction, and causal interpretation.
Neuroscience provides the principal conceptual framework and teaching examples, while the analytical principles are broadly applicable across biology and biomedicine.
Content Providers
Learning Outcomes
Purpose
The purpose of the course is to develop doctoral students’ ability to use quantitative approachesto address scientific questions involving time-resolved data in neuroscience and biology. Thecourse aims to strengthen students’ ability to choose appropriate analytical strategies, interpretquantitative results in their biological context, and critically assess the strength and limitationsof conclusions drawn from complex datasets.
Intended learning outcomes
After completing the course, the student should be able to explain key concepts and analyticalchallenges associated with time-resolved neural and biological data, including high-dimensional representations, temporal dynamics, and relationships between biological signals.
Competence and skills
After completing the course, the student should be able to:
• select and apply an appropriate quantitative approach to a time-resolved neural, behavioral, or
other biological dataset;
• visualize and interpret high-dimensional structure, temporal dynamics, or relationships
between biological signals in relation to a clearly formulated scientific question.
Judgement and approach
After completing the course, the student should be able to:
• critically evaluate the suitability and robustness of a quantitative analysis in relation to the
data, experimental design, and biological question;
• distinguish between association, prediction, and causal interpretation, and identify important
limitations or alternative explanations when interpreting quantitative results.
Prerequisites & Technical Requirements
Prerequisites
Basic familiarity with biological research and quantitative reasoning is expected. Basic programming experience in Python, MATLAB, R, or an equivalent language is required. Advanced programming proficiency is not required.
Technical requirements
Participants should bring their own laptop for hands-on computational exercises. Basic familiarity with Python, MATLAB, R, or an equivalent programming language is expected. Instructions regarding software installation and computational resources will be provided before the course.
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Affiliations & Networks
Activity log
