Cognitive Data Science: Genes, Brains and Behaviour

Course manual 2024/2025

Course content

"Cognitive Data Science: Genes, Brains, Behavior" is a course designed for those interested in the intersection of data science and cognitive science. The course is broken up into three parts, focusing on data analytic techniques at the molecular, neural, and behavioral levels. At the molecular level, students will gain proficiency in preprocessing and analyzing gene expression data, including univariate and multivariate analyses, and understanding gene expression correlations within regulatory networks. The neuronal section covers processing and interpreting both single neuron and ensemble neural recordings, employing dimensionality reduction and decoding techniques to uncover underlying neural activities. Finally, the course explores the behavioral level, where students learn to formulate, simulate, and validate computational models of behavior, fit these models to experimental data, and employ model comparison techniques to test hypotheses. In addition to lectures and discussions, students will get hands-on experience analyzing, visualizing, and modeling experimental data in python at multiple scales.

Objectives

• Perform and interpret multivariate methods for gene expression analysis
• Apply data analysis toolboxes to experimental data using Python
• Demonstrate ability to (pre)process gene expression (RNAseq) data
• Perform and interpret univariate comparative gene expression analysis
• Demonstrate ability to (pre)process neuronal activity, with a focus on ensemble neural recordings
• Analyse and interpret the activity of single neurons and of populations of neurons,
• Apply decoding approaches to the activity of populations of neurons
• Formulate, implement, and simulate computational models of behaviour
• Fit computational models of behaviour to experimental data
• Be able to compare the ability of alternative models of behavior to fit a dataset

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study

Each week of the course, we will have two lectures that will introduce various concepts in cognitive data science. Following lectures, there will be computer labs where you put into practice the concepts covered in the lectures. The lab practicals will be done using Jupyter Notebooks in Python. A portion of each lab will be spent working on one of 3 graded assignments (in pairs) corresponding to each of the three modules of the course (genes, brains, behaviour). In addition, students will use time outside of class for self-study and completing the assignments.

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. In the case of practicals, the student must attend at least 80%. Should the student attend less than 80%, he/she must redo the practical, or the Examinations Board may have one or more supplementary assignments issued.
2. In the case of study-group sessions with assignments, the student must attend at least 80% of the study-group sessions. Should the student attend less than 80%, he/she must redo the study group, or the Examinations Board may have one or more supplementary assignments issued.

Additional requirements for this course:

We expect students to be present at all computer labs as students will work in pairs on graded assignments during the lab practicals. If you cannot attend a lab practical due to illness, you must let the lab instructor know and coordinate with your assignment partner to contribute to the assignments.

Assessment

Assignments

Students will work in pairs on three assignments during the course corresponding to each of the course modules (genes, brains, behavior). A portion of each computer lab will be used to work on the assignments, but students may all need to spend time outside of class to complete them. A grading rubric for the assignments will be made available on Canvas.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Last year's student feedback

In order to provide students some insight how we use the feedback of student feedback to enhance the quality of education, we decided to include the table below in all course guides.

Contact information

Coordinator

• dr. B.T. Martin