• PSYC65: Systems Neuroscience with Lab

  • Official course description: The primary focus of this course is the physiological basis of behavior from a systems perspective. Such topics as localization of function, neural models, and the physiological bases of sensory/motor systems, learning/memory, and spatial cognition are considered. The laboratory introduces the student to the anatomy and physiology of the mammalian central nervous system and to some of the principal techniques used in systems and behavioral neuroscience. Laboratory sections will be assigned during the first week of class.

  • course syllabus (2015)
  • PSYC50.10: The Rhythmic Brain

  • Official course description: This course explores the physiological basis and functional relevance of oscillations, which are ubiquitous in the brain. Rhythmic pattern generators in specific neurons and circuits are essential for generating repeating movements such as breathing and walking; yet, oscillations are equally prominent in neural systems for sensation, cognition, and memory. Could it be that these rhythms are a fundamental building block of information processing in neural circuits? This course provides an introduction to the detection, analysis and interpretation of oscillations in the brain. Using these tools, we will survey the origin and functional role of oscillations in a variety of neural systems across animal and human species, and ask what general principles emerge.

  • course syllabus (2016)

  • This class features "heads-on" experience with headsets that record brain activity.


  • Analysis of Neural Data

  • Official course description: Overall, the course is designed to provide hands-on experience with management, visualization, and analysis of neural data. Becoming skilled at these things is a rate-limiting step for many graduate projects requiring analysis. Even if your work only requires rudimentary analysis, awareness of what else can be done and how to do it well is valuable, for instance when evaluating the work of others in the literature!

    To do so, the focus is on introducing some commonly used tools, such as GitHub and relevant functionality within MATLAB - and then to actually use these on real data sets. Initially, those data sets will be local field potentials and spiking data recorded from various brain areas in freely moving rodents in the van der Meer lab; however, a crucial goal of the course is that after some initial steps you will use your own data. (If you don't have your own, you can do everything with data provided here.)

    We will make contact with a few concepts from computer science, signal processing, and statistics. However, the focus is on making initial steps that work and getting pointers to more complete treatment, rather than a thorough theoretical grounding. Nevertheless, to make sure that what you learn is not tied to specific data sets only, a number of principles of data analysis - applicable to any project of sufficient complexity - will be referenced throughout the course. You are invited to think of these and others, not only as you progress through this course, but especially as you organize your own data analyses and read analyses performed by others.

  • This class uses step-by-step tutorials, available on our lab wiki.
  • Current Topics in Behavioral Neuroscience

  • Official course description: Examining what changes in behavior result from alterations to structures in the brain continues to be a foundational approach in neuroscience. The last decade has seen an unprecedented increase in the specificity of such interventions; yet, these technological advances have placed in sharp focus the difficulties inherent in interpreting the resulting behavioral effects. This course provides training in the design and interpretation of contemporary behavioral neuroscience experiments. Topics include multiple parallel systems, compensation, learning vs. performance, on-target vs. off-target effects, averaging across trials and/or subjects, single vs. multiple tasks, and functional localization vs. distribution, with particular focus on how these perennial issues relate to current and emerging experimental tools.