NRS 495Neuroscience Seminar

Grinnell College · 4 credits · UGRD

Catalog

The seminar provides the culmination of the neuroscience concentration. As a recapitulation of the interdisciplinary nature of the field, a significant problem in the field will be chosen for study and students will be exposed to multiple approaches to address this problem. The course will focus on analysis of relevant primary literature with an emphasis on student-led discussion. A major writing project in the course will integrate the student's coursework in the concentration. Prerequisite: Neuroscience 250, completion of or concurrent enrollment in the cross-divisional elective, and senior standing. Limited to neuroscience concentrators. STAFF.

Recent sections

Fall 2026 · 2 sections

Aug 27 – Dec 18

Section	Days & time	Location	Instructor
01Class #98198-NRS-495-01	Tuesday, Thursday 8:00 AM - 9:20 AM Noyce Science Ctr 1531 (Research)	Noyce Science Ctr 1531	Nancy Rempel-Clower
01 notes: Writing intensive. Faculty will give substantial oral and/or written feedback to students on writing assignments and provide students with opportunities to apply that feedback to future assignments or revisions. At least 50% of the final grade is based on graded writing assignments. Data intensive. Develop an informed critical or theoretical perspective on the social impact of data collection, including the social construction of data production, and the use of algorithmic techniques to process that data.

02Class #98199-NRS-495-02	Tuesday, Thursday 1:10 PM - 2:30 PM Noyce Science Ctr 1245 (Research)	Noyce Science Ctr 1245	Nancy Rempel-Clower
02 notes: Writing intensive. Faculty will give substantial oral and/or written feedback to students on writing assignments and provide students with opportunities to apply that feedback to future assignments or revisions. At least 50% of the final grade is based on graded writing assignments. Data intensive. Develop an informed critical or theoretical perspective on the social impact of data collection, including the social construction of data production, and the use of algorithmic techniques to process that data.