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NIH Brain Initiative Summer Course on Models and Neurobiology

Interdisciplinary Research Training in Computational Neuroscience

for PhD students, post-docs, medical students, residents, and faculty

Course Flyer

2-week Summer Course

  • June 4-15, 2018
  • Starts at 9 a.m. on Monday, June 4, and ends at noon on Friday, June 15
  • Limited to 24 participants
  • Deadline extended to March 1 2018
  • Review of applications may begin earlier

A longstanding goal of neuroscience research is to understand how activity of individual neurons and within neural circuits gives rise to outputs ranging from movement to thought. Integrative and interdisciplinary training in neuroscience is necessary to help develop scientists who can work together to address this goal by using approaches from diverse fields including biology, psychology, computer science, electrical engineering, and physics. Our training course is designed to introduce and strengthen the quantitative skills of researchers with biological backgrounds and increase the knowledge of neuroscience concepts for those from quantitative backgrounds. No previous experience with modeling is expected.

Summer Course components: Math refresher; Neurons and Circuits – including wet lab experiments; Modeling projects at 1- and 2-cell levels using canned NEURON programs; Model development from basics using NEURON; Neuro-electrophysiology from an engineering systems perspective; Computational model research cases studies; and Development of individual computational research projects.

Click here for a  Tentative Schedule 2018

Past attendees have been from universities across the nation, including UC-Berkeley, UCLA, Brown, U-Florida, Georgia-Tech/Emory, U-Mass, MIT, MGH, U-Maryland, U-Mich, NYU, U-Penn, U-Rochester, Rutgers, UCSF, Stanford, Tulane, U-Washington, and more.

NIH Research Training (BRAIN Initiative)

Computational Neuroscience: Models & Neurobiology (for pre-doc, post-docs, medical students, residents and faculty)


Undergraduate Research

NSF Neuro REU Project


Modeling Resources

Single Neuron Models


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