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Updated: Feb 26, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Computational training for the next generation of neuroscientists
Mark S Goldman1, Michale S Fee2
1Center for Neuroscience, Department of Neurobiology, Physiology, and Behavior, and Department of Ophthalmology and Vision Science, University of California - Davis, Davis, CA 95618, USA.
Current Opinion in Neurobiology
|July 25, 2017
Summary
Neuroscience increasingly needs computational skills, but training often lacks these quantitative methods. This study surveyed neuroscientists to identify gaps and suggest improvements for computational neuroscience education.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Data Science in Biology
Background:
- Modern neuroscience relies heavily on quantitative and computational data analysis.
- Traditional biology curricula often lack emphasis on advanced computational and quantitative approaches.
- A gap exists between the computational demands of neuroscience research and current training methods.
Purpose of the Study:
- To identify critical needs in computational neuroscience training.
- To pinpoint areas for improvement in educational resources.
- To propose strategies for enhancing quantitative and computational skills among neuroscientists.
Main Methods:
- Conducted an informal poll of computational and non-computational neuroscientists.
- Gathered data on perceived training deficiencies and resource availability.
- Analyzed survey responses to identify key themes and recommendations.
Main Results:
- Identified significant needs for advanced statistical and computational training.
- Highlighted a demand for more accessible educational resources and mentorship.
- Revealed a desire for interdisciplinary training integrating biology and computation.
Conclusions:
- Addressing training gaps is crucial for the advancement of computational neuroscience.
- Developing targeted educational programs and resources is essential.
- Facilitating quantitative and computational training will better prepare future neuroscientists.
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