Title: Python for the practicing neuroscientist: an online educational resource
Authors: Emily Schlafly1, Anthea Cheung2, Samantha W Michalka3, Paul A. Lipton4, Caroline Moore-Kochlacs1, Jason Bohland, Uri T. Eden2, Mark A. Kramer2,6
1. Graduate Program in Neuroscience, Boston University, Massachusetts 02215
2. Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215
3. Olin College of Engineering, Needham, MA 02492
4. Princeton School of Public and International Affairs, Princeton University, Princeton NJ 08544
5. Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA 15260
6. Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
Introduction
Sophisticated data collection requires sophisticated data analysis:
every neuroscientist now requires skills in computer programming,
statistics, and mathematics [1], [2]. However, most experimental
and clinical neuroscientists lack sufficient training in these skills,
and the idea of tackling a standard technical course or textbook on data
analysis without sufficient tutorials, examples, and hands-on practice
is daunting. Therefore, a fundamental gap has emerged in neuroscience
labs between highly sophisticated and rich data collection procedures,
and limited training to assess these data with state-of-the-art
quantitative techniques. While important to training the next generation
of neuroscientists, challenges exist in computational neuroscience
education [1], compounded by recent requirements for remote learning
and virtual classrooms [3].
To help address these challenges, we present here an online, freely
available resource to enhance training in neural data analysis:https://mark-kramer.github.io/Case-Studies-Python.
To reach the practicing neuroscientist, we assume only a basic knowledge
of calculus and statistics, common to those trained in biological
sciences [1]. We also invert the standard presentation of data
analysis techniques. Typical statistics textbooks in data analysis begin
with the development of theory and then describe applications. We
instead begin each topic with an example case study of neural data
(e.g., action potentials from rat hippocampus or scalp
electroencephalogram data recorded from a human subject). These data
then motivate the development and application of modern analysis
techniques (e.g., visualization approaches, spectral analysis,
bootstrapping, and generalized linear models). We emphasize a hands-on
approach; example data sets are provided, and computer (Python) code
interspersed within the material encourages direct interaction with the
concepts. The data, analysis methods and code are not toy examples, but
instead correspond directly with modern techniques in use today. Upon
completing each case study, the learner will be able to immediately
deploy these data analysis tools in his or her own research.