Neural Data Analysis Resources

This list is by no means exhaustive. I have left out countless amazing resources, researchers, etc.

Papers

I store all my papers on Zotero. I’ve yet to decide what I think are the most important of the hundreds of papers I’ve looked at, but will list them at some stage.

Researchers

• John P. Cunningham - Pushing the field of ensemble theory forward through arguments and simulations.
• Rafael Yuste - Focus on neural ensembles and testing them using optogenetics.
• Johnathon Pillow - Modelling, characterising neural pop responses, and extracting structure from high dim responses.
• Miguel Nicolelis - Mostly in BMI and neuroprosthetics, but was one of the OG people looking at neural ensembles and inter-regional communication as he was using to make BMIs.
• Juan Gallego - Does a lot of work on dimension reduction using manifolds.
• Gyorgy Buszaki - His work with David Tingley and his self authored publications tend to share wide ranging views on brain function and are very interesting.
• Marcus Raichle - Default mode, “dark energy”, and propagation of signals. FMRI mostly.
• Martin Vinck - Mostly focused on the visual system, so not all is relevant - but has good views on statistics more generally.

Some from Mark Humphries blog are: Johnathan Pillow; Christian Machens; Konrad Kording; Kanaka Rajan; John Cunningham; Adrienne Fairhall; Philip Berens; Cian O’Donnell; Il Memming Park; Jakob Macke; Gasper Tkacik; Oliver Marre. Um, me. Others are experimental labs with strong data science inclinations: Anne Churchland; Mark Churchland; Nicole Rust; Krishna Shenoy; Carlos Brody.

Journals

• Software Journals
• Quora List

Conferences

• Bernstein Compuational Neuroscience
• Computational and Systems Neuroscience
• UK Neural Computation Conference
• Neuromatch Academy

Books

• Computational Cognitive Neuroscience - freely available textbook with the source openly available on GitHub.
• Neuroscience Online - free online basic neuroscience book by the University of Texas.
• Statistics Done Wrong - a guide to the most popular statistical errors and slip-ups, with the replication crisis and I would argue that many of these are more well known than at the time of writing.
• Methods for Neural Ensemble Recordings - early book edited by Nicolelis.
• Theoretical Neuroscience - famous book by Dayan and Abbott.

Blogs

• The Spike - Mark Humphries blog on neural data analysis and more.

Podcasts

• Brain Inspired - Paul Middlebrooks fantastic podcast on AI and Neuroscience and their overlap.
• Brain Science - Virginia Campbell’s podcast, most recent 50 episodes are free.
• Brain Matters - University of Texas at Austin podcast on neuroscience.

Websites

• Awesome Neuroscience - another list of great resources, likely to be some overlap.
• Computational Neuroscience Researchers - a list of comp neuro researchers.
• Open Computational Neuroscience Resources - a comprehensive list of resources on GitHub.

Software

• Raphael Vallat - in particular, pingouin is a stats library with more detail than scipy provides, and entropy is a time-series complexity evaluator.
• LFPy - simulation from neuron models.
• Ning Leow’s list - online tools.
• DeepInsight - very accurate decoding without spike sorting.

Courses

• Data Analysis in Python - a good Duke University guide to programming in Python, covers basic and complex topics, such as parallelism and just in time compilation.

Groups

• The BRAIN Initiative - study how individual brain cells and complex neural circuits interact.
• Allen Institute for Brain Science.

Miscellaneous

• Chicago Leadership Lab - The craft of writing effectively, and also the writing beyond the academy lectures.

Photo by Jack Church on Unsplash