Article Summary

“A Neural Data Science: How and Why” published March 26, 2018 by Mark Humphries. You can read it here.

At the core of the field of Neuroscience lies the question: how does the brain work? The human brain is comprised of billions of neurons that communicate by sending electrical and chemical signals to one another. The vast amount of data collected from neural recordings has led to an emerging field: neural data science. Using machine learning techniques, neural data can be explored to answer crucial questions regarding the collaboration and inter-connectedness of neurons in the brain.

Imagine you have n amount of neurons that you compile into a matrix with n columns and rows for every time-point at which data was collected. Using machine learning, this data can be modeled to answer specific systems-Neuroscience questions such as what is the structure of neural spikes? What causes the spikes? What are the inputs and outputs of these spikes?

Applications of Machine Learning for Systems Neuroscience

Through use of machine learning approaches and data science tools, Neuroscience big data has become incredibly accessible and promising for those interested in further studying the underlying principles of neural circuitry.

About the Author

Mark Humphries is theorist and neuroscientist who writes about the intersection of Neuroscience, data science, and artificial intelligence. In March of 2021, he published his book titled The Spike: An Epic Journey Through the Brain in 2.1 Seconds which you can purchase here.

Research Applications

Many Neuroscience-oriented research labs across the country are taking a data-science approach to further the understanding of how the brain works.

Neural Data Science (NerDS) Lab at Georgia Tech

Lab Website

This lab, headed by Eva L. Dyer, PhD., centers its research on the analyzing and interpretation of “complex neuroscience datasets” through machine learning.

Image source: A. Balwani+, J. Miano+, R. Liu, L. Kitchell, J.A. Prasad, E.C. Johnson, W. Gray-Roncal, E.L. Dyer, Multi-scale modeling of neural structure in X-ray imagery, to appear at the IEEE International Conf. on Image Processing (ICIP), 2021

Recent Papers:

A. Balwani+, J. Miano+, R. Liu, L. Kitchell, J.A. Prasad, E.C. Johnson, W. Gray-Roncal, E.L. Dyer, Multi-scale modeling of neural structure in X-ray imagery, to appear at the IEEE International Conf. on Image Processing (ICIP), 2021 (Paper)

C-H. Lin, M. Azabou, E.L. Dyer, Making transport more robust and interpretable by moving data through a small number of anchor points, to appear at the International Conference on Machine Learning (ICML), 2021

Neural Systems and Data Science Lab (NSDSLab) at University of California, Berkeley

Lab Website

This lab is led PI Kristofer Bouchard and develops data science tools to explore how neural circuits lead to behaviors and perception.

Image source: Sparse coding of ECoG signals identifies interpretable components for speech control in human sensorimotor cortex. Bouchard, K.E.*, Bujan, A.F., Chang, E.F., Sommer, F.T.; IEEE, EMBC, Aug., 2017.

Recent Papers

Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation. Sachdeva, P.S., Livezey, J.A., Dougherty, M.E., Gu, B.M., Berke, J.D., Bouchard, K.E., J. Neuroscience Mthds, 2021

Deep-learning as a data analysis tool for systems neuroscience. Livezey, J., Bouchard, K.E.\(, Chang, E.F.\); PLoS Computational Biology, 15(9): e1007091. Sept, 2019.

Personal Interest

As a Neuroscience major and Data Science minor, I am fascinated by the implications that neural data science has for the field of Neuroscience at large. The largely computational nature of the brain poses an interesting opportunity for the use of programming and machine learning in exploration. I think that models of neural signaling, such as those described by Humphries, may be the future of understanding many neurological disorders such as epilepsy.

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