Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

, , ,
1 Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
2 Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
3 School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
4 Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
5 Department of Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
6 Department of Bioengineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
7 Department of Electrical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
8 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
DOI
10.7287/peerj.preprints.27340v2
Published
Accepted
Subject Areas
Neuroscience, Data Mining and Machine Learning
Keywords
Neural populations, network models, dimensionality reduction
Copyright
© 2019 Williamson et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Williamson RC, Doiron B, Smith MA, Yu BM. 2019. Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction. PeerJ Preprints 7:e27340v2

Abstract

A long-standing goal in neuroscience has been to bring together neuronal recordings and neural network modeling to understand brain function. Neuronal recordings can inform the development of network models, and network models can in turn provide predictions for subsequent experiments. Traditionally, neuronal recordings and network models have been related using single-neuron and pairwise spike train statistics. We review here recent studies that have begun to relate neuronal recordings and network models based on the multi-dimensional structure of neuronal population activity, as identified using dimensionality reduction. This approach has been used to study working memory, decision making, motor control, and more. Dimensionality reduction has provided common ground for incisive comparisons and tight interplay between neuronal recordings and network models.

Author Comment

This version includes some clarification of wording and additional citations. Several punctuation and stylistic errors were also corrected.