Imperial College London
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ProfessorSimonSchultz

Faculty of Engineering, Department of Bioengineering

Professor of Neurotechnology
 
 
 
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Contact

 

s.schultz Website

 
 
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Location

 

4.11Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mitchell-Heggs:2022:10.1007/s10827-022-00839-3,
author = {Mitchell-Heggs, R and Prado, S and Gava, G and Go, MA and Schultz, S},
doi = {10.1007/s10827-022-00839-3},
journal = {Journal of Computational Neuroscience},
pages = {1--21},
title = {Neural manifold analysis of brain circuit dynamics in health and disease},
url = {http://dx.doi.org/10.1007/s10827-022-00839-3},
volume = {51},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as “neural manifolds”, and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer’s Disease, and speculate that neural manif
AU  - Mitchell-Heggs,R
AU  - Prado,S
AU  - Gava,G
AU  - Go,MA
AU  - Schultz,S
DO  - 10.1007/s10827-022-00839-3
EP  - 21
PY  - 2022///
SN  - 0929-5313
SP  - 1
TI  - Neural manifold analysis of brain circuit dynamics in health and disease
T2  - Journal of Computational Neuroscience
UR  - http://dx.doi.org/10.1007/s10827-022-00839-3
UR  - https://link.springer.com/article/10.1007/s10827-022-00839-3
UR  - http://hdl.handle.net/10044/1/101199
VL  - 51
ER  -
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