Cited by 1,219Neural dynamics and brain functionUndergraduate Neuroscience Education and ResearchNeural Networks and Applications

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Dimensionality reduction for large-scale neural recordings

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Cunningham, John P, Yu, Byron M·2014·Nature Neuroscience

DOI 10.1038/nn.3776PMC PMC4433019

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Abstract

Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.

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Authors: Cunningham, J. P., & Yu, B. M.
Affiliations: Columbia University (US)
Center for the Neural Basis of Cognition (US)
Corresponding author: Cunningham, John P
Yu, Byron M
ORCID: Cunningham, John P
Yu, Byron M
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2014-08-24
Volume: 17
Issue: 11
Pages: 1500-1509
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn.3776
PMC: PMC4433019
OpenAlex: W2017539895
Cited by: 1,219
References: 98
Citations by year: 2026: 141 · 2025: 137 · 2024: 135 · 2023: 116 · 2022: 123 · 2021: 115 · 2020: 85 · 2019: 103 · 2018: 82 · 2017: 82 · 2016: 50 · 2015: 41 · 2014: 9
FWCI: 5.85
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD071686)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Social Sciences › Education › Undergraduate Neuroscience Education and Research
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: dimensionality reduction, neural population activity, large-scale neural recording, population coding, single-trial analysis, statistical power, exploratory data analysis, systems neuroscience, method selection
MeSH terms: Animals, Data Collection, Humans, Nerve Net, Neurons, Statistics as Topic

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