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Dimensionality reduction for large-scale neural recordings
Open accessAbstract
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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“Analysis of intrinsic dimensionality (44) (Materials and Methods) during decision-making (Fig. 2M) confirms the high-dimensional neural coding during epoch 1 that collapses rapidly to just a single dimension following the FT during epochs 2, 3, and 4.”
From Neural correlates of perceptual decision-making in the primary somatosensory cortex (2026) · cites this paper as [44]
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Metadata
- 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
- Venue
- Nature Neuroscience
- Published
- 2014-08-24
- Volume
- 17
- Issue
- 11
- Pages
- 1500-1509
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- 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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