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Europe PubMed Central
Article . 2013
Data sources: PubMed Central
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A multivariate method to determine the dimensionality of neural representation from population activity

Authors: Diedrichsen, Jörn; Wiestler, Tobias; Ejaz, Naveed;

A multivariate method to determine the dimensionality of neural representation from population activity

Abstract

How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches.

Highlights • Neural population activity represents external variables using multiple feature dimensions. • We present a general method to determine the dimensionality of this feature space. • Primary motor cortex represents force through a low-dimensional feature space. • Individual finger movements are represented using a four-dimensional feature space.

Country
Canada
Subjects by Vocabulary

Microsoft Academic Graph classification: Multivariate statistics Computer science Gaussian computer.software_genre Brain mapping education.field_of_study medicine.diagnostic_test symbols Primary motor cortex Curse of dimensionality Feature vector Population Machine learning symbols.namesake medicine education business.industry Pattern recognition Artificial intelligence business Functional magnetic resonance imaging computer Classifier (UML)

Keywords

Male, Primary motor cortex, Computer-Assisted, Models, Multivoxel pattern analysis, Psychology, Neurons, Brain Mapping, Brain, Representational models, Magnetic Resonance Imaging, Neurology, Neurological, Female, Algorithms, Cognitive Neuroscience, Models, Neurological, Article, Young Adult, Image Interpretation, Computer-Assisted, Humans, Image Interpretation, Neurosciences

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    impulse
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    Top 10%
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    33
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Average
Top 10%
Funded by
WT| Core support for the Wellcome Trust Centre for Neuroimaging
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 091593
  • Funding stream: Neuroscience and Mental Health
,
WT| Learning and recovery of skilled finger movements.
Project
  • Funder: Wellcome Trust (WT)
  • Project Code: 094874
  • Funding stream: Neuroscience and Mental Health
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