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Estimating the dimensionality of the manifold underlying multi-electrode neural recordings
- Source :
- PLoS Computational Biology, Vol 17, Iss 11, p e1008591 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- It is generally accepted that the number of neurons in a given brain area far exceeds the number of neurons needed to carry any specific function controlled by that area. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating the dimensionality of linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms’ accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding of the low-dimensional manifold within the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated the dimensionality. We thus developed a denoising algorithm based on deep learning, the “Joint Autoencoder”, which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the intrinsic dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data.<br />Author summary The number of neurons that we can simultaneously record from has increased exponentially for decades; today that number is in the thousands of neurons. However, the individual firing rates are highly redundant. One approach to identifying important features from redundant data is to estimate the dimensionality of the neural recordings, which represents the number of degrees of freedom required to describe the data without significant information loss. Better understanding of dimensionality may also uncover the mechanisms of computation within a neural circuit. Circuits carrying out complex computations might be higher-dimensional than those carrying out simpler computations. Typically, studies have quantified neural dimensionality using one of several available methods reflecting a lack of consensus on which method would be most appropriate for neural data. In this work, we used several methods to investigate the accuracy of simulated neural data with properties mimicking those of actual neural recordings. Based on these results, we devised an analysis pipeline to estimate the dimensionality of neural recordings. Our work will allow scientists to extract informative features from a large number of highly redundant neurons, as well as quantify the complexity of information encoded by these neurons.
- Subjects :
- Computer science
Signal-To-Noise Ratio
Machine Learning
Mathematical and Statistical Techniques
Redundancy (information theory)
Animal Cells
Biology (General)
Neurons
Brain Mapping
Likelihood Functions
Principal Component Analysis
education.field_of_study
Maximum Likelihood Estimation
Ecology
Applied Mathematics
Simulation and Modeling
Statistics
Brain
Haplorhini
Manifold
Computational Theory and Mathematics
Modeling and Simulation
Physical Sciences
Principal component analysis
Engineering and Technology
Embedding
Cellular Types
Monte Carlo Method
Algorithms
Research Article
Curse of dimensionality
Computer and Information Sciences
QH301-705.5
Computation
Models, Neurological
Population
Research and Analysis Methods
Machine Learning Algorithms
Cellular and Molecular Neuroscience
Artificial Intelligence
Robustness (computer science)
Computational Techniques
Genetics
Animals
Humans
Computer Simulation
Statistical Methods
Signal to Noise Ratio
education
Electrodes
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Quantitative Biology::Neurons and Cognition
business.industry
Deep learning
Computational Pipelines
Computational Biology
Biology and Life Sciences
Eigenvalues
Pattern recognition
Cell Biology
Autoencoder
Electrophysiological Phenomena
Nonlinear system
Algebra
Nonlinear Dynamics
Linear Algebra
Cellular Neuroscience
Multivariate Analysis
Signal Processing
Linear Models
Noise (video)
Artificial intelligence
business
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....8ed823d7c90d6d8bbc31c5c9a8432205