ArticlePloS one2014
Comparison of classifiers for decoding sensory and cognitive information from prefrontal neuronal populations.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Article
- Prefrontal Control of Proactive and Reactive Mechanisms of Visual Suppression.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior.Frontiers in neuroscience · 2022Review
- Solvable Model for the Linear Separability of Structured Data.Entropy (Basel, Switzerland) · 2021Article
- Auditory cues reveal intended movement information in middle frontal gyrus neuronal ensemble activity of a person with tetraplegia.Scientific reports · 2021Article
- Revisiting Persistent Neuronal Activity During Covert Spatial Attention.Frontiers in neural circuits · 2021Review
- Dynamic shifts of visual and saccadic signals in prefrontal cortical regions 8Ar and FEF.Journal of neurophysiology · 2020Article
- Article
- Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Generalizable knowledge outweighs incidental details in prefrontal ensemble code over time.eLife · 2017Article
- Decoding of top-down cognitive processing for SSVEP-controlled BMI.Scientific reports · 2016Article
- Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.Neural computation · 2016Article
- Prefrontal and anterior cingulate cortex neurons encode attentional targets even when they do not apparently bias behavior.Journal of neurophysiology · 2016Article
- Specific frontal neural dynamics contribute to decisions to check.Nature communications · 2016Article
- Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex.PLoS computational biology · 2016Article
- Single-Trial Decoding of Visual Attention from Local Field Potentials in the Primate Lateral Prefrontal Cortex Is Frequency-Dependent.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
- Differential dynamics of spatial attention, position, and color coding within the parietofrontal network.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
- Selective visual attention to drive cognitive brain-machine interfaces: from concepts to neurofeedback and rehabilitation applications.Frontiers in systems neuroscience · 2014Review
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.