Relating Population-Code Representations between Man, Monkey, and Computational Models.
about
Spikes, BOLD, attention, and awareness: a comparison of electrophysiological and fMRI signals in V1Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and futureDeep supervised, but not unsupervised, models may explain IT cortical representationNeural population coding: combining insights from microscopic and mass signalsLeft ear advantage in speech-related dichotic listening is not specific to auditory processing disorder in children: A machine-learning fMRI and DTI study.Hippocampal activity patterns carry information about objects in temporal contextFirst steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage.Generic decoding of seen and imagined objects using hierarchical visual features.How does a child solve 7 + 8? Decoding brain activity patterns associated with counting and retrieval strategies.Familiarization: A theory of repetition suppression predicts interference between overlapping cortical representations.Shape effects on reflexive spatial attention are driven by the dorsal stream.Decoding working memory of stimulus contrast in early visual cortex.What the success of brain imaging implies about the neural code.Quantifying cerebral contributions to pain beyond nociception.Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.Using goal-driven deep learning models to understand sensory cortex.Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulationFeedforward object-vision models only tolerate small image variations compared to human.Is neuroimaging measuring information in the brain?Visual representations are dominated by intrinsic fluctuations correlated between areas.Visual object categorization in birds and primates: integrating behavioral, neurobiological, and computational evidence within a "general process" framework.Population coding and the labeling problem: extrinsic versus intrinsic representations.Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.The brain of the beholder: honouring individual representational idiosyncrasies
P2860
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P2860
Relating Population-Code Representations between Man, Monkey, and Computational Models.
description
2009 nî lūn-bûn
@nan
2009 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@ast
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@en
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@nl
type
label
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@ast
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@en
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@nl
prefLabel
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@ast
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@en
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@nl
P2860
P1476
Relating Population-Code Representations between Man, Monkey, and Computational Models.
@en
P2860
P304
P356
10.3389/NEURO.01.035.2009
P577
2009-12-15T00:00:00Z