Article

Neural prediction decorrelation reveals that adversarial robustness substantially improves DNN prediction accuracy across the entire human auditory cortex

2026-08-11

Abstract excerpt

Sensory neuroscientists seek to model the neural computations that encode complex stimuli. Distinct encoding models often make similar predictions for natural stimuli such as speech, posing a challenge for model comparison. We developed a method to synthesize stimuli that decorrelate model predictions across a neural population, termed neural prediction decorrelation (NPD). Using fMRI responses to NPD sounds, we c...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d3e2034e-8630-5101-bb27-01c90797720d
DOI: 10.64898/2026.08.05.743059

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Neural prediction decorrelation reveals that adversarial robustness substantially improves DNN prediction accuracy across the entire human auditory cortexDOI 10.64898/2026.08.05.743059Read

Investigating Invariances in Auditory Event Categorization with Model MetamersSemantic proximity Read

Model metamers illuminate divergences between biological and artificial neural networksSemantic proximity Read

Bidirectional Generative Adversarial Representation Learning for Natural Stimulus SynthesisSemantic proximity Read

End-to-end deep image reconstruction from human brain activitySemantic proximity Read

Automatic musical predictions in the brain as indexed by the Mismatch Negativity -- From acoustic deviants to cognitive musical errorsSemantic proximity Read

Stochastic Surprise Signatures in Human EEG: A Reproducible Benchmark of Prediction-Error Models Using the ERP CORE Oddball DatasetSemantic proximity Read

Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial RobustnessSemantic proximity Read

Cortical response to naturalistic stimuli is largely predictable with deep neural networksSemantic proximity Read

Natural speech re-synthesis from direct cortical recordings using a pre-trained encoder-decoder frameworkSemantic proximity Read

Toward Generating Physiologically Plausible Artificial Electroencephalography Data: Effects of Convolution-based Upsampling Methods on Data QualitySemantic proximity Read

Joint Generative Modeling of EEG and fMRI for Cognitive Task Simulation Using Adversarial LearningSemantic proximity Read

Inferring the basis of binaural detection with a modified autoencoderSemantic proximity Read