arXiv Computer Vision
Can a Neural Encoding Model Replicate an fMRI Visualization Study?
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Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
arXiv:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
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AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
arXiv:2609.27729v1 Announce Type: cross Abstract: In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or...
arXiv Machine Learning
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Receptive-field-constrained stimulus optimization for human early and intermediate visual cortex
arXiv:2609.36391v1 Announce Type: cross Abstract: An ongoing challenge in sensory neuroscience is to characterize the feature dimensions encoded by cortical populations. Recent approaches probe featu...
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Jun 3
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv Machine Learning
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MAME: Multidimensional Adaptive Metamer Exploration with Human Perceptual Feedback
arXiv:2503. 13212v3 Announce Type: replace Abstract: Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning.
arXiv AI
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Versatile Framework with Semantic and Structural guidance for Image Reconstruction from Brain Activity
arXiv:2606. 00121v1 Announce Type: cross Abstract: Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task in brain decoding.