Multi-Level Optimal Transport (MOT) jointly infers soft layer couplings and neuron transport plans to produce global alignment scores and structured hierarchical correspondences between networks of varying depths.
Deep supervised, but not unsupervised, models may explain IT cortical representation
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MIRAGE uses adaptive multimodal gating on native multimodal backbones plus a transformer encoder to achieve state-of-the-art whole-brain fMRI prediction for naturalistic audiovisual stimuli, outperforming post-hoc unimodal aggregation.
Backpropagated gradients from vision models predict higher visual cortex signals but diverge from brain hierarchies in spatial and temporal organization.
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
RSA on 7T fMRI during natural scene viewing identifies ventromedial and lateral occipitotemporal representational routes for scene context versus animate content, with differential alignment to vision and language models.
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.
citing papers explorer
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Representational Alignment Across Model Layers and Brain Regions with Multi-Level Optimal Transport
Multi-Level Optimal Transport (MOT) jointly infers soft layer couplings and neuron transport plans to produce global alignment scores and structured hierarchical correspondences between networks of varying depths.
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MIRAGE: Adaptive Multimodal Gating for Whole-Brain fMRI Encoding
MIRAGE uses adaptive multimodal gating on native multimodal backbones plus a transformer encoder to achieve state-of-the-art whole-brain fMRI prediction for naturalistic audiovisual stimuli, outperforming post-hoc unimodal aggregation.
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Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Backpropagated gradients from vision models predict higher visual cortex signals but diverge from brain hierarchies in spatial and temporal organization.
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Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
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Zero-shot World Models Are Developmentally Efficient Learners
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
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Shared representations in brains and models reveal a two-route cortical organization during scene perception
RSA on 7T fMRI during natural scene viewing identifies ventromedial and lateral occipitotemporal representational routes for scene context versus animate content, with differential alignment to vision and language models.
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Dual-Stream EEG Decoding for 3D Visual Perception
Dual-stream EEG decoder separates identity and orientation to support 3D reconstruction from neural signals via circular regression and conditioned diffusion.