Copertina

Transformer brain encoders explain human high-level visual responses

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Adeli, Hossein, Minni, Sun, Kriegeskorte, Nikolaus
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Neurons and Cognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

The Topology and Geometry of Neural Representations
di: Lin, Baihan, et al.
Pubblicazione: (2023)

In Silico Mapping of Visual Categorical Selectivity Across the Whole Brain
di: Hwang, Ethan, et al.
Pubblicazione: (2025)

Separating the what and how of compositional computation to enable reuse and continual learning
di: Shan, Haozhe, et al.
Pubblicazione: (2025)

Growing a Neural Network in Breadth, Depth, and Time
di: Butkus, Eivinas, et al.
Pubblicazione: (2026)

Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
di: Nan, Mu, et al.
Pubblicazione: (2026)

Human-like Object Grouping in Self-supervised Vision Transformers
di: Adeli, Hossein, et al.
Pubblicazione: (2026)

Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex
di: Yu, Muquan, et al.
Pubblicazione: (2025)

Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations
di: Lippl, Samuel, et al.
Pubblicazione: (2024)

How to optimize neuroscience data utilization and experiment design for advancing brain models of visual and linguistic cognition?
di: Tuckute, Greta, et al.
Pubblicazione: (2024)

Scaling and context steer LLMs along the same computational path as the human brain
di: Raugel, Joséphine, et al.
Pubblicazione: (2025)

Sequence-to-Sequence Models with Attention Mechanistically Map to the Architecture of Human Memory Search
di: Salvatore, Nikolaus, et al.
Pubblicazione: (2025)

Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMs
di: Salvatore, Nikolaus, et al.
Pubblicazione: (2025)

An explainable framework for the relationship between dementia and glucose metabolism patterns
di: Vázquez-García, C., et al.
Pubblicazione: (2026)

Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
di: Marin-Llobet, Arnau, et al.
Pubblicazione: (2025)

Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
di: Roffet, Facundo, et al.
Pubblicazione: (2024)

Population Transformer: Learning Population-level Representations of Neural Activity
di: Chau, Geeling, et al.
Pubblicazione: (2024)

Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
di: Tchetchenian, Ari, et al.
Pubblicazione: (2024)

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
di: Lo, Yui, et al.
Pubblicazione: (2024)

Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance
di: Asrari, Hojjat Azimi, et al.
Pubblicazione: (2025)

Generative forecasting of brain activity enhances Alzheimer's classification and interpretation
di: Gao, Yutong, et al.
Pubblicazione: (2024)

Ensemble-based graph representation of fMRI data for cognitive brain state classification
di: Vlasenko, Daniil, et al.
Pubblicazione: (2025)

HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
di: Jalali, Parnian, et al.
Pubblicazione: (2023)

Neuro-Symbolic Decoding of Neural Activity
di: Wang, Yanchen, et al.
Pubblicazione: (2026)

Group-specific discriminant analysis reveals statistically validated sex differences in lateralization of brain functional network
di: Zhou, Shuo, et al.
Pubblicazione: (2024)

EEG-to-fMRI synthesis of task-evoked and spontaneous brain activity: addressing issues of statistical significance and generalizability
di: Mehta, Neil, et al.
Pubblicazione: (2025)

Deep probabilistic model synthesis enables unified modeling of whole-brain neural activity across individual subjects
di: Bishop, William E., et al.
Pubblicazione: (2026)

Modeling Visual Hallucination: A Generative Adversarial Network Framework
di: Zareh, Masoumeh, et al.
Pubblicazione: (2021)

Toward Efficient Spiking Transformers: Synapse Pruning Meets Synergistic Learning-Based Compensation
di: Sun, Hongze, et al.
Pubblicazione: (2025)

Compressed representation of brain genetic transcription
di: Ruffle, James K, et al.
Pubblicazione: (2023)

A flexible Bayesian non-parametric mixture model reveals multiple dependencies of swap errors in visual working memory
di: Radmard, Puria, et al.
Pubblicazione: (2025)

How does the primate brain combine generative and discriminative computations in vision?
di: Peters, Benjamin, et al.
Pubblicazione: (2024)

Linguistic properties and model scale in brain encoding: from small to compressed language models
di: Oota, Subba Reddy, et al.
Pubblicazione: (2026)

Allometric scaling of brain activity explained by avalanche criticality
di: Simões, Tiago S. A. N., et al.
Pubblicazione: (2025)

Generating Visual Stimuli from EEG Recordings using Transformer-encoder based EEG encoder and GAN
di: Mishra, Rahul, et al.
Pubblicazione: (2024)

Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
di: Ahmadi, Sana, et al.
Pubblicazione: (2024)

Sparks of cognitive flexibility: self-guided context inference for flexible stimulus-response mapping by attentional routing
di: Sommers, Rowan P., et al.
Pubblicazione: (2025)

SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
di: Le, Trung, et al.
Pubblicazione: (2025)

Neural encoding with affine feature response transforms
di: Le, Lynn, et al.
Pubblicazione: (2025)

ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
di: Piriyasatit, Suchanuch, et al.
Pubblicazione: (2025)

Predicting Cognition from fMRI:A Comparative Study of Graph, Transformer, and Kernel Models Across Task and Rest Conditions
di: Patel, Jagruti, et al.
Pubblicazione: (2025)