Image de couverture de livre

On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Xu, Dehong, Gao, Ruiqi, Zhang, Wen-Hao, Wei, Xue-Xin, Wu, Ying Nian
Format:	Preprint
Publié:	2024
Sujets:	Neurons and Cognition Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
par: Xu, Dehong, et autres
Publié: (2023)

Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planning
par: Zhao, Minglu, et autres
Publié: (2025)

A minimalistic representation model for head direction system
par: Zhao, Minglu, et autres
Publié: (2024)

If Grid Cells are the Answer, What is the Question? A Review of Normative Grid Cell Theory
par: Dorrell, William, et autres
Publié: (2026)

Determinantal Point Process Attention Over Grid Cell Code Supports Out of Distribution Generalization
par: Mondal, Shanka Subhra, et autres
Publié: (2023)

The Principle of Isomorphism: A Theory of Population Activity in Grid Cells and Beyond
par: Xu, Maoshen, et autres
Publié: (2025)

The Hippocampal Place Field Gradient: An Eigenmode Theory Linking Grid Cell Projections to Multiscale Learning
par: Zhou, Shujun, et autres
Publié: (2025)

Actionable Neural Representations: Grid Cells from Minimal Constraints
par: Dorrell, William, et autres
Publié: (2022)

The Geometry of Abstraction: Continual Learning via Recursive Quotienting
par: Li, Xin
Publié: (2025)

Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
par: Choi, Jungwon, et autres
Publié: (2024)

ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
par: Hsu, Hao-Lun, et autres
Publié: (2024)

State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
par: Lu, Sen, et autres
Publié: (2025)

On Design Choices in Similarity-Preserving Sparse Randomized Embeddings
par: Kleyko, Denis, et autres
Publié: (2024)

Learning High-Order Relationships of Brain Regions
par: Qiu, Weikang, et autres
Publié: (2023)

Lost in the Middle: An Emergent Property from Information Retrieval Demands in LLMs
par: Salvatore, Nikolaus, et autres
Publié: (2025)

Beyond Turing: Memory-Amortized Inference as a Foundation for Cognitive Computation
par: Li, Xin
Publié: (2025)

Memory-Amortized Inference: A Topological Unification of Search, Closure, and Structure
par: Li, Xin
Publié: (2025)

DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks
par: Wang, Xiang, et autres
Publié: (2025)

ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding
par: Piriyasatit, Suchanuch, et autres
Publié: (2025)

Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation
par: Faremi, Saheed, et autres
Publié: (2026)

Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behavior
par: Wang, Jiyi, et autres
Publié: (2025)

Bridging Foundation Models and Efficient Architectures: A Modular Brain Imaging Framework with Local Masking and Pretrained Representation Learning
par: Wang, Yanwen, et autres
Publié: (2025)

Dynamic Manifold Hopfield Networks for Context-Dependent Associative Memory
par: Li, Chong, et autres
Publié: (2025)

Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining
par: Bae, Sangyoon, et autres
Publié: (2025)

Probabilistic Recurrent Intention Switching Model
par: Sheng, Wenyuan, et autres
Publié: (2026)

A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders
par: Wei, Xinxu, et autres
Publié: (2025)

Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
par: Wang, Yule, et autres
Publié: (2023)

Distance by de-correlation: Computing distance with heterogeneous grid cells
par: Dasbehera, Pritipriya, et autres
Publié: (2025)

Generative forecasting of brain activity enhances Alzheimer's classification and interpretation
par: Gao, Yutong, et autres
Publié: (2024)

Functional dissociations versus post-hoc selection: Moving beyond the Stockart et al. (2025) compromise
par: Schmidt, Thomas, et autres
Publié: (2024)

SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
par: Aristimunha, Bruno, et autres
Publié: (2026)

Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment
par: Chen, Ruiqi, et autres
Publié: (2025)

DFORM: Diffeomorphic vector field alignment for assessing dynamics across learned models
par: Chen, Ruiqi, et autres
Publié: (2024)

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
par: Noman, Fuad, et autres
Publié: (2021)

Neural timescales from a computational perspective
par: Zeraati, Roxana, et autres
Publié: (2024)

Large language models reorganize representational geometry during in-context learning
par: Xiong, Hua-Dong, et autres
Publié: (2026)

Bayesian Active Learning for Discrete Latent Variable Models
par: Jha, Aditi, et autres
Publié: (2022)

Latent Representation Learning for Multimodal Brain Activity Translation
par: Afrasiyabi, Arman, et autres
Publié: (2024)

Hierarchical Latent Structure Learning through Online Inference
par: Aitsahalia, Ines, et autres
Publié: (2026)

When Representations Align: Universality in Representation Learning Dynamics
par: van Rossem, Loek, et autres
Publié: (2024)