Cover Image

A Quotient Homology Theory of Representation in Neural Networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Beshkov, Kosio
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Algebraic Topology Neurons and Cognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A rank decomposition for the topological classification of neural representations
by: Beshkov, Kosio, et al.
Published: (2024)

Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
by: Chung, Moo K., et al.
Published: (2022)

Persistent Homology on a lattice of multigraphs
by: Boils, Joaquin Diaz
Published: (2025)

Zigzag Persistence of Neural Responses to Time-Varying Stimuli
by: Gardinazzi, Yuri, et al.
Published: (2026)

Tracking the topology of neural manifolds across populations
by: Yoon, Iris H. R., et al.
Published: (2025)

Topological decoding of grid cell activity via path lifting to covering spaces
by: Yao, Yuxing Jared, et al.
Published: (2025)

Analyzing Brain Tumor Connectomics using Graphs and Persistent Homology
by: Bhattacharya, Debanjali, et al.
Published: (2024)

Topologically Invariant Permutation Test
by: Dakurah, Sixtus
Published: (2025)

Topological Representational Similarity Analysis in Brains and Beyond
by: Lin, Baihan
Published: (2024)

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

Using topological data analysis to compare inter-subject variability across resting state functional MRI brain representations
by: Easley, Ty, et al.
Published: (2023)

Estimating Dimensionality of Neural Representations from Finite Samples
by: Chun, Chanwoo, et al.
Published: (2025)

Neural-Behavioral Representation of Natural Whole-body Movement in Monkeys
by: He, Jieshi, et al.
Published: (2026)

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

NeuroBind: Towards Unified Multimodal Representations for Neural Signals
by: Yang, Fengyu, et al.
Published: (2024)

Estimating Neural Representation Alignment from Sparsely Sampled Inputs and Features
by: Chun, Chanwoo, et al.
Published: (2025)

Integral Betti signature confirms the hyperbolic geometry of brain, climate, and financial networks
by: Caputi, Luigi, et al.
Published: (2024)

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

Topological Sensitivity in Connectome-Constrained Neural Networks
by: Dhiman, Nalin
Published: (2026)

Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
by: Xu, Dehong, et al.
Published: (2023)

Causal Interpretation of Neural Network Computations with Contribution Decomposition
by: Melander, Joshua Brendan, et al.
Published: (2026)

CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
by: Zheng, Kaizhong, et al.
Published: (2023)

Training Large Neural Networks With Low-Dimensional Error Feedback
by: Hanut, Maher, et al.
Published: (2025)

Lattice-Based Pruning in Recurrent Neural Networks via Poset Modeling
by: Sengupta, Rakesh
Published: (2025)

Algorithm Development in Neural Networks: Insights from the Streaming Parity Task
by: van Rossem, Loek, et al.
Published: (2025)

Walks with jumps: a neurobiologically motivated class of paths in the hyperbolic plane
by: DeBlois, Jason, et al.
Published: (2024)

When Representations Align: Universality in Representation Learning Dynamics
by: van Rossem, Loek, et al.
Published: (2024)

Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networks
by: Paul, Ankita, et al.
Published: (2026)

HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses
by: Deng, Zhichao, et al.
Published: (2025)

Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment
by: Yu, Chengting, et al.
Published: (2025)

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
by: Qu, Gang, et al.
Published: (2024)

The Umwelt Representation Hypothesis: Rethinking Universality
by: Bosch, Victoria, et al.
Published: (2026)

Exploratory Experience Shapes the Geometry of Predictive Representations
by: Shilova, Kseniia, et al.
Published: (2026)

Unified Generative Latent Representation for Functional Brain Graphs
by: Abulikemu, Subati, et al.
Published: (2025)

Criticality Analysis: Bio-inspired Nonlinear Data Representation
by: Scheper, Tjeerd V. olde
Published: (2023)

Latent Representation Learning for Multimodal Brain Activity Translation
by: Afrasiyabi, Arman, et al.
Published: (2024)

Representation biases: will we achieve complete understanding by analyzing representations?
by: Lampinen, Andrew Kyle, et al.
Published: (2025)

Multi-intention Inverse Q-learning for Interpretable Behavior Representation
by: Zhu, Hao, et al.
Published: (2023)

Bridging Theory and Practice in Crafting Robust Spiking Reservoirs
by: Freddi, Ruggero, et al.
Published: (2026)

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
by: Banville, Hubert, et al.
Published: (2026)