Continuous Attractor Neural Networks

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9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models
Oct 21, 2026Md Ali Ashraf Yad, Nazifa Tabassum NabilaContinuous Attractor Neural Networks Brain Network Analysis
Wrocław University of Science and Technology · Comilla University
A Spiking Ring-Attractor Architecture for Representation and Control of Revolute Robot Joints
Oct 19, 2026Aung Htet, Alejandro Rodriguez Jimenez, Ruidong Ma +1Continuous Attractor Neural Networks Robotic Control
Sheffield Hallam University, Sheffield, UK
Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory
Aug 3, 2026Zhaotian Gu, Jie Su, Weiwei Wang +3Recurrent Neural Networks Continuous Attractor Neural Networks
School of System Science, Beijing Normal University · Qiyuan Laboratory · State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
CANNs: A Toolkit for Research on Continuous Attractor Neural Networks
Jun 26, 2026Sichao He, Aiersi Tuerhong, Shangjun She +4Neural Representation Geometry Recurrent Neural Networks
School of Psychological and Cognitive Sciences, Peking–Tsinghua Center for Life Sciences, PKU-IDG/McGovern Institute for Brain Research, Center of Quantitative Biology, Peking University, Beijing, China · College of Mathematics and Statistics, Chongqing University, Chongqing, China
A Theory-grounded Hybrid Neural Network Integrating Complementary Estimation Mechanisms for Stable Visual Object TrackingA
Jun 21, 2026Yancheng Zhou, Hanle Zheng, Lei Deng +1Visual Object Tracking Continuous Attractor Neural Networks
Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China · Weixian College, Tsinghua University, Beijing, China · Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
Controlled Dynamics Attractor Transformer
Jun 13, 2026Cheng Zhang, Minnan Luo, Zesheng Yang +3Energy-Based Models Continuous Attractor Neural Networks
School of Computer Science and Technology, MOEK-LINNS Lab, Xi’an Jiaotong University, Xi’an, Shaanxi, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China
Symmetry, Defects, and Diffusion in Continuous-memory Recurrent Networks
May 5, 2026Hanson Hanxuan MoEquivariant Neural Networks Recurrent Neural Networks
Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
Apr 17, 2026Yanlin Zhang, Yan Zhang, Muhua Zheng +1Recurrent Neural Networks Continuous Attractor Neural Networks
School of physics and Electronic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China · Teaching Department, Jiangsu University Jingjiang College, Zhenjiang 212028, Jiangsu, China
Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation
Apr 15, 2026Federica Ferrari, Flavia Davidhi, Bernard Maacaron +6Continuous Attractor Neural Networks Robot State Estimation
Istituto Italiano di Tecnologia, Genoa, Italy · Department of Neurosurgery, University Hospital Zürich, Switzerland · Institute of Neuroinformatics, Univ. of Zurich and ETH Zurich, Switzerland +3

Oct 21, 2026J/K move · Enter open · S save

Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models

Md Ali Ashraf Yad, Nazifa Tabassum Nabila

Wrocław University of Science and Technology · Comilla University

Continuous Attractor Neural Networks Brain Network Analysis Neural Network Robustness

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Neural network models are increasingly constructed from synapse-resolution connectomes, and the same circuit is now available in reconstructions from other animals and pipelines. We do not know whether such models are dependent on the reconstruction they are built from, and whether connectome differences are biological or measurement errors. We studied the Drosophila head-direction circuit in nine versions: four flies (male CNS, hemibrain, FlyWire, BANC) and five variants of the same fly varying only by thresholds, proofreading or synapse detection. Using wiring-based correspondences validated by anatomical labels, we place all differences on one scale. A change of synapse detection within one fly changed the circuit more than halfway to the smallest between-fly difference on four distance measures; proofreading differences were smaller but depended on the metric, and left and right halves of one fly differed no more than random halves. Networks trained on each wiring to hold a cued heading developed near-perfect ring attractors, but their error increased from below 1 degree to 10-28 degrees when all weights changed by 1%. Training on each wiring did not transfer to any other connectome, including other versions of the same fly; refitting thirty cell-type gains, we repaired filtering-level differences but remaining error depended on wiring distance. Training with noise lowered precision and gave only partial transfer. In imaging data, the fly compass drifted gradually in darkness. Connectome-constrained models should report the reconstruction they were built on and be tested for robustness across reconstructions.