Saved in:
| Main Authors: | Qian, Daoyuan, Liang, Qiyao, Fiete, Ila |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.13707 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Delay Embedding Theory of Neural Sequence Models
by: Ostrow, Mitchell, et al.
Published: (2024)
by: Ostrow, Mitchell, et al.
Published: (2024)
Method for noise-induced regularization in quantum neural networks
by: Kuzmin, Viacheslav, et al.
Published: (2024)
by: Kuzmin, Viacheslav, et al.
Published: (2024)
Learning optimal integration of spatial and temporal information in noisy chemotaxis
by: Alonso, Albert, et al.
Published: (2023)
by: Alonso, Albert, et al.
Published: (2023)
Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
by: Zhang, Yanlin, et al.
Published: (2026)
by: Zhang, Yanlin, et al.
Published: (2026)
Fault-Tolerant Neural Networks from Biological Error Correction Codes
by: Zlokapa, Alexander, et al.
Published: (2022)
by: Zlokapa, Alexander, et al.
Published: (2022)
Provably robust learning of regression neural networks using $β$-divergences
by: Ghosh, Abhik, et al.
Published: (2026)
by: Ghosh, Abhik, et al.
Published: (2026)
InputDSA: Demixing then Comparing Recurrent and Externally Driven Dynamics
by: Huang, Ann, et al.
Published: (2025)
by: Huang, Ann, et al.
Published: (2025)
Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia
by: Pan, Haining, et al.
Published: (2026)
by: Pan, Haining, et al.
Published: (2026)
Experimental verification of the quantum nature of a neural network
by: Patrascu, Andrei T.
Published: (2022)
by: Patrascu, Andrei T.
Published: (2022)
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
by: Chavlis, Spyridon, et al.
Published: (2024)
by: Chavlis, Spyridon, et al.
Published: (2024)
Toward stochastic neural computing
by: Qi, Yang, et al.
Published: (2023)
by: Qi, Yang, et al.
Published: (2023)
Universality of reservoir systems with recurrent neural networks
by: Yasumoto, Hiroki, et al.
Published: (2024)
by: Yasumoto, Hiroki, et al.
Published: (2024)
Gated recurrent neural networks discover attention
by: Zucchet, Nicolas, et al.
Published: (2023)
by: Zucchet, Nicolas, et al.
Published: (2023)
Towards graph neural networks for provably solving convex optimization problems
by: Qian, Chendi, et al.
Published: (2025)
by: Qian, Chendi, et al.
Published: (2025)
When and Where: A Model Hippocampal Network Unifies Formation of Time Cells and Place Cells
by: Yu, Qiaorong S., et al.
Published: (2026)
by: Yu, Qiaorong S., et al.
Published: (2026)
Statistics of correlations in nonlinear recurrent neural networks
by: Mato, German, et al.
Published: (2025)
by: Mato, German, et al.
Published: (2025)
Learning fast changing slow in spiking neural networks
by: Capone, Cristiano, et al.
Published: (2024)
by: Capone, Cristiano, et al.
Published: (2024)
Designing deep neural networks for driver intention recognition
by: Vellenga, Koen, et al.
Published: (2024)
by: Vellenga, Koen, et al.
Published: (2024)
Learning richness modulates equality reasoning in neural networks
by: Tong, William L., et al.
Published: (2025)
by: Tong, William L., et al.
Published: (2025)
Investigating the generative dynamics of energy-based neural networks
by: Tausani, Lorenzo, et al.
Published: (2023)
by: Tausani, Lorenzo, et al.
Published: (2023)
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
by: Fong, Iat Hang, et al.
Published: (2025)
by: Fong, Iat Hang, et al.
Published: (2025)
Learn to integrate parts for whole through correlated neural variability
by: Zhu, Zhichao, et al.
Published: (2024)
by: Zhu, Zhichao, et al.
Published: (2024)
Three factor delay learning rules for spiking neural networks
by: Vassallo, Luke, et al.
Published: (2026)
by: Vassallo, Luke, et al.
Published: (2026)
Improved weight initialization for deep and narrow feedforward neural network
by: Lee, Hyunwoo, et al.
Published: (2023)
by: Lee, Hyunwoo, et al.
Published: (2023)
Spike-based computation using classical recurrent neural networks
by: De Geeter, Florent, et al.
Published: (2023)
by: De Geeter, Florent, et al.
Published: (2023)
Hypercomplex neural network in time series forecasting of stock data
by: Kycia, Radosław, et al.
Published: (2024)
by: Kycia, Radosław, et al.
Published: (2024)
Flexible inference for animal learning rules using neural networks
by: Liu, Yuhan Helena, et al.
Published: (2025)
by: Liu, Yuhan Helena, et al.
Published: (2025)
Optimal feature rescaling in machine learning based on neural networks
by: Vitrò, Federico Maria, et al.
Published: (2024)
by: Vitrò, Federico Maria, et al.
Published: (2024)
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
by: Fra, Vittorio
Published: (2025)
by: Fra, Vittorio
Published: (2025)
Fast gradient-free activation maximization for neurons in spiking neural networks
by: Pospelov, Nikita, et al.
Published: (2023)
by: Pospelov, Nikita, et al.
Published: (2023)
A rationale from frequency perspective for grokking in training neural network
by: Zhou, Zhangchen, et al.
Published: (2024)
by: Zhou, Zhangchen, et al.
Published: (2024)
Gradient-based inference of abstract task representations for generalization in neural networks
by: Hummos, Ali, et al.
Published: (2024)
by: Hummos, Ali, et al.
Published: (2024)
Effects of structural properties of neural networks on machine learning performance
by: Arya, Yash, et al.
Published: (2025)
by: Arya, Yash, et al.
Published: (2025)
Prototype-based interpretation of the functionality of neurons in winner-take-all neural networks
by: Sabzevar, Ramin Zarei, et al.
Published: (2020)
by: Sabzevar, Ramin Zarei, et al.
Published: (2020)
Self-orthogonalizing attractor neural networks emerging from the free energy principle
by: Spisak, Tamas, et al.
Published: (2025)
by: Spisak, Tamas, et al.
Published: (2025)
Measurement-driven neural-network training for integrated magnetic tunnel junction arrays
by: Borders, William A., et al.
Published: (2023)
by: Borders, William A., et al.
Published: (2023)
Modular Neural Computer
by: Leon, Florin
Published: (2026)
by: Leon, Florin
Published: (2026)
Role of scrambling and noise in temporal information processing with quantum systems
by: Xiong, Weijie, et al.
Published: (2025)
by: Xiong, Weijie, et al.
Published: (2025)
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
by: Pabian, Mateusz, et al.
Published: (2024)
by: Pabian, Mateusz, et al.
Published: (2024)
Modular Duality in Deep Learning
by: Bernstein, Jeremy, et al.
Published: (2024)
by: Bernstein, Jeremy, et al.
Published: (2024)
Similar Items
-
Delay Embedding Theory of Neural Sequence Models
by: Ostrow, Mitchell, et al.
Published: (2024) -
Method for noise-induced regularization in quantum neural networks
by: Kuzmin, Viacheslav, et al.
Published: (2024) -
Learning optimal integration of spatial and temporal information in noisy chemotaxis
by: Alonso, Albert, et al.
Published: (2023) -
Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
by: Zhang, Yanlin, et al.
Published: (2026) -
Fault-Tolerant Neural Networks from Biological Error Correction Codes
by: Zlokapa, Alexander, et al.
Published: (2022)