Papers

• Optimal stimulation sites are not the most affected: personalised models of resting-state fMRI in Alzheimer's disease (2026)
  Cristiano Capone et al.
  5.49
• Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift (2026)
  Robin Holzinger (Department of Electrical Engineering and Computer Sciences et al.
  4.39
• High-entropy Advantage in Neural Networks' Generalizability (2025)
  Entao Yang et al.
  3.64
• How the fly holds a single goal: normalization, not selection, in Drosophila FC2 (2026)
  Gioele Nanni et al.
  3.51
• How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function (2026)
  Paul M. Thompson
  3.34
• More Electrodes, Faster Minds? Rethinking Bandwidth in Brain-Computer Interfaces (2026)
  Boxuan Jiang
  2.00
• Foundation Models for EEG Are Blind to Long-Range Temporal Correlations: A Spectral-Temporal Dissociation Behind Their Cross-Population Fragility (2026)
  Marzieh Zare
  2.00
• A Neural Network model of Cultural Evolution (2026)
  Kingsley J. A. Cox et al.
  2.00
• Mechanisms of Width Scaling in Normalized Residual Networks: The Effective Alignment Dimension (2026)
  Jinhao Zhang et al.
  2.00
• When Branch-Local Shunting Helps: A Gain-Load-Alignment Principle for Dendritic E/I Networks (2026)
  Houman Safaai et al.
  2.00
• Are the High-weight Neurons the Important Ones in Image Classification Neural Networks? (2026)
  Qitao Chen et al.
  2.00
• Multi-Scale Temporal Homeostasis Enables Efficient and Robust Neural Networks (2026)
  MD Azizul Hakim
  1.72
• Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems (2026)
  Benedikt Hartl et al.
  1.67
• FAME: Adaptive Functional Attention with Expert Routing for Function-on-Function Regression (2025)
  Yifei Gao et al.
  1.50
• Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study (2025)
  Spyros Rigas et al.
  1.44
• Kuramoto Orientation Diffusion Models (2025)
  Yue Song et al.
  1.44
• Meta-Learning Theory-Informed Inductive Biases using Deep Kernel Gaussian Processes (2025)
  Bahti Zakirov and Ga\v{s}per Tka\v{c}ik
  1.44
• Langevin Flows for Modeling Neural Latent Dynamics (2025)
  Yue Song et al.
  1.33
• How important are activation functions in regression and classification? A survey, performance comparison, and future directions (2022)
  Ameya D. Jagtap and George Em Karniadakis
  —
• Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories (2024)
  Yicheng Li et al.
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