High-rank connectivity scaffolds support precision and generalisation in recurrent neural networks

arXiv:2609.35207 · q-bio.NC · Submitted 2026-09-28 · Read on arXiv

q-bio.NC

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Accuracy — 9× in this paper · explained in 1 episode
• Core — 6× in this paper · explained in 2 episodes
• Latent Dynamics — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Activation Function — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Action Decoder — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
• Proximal Policy Optimization Algorithms
• Connectivity structure and dynamics of nonlinear recurrent neural networks
• High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
• OMiSO: Adaptive optimization of state-dependent brain stimulation to shape neural population states
• Capacity and Trainability in Recurrent Neural Networks
• Universality and individuality in neural dynamics across large populations of recurrent networks
• Captum: A unified and generic model interpretability library for PyTorch
• Axiomatic Attribution for Deep Networks

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