NeuroSketch: A Practical Design Recipe for Neural Decoding

arXiv:2512.09524 · q-bio.NC, cs.AI, cs.LG, eess.SP · Submitted 2025-12-10 · Read on arXiv

q-bio.NC, cs.AI, cs.LG, eess.SP

Submitted: 2025-12-10

Updated: 2026-09-16

Code: https://github.com/Galaxy-Dawn/NeuroSketch

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 47× in this paper · explained in 2 episodes
• Accuracy — 36× in this paper · explained in 1 episode
• Foundation Model — 8× in this paper · explained in 3 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Feature Extraction — 7× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Brain-Computer Interface — 4× in this paper
• Generalization — 4× in this paper · explained in 13 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• 1D CNN — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Data Augmentation — 2× in this paper · explained in 7 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Hybrid Architectures — 2× in this paper · explained in 3 episodes
• Hybrid Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Time Series Forecasting — 2× in this paper · explained in 3 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• MultiGrain: a unified image embedding for classes and instances
• Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
• Why Attention Fails: The Degeneration of Transformers into MLPs in Time Series Forecasting

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