Neural Decoding as Cognitive Inference

arXiv:2610.11923 · q-bio.NC, cs.AI · Submitted 2026-10-08 · Read on arXiv

q-bio.NC, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Accuracy — 79× in this paper · explained in 1 episode
• fNIRS — 14× in this paper · explained in 2 episodes
• Embeddings — 11× in this paper · explained in 2 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Cosine Similarity — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Structural Constraints — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Structural Control — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Compared with Neural Code Conversion — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes

Sources

• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

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