Cross-Modal Neural Interpretation Modulation: Acoustic Encoding for Ill-Conditioned Sensory Channels
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Description
This work extends Neural Interpretation Modulation (NIM) into a Cross-Modal Residual Framework for handling ill-conditioned sensory channels. It introduces a structured acoustic encoding approach that populates the null space of degraded visual mappings, enabling inference stability under conditions such as photophobic saturation or extreme glare.
The framework integrates:
Ill-conditioned inverse problems and condition number analysis
Cross-modal residual decomposition
Hysteresis-driven modality switching
Information geometry via Fisher Information metrics
Stochastic resonance and the Davidson Peak optimal noise regime
Predictive processing and neural oscillatory alignment
Rather than binary sensory substitution, the model treats cross-modal encoding as a complementary reconstruction pathway that preserves inference fidelity when primary channels degrade.
This preprint is part of a broader research program on scale-dependent quantum/inference systems and environmental information encoding (NIM framework).
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References
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