שמור ב:
| מחבר ראשי: | |
|---|---|
| פורמט: | Recurso digital |
| שפה: | אנגלית |
| יצא לאור: |
Zenodo
2026
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| נושאים: | |
| גישה מקוונת: | https://doi.org/10.5281/zenodo.20129339 |
| תגים: |
הוספת תג
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תוכן הענינים:
- <h2>Resilient functioning in the face of sensory failure is a characteristic of biological systems, but remains a challenge for artificial neural networks. We subjected a compact recurrent network (262k parameters) to one hour of total sensory deprivation coupled with a block on plasticity, followed by the restoration of both inputs and plasticity. Using fractal metrics (Hurst exponent H and fractal dimension D), global uncertainty, and a consistency index C, we discovered two phenomenological laws governing post-deprivation recovery.</h2> <ul> <li> <p>AUNI Law</p> </li> <li> <p>Law of SCO</p> </li> </ul> <p><strong>AUNI's law describes a rapid exponential restoration of the internal fractal structure (τ ≈ 5.3 min for D, and τ ≈ 5.8 min for uncertainty).<br><br>On the other hand, SCO's law reveals a strong inertia in the predictive consistency of system C, which remains stable during deprivation and recovers only very slowly (τ > 12 min).<br><br>This dual dynamic—rapid microstructural repair versus slow macrocognitive adaptation—demonstrates a functional dissociation within the same artificial cognitive system. Our results provide the first quantitative experimental validation of such a dissociation in a recurrent neural network, opening new avenues for the design of resilient and self-repairing AI. This represents a complete paradigm shift.</strong></p>