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| Format: | Recurso digital |
| Language: | English |
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2026
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| Online Access: | https://doi.org/10.5281/zenodo.20129339 |
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| author | SCOGNAMIGLIO, Remi |
| author_facet | SCOGNAMIGLIO, Remi |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20129339 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Phenomenological resilience laws in SR-RNN architectures: Dissociation between fractal restoration (AUNI) and coherence inertia (SCO) SCOGNAMIGLIO, Remi sco auni sr-rnn iovane fractal processor Loi de SCO loi d'auni Double cinétique de récupération Résilience phénoménologique Privation sensorielle numérique Conscious Fractal Processor (CFP v2.1) cfp Conscious Fractal Processor Dimension fractale Exposant de Hurst Allostasie numérique Intelligence Artificielle bio-inspirée Systèmes critiques et autonomes AUNI Law SCO Law Dual recovery kinetics Self-Referential RNN Phenomenological resilience Sensory deprivation Fractal Dimension (D) Hurst Exponent (H) Allostatic plasticity Predictive coherence Bio-inspired AI ai intelligence artificielle Autonomous agent survival Sophimatics principles Sophimatics <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> |
| title | Phenomenological resilience laws in SR-RNN architectures: Dissociation between fractal restoration (AUNI) and coherence inertia (SCO) |
| topic | sco auni sr-rnn iovane fractal processor Loi de SCO loi d'auni Double cinétique de récupération Résilience phénoménologique Privation sensorielle numérique Conscious Fractal Processor (CFP v2.1) cfp Conscious Fractal Processor Dimension fractale Exposant de Hurst Allostasie numérique Intelligence Artificielle bio-inspirée Systèmes critiques et autonomes AUNI Law SCO Law Dual recovery kinetics Self-Referential RNN Phenomenological resilience Sensory deprivation Fractal Dimension (D) Hurst Exponent (H) Allostatic plasticity Predictive coherence Bio-inspired AI ai intelligence artificielle Autonomous agent survival Sophimatics principles Sophimatics |
| url | https://doi.org/10.5281/zenodo.20129339 |