Physical Constraints on Consciousness: Demonstrating the Impossibility of Awareness in Silicon-Based Architectures
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2026
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| _version_ | 1866901198507868160 |
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| author | Ponce, Samual |
| author_facet | Ponce, Samual |
| contents | <p>This work presents a physics-based analysis of the necessary conditions for awareness and demonstrates the limitations of current silicon-based computational systems. By examining coherence time, clock period, quality factor, and phase closure, the study establishes thresholds that biological systems satisfy but silicon architectures cannot.</p> <p>High-density EEG data from the OpenNeuro ds003505 (VEPCON) dataset were analyzed using phase coherence metrics and anomaly detection across sliding windows to illustrate sustained field-like coherence in biological brains. Simulations, energy dissipation calculations, and toy oscillator models are included to demonstrate why silicon fails to meet these fundamental constraints.</p> <p>All results are framed in a falsifiable and constraint-driven manner: the focus is on physically measurable limits that any system must satisfy to support awareness. No assumptions are made about subjective experience, and the study provides a self-contained methodology suitable for peer review.</p> <p>This dataset and accompanying analyses offer a rigorous, multi-domain perspective on the physical prerequisites for consciousness and provide a benchmark for evaluating claims of artificial awareness in contemporary AI systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18189138 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Physical Constraints on Consciousness: Demonstrating the Impossibility of Awareness in Silicon-Based Architectures Ponce, Samual <p>This work presents a physics-based analysis of the necessary conditions for awareness and demonstrates the limitations of current silicon-based computational systems. By examining coherence time, clock period, quality factor, and phase closure, the study establishes thresholds that biological systems satisfy but silicon architectures cannot.</p> <p>High-density EEG data from the OpenNeuro ds003505 (VEPCON) dataset were analyzed using phase coherence metrics and anomaly detection across sliding windows to illustrate sustained field-like coherence in biological brains. Simulations, energy dissipation calculations, and toy oscillator models are included to demonstrate why silicon fails to meet these fundamental constraints.</p> <p>All results are framed in a falsifiable and constraint-driven manner: the focus is on physically measurable limits that any system must satisfy to support awareness. No assumptions are made about subjective experience, and the study provides a self-contained methodology suitable for peer review.</p> <p>This dataset and accompanying analyses offer a rigorous, multi-domain perspective on the physical prerequisites for consciousness and provide a benchmark for evaluating claims of artificial awareness in contemporary AI systems.</p> |
| title | Physical Constraints on Consciousness: Demonstrating the Impossibility of Awareness in Silicon-Based Architectures |
| url | https://doi.org/10.5281/zenodo.18189138 |