Hybrid Coherence-Field Simulation Engine: A Lightweight Model for Neural–Synthetic Alignment
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| Natura: | Recurso digital |
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2025
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| _version_ | 1866902062076264448 |
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| author | Ryder, JOHN F. |
| author_facet | Ryder, JOHN F. |
| contents | <p>This technical note presents a lightweight simulation engine that models real-time coherence between biological-like and synthetic signals without relying on neural-spike data. The system generates fast alpha-band activity and a slower physiological-surrogate signal, applies band-pass noise reduction, and computes three coherence measures — σ_neural, σ_slow, and σ_total — using mathematical logic introduced in the <em>Laniakea Coherence Model</em> (Ryder 2025, DOI 10.5281/zenodo.17415499).</p> <p>The accompanying Python package demonstrates how multi-timescale field alignment can stabilise telepresence and adaptive-interface systems while remaining entirely non-invasive and simulation-only. The architecture can be extended to future research in telemedicine, prosthetics, and human-computer synchronisation.</p> <p>This record forms the Tier-1 public summary. A full technical manuscript has been submitted to <strong>IEEE</strong> for peer review (2025). Proprietary hardware details and equations retained for patent filing are intentionally omitted.</p> <p><strong>© 2025 John F. Ryder – All rights reserved (public disclosure only)</strong><br><strong>Author:</strong> John F. Ryder (Drive-In s.r.o.)<br><strong>Contact:</strong> <a href="mailto:john@driveinsolution.com" rel="noopener">john@driveinsolution.com</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17593235 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hybrid Coherence-Field Simulation Engine: A Lightweight Model for Neural–Synthetic Alignment Ryder, JOHN F. coherence field, sigma-coherence, telepresence, neural interface, latency control, simulation engine, biomedical AI, human–machine synchronisation, signal processing, adaptive systems, artificial intelligence, biomedical engineering, neural systems, human–computer interaction <p>This technical note presents a lightweight simulation engine that models real-time coherence between biological-like and synthetic signals without relying on neural-spike data. The system generates fast alpha-band activity and a slower physiological-surrogate signal, applies band-pass noise reduction, and computes three coherence measures — σ_neural, σ_slow, and σ_total — using mathematical logic introduced in the <em>Laniakea Coherence Model</em> (Ryder 2025, DOI 10.5281/zenodo.17415499).</p> <p>The accompanying Python package demonstrates how multi-timescale field alignment can stabilise telepresence and adaptive-interface systems while remaining entirely non-invasive and simulation-only. The architecture can be extended to future research in telemedicine, prosthetics, and human-computer synchronisation.</p> <p>This record forms the Tier-1 public summary. A full technical manuscript has been submitted to <strong>IEEE</strong> for peer review (2025). Proprietary hardware details and equations retained for patent filing are intentionally omitted.</p> <p><strong>© 2025 John F. Ryder – All rights reserved (public disclosure only)</strong><br><strong>Author:</strong> John F. Ryder (Drive-In s.r.o.)<br><strong>Contact:</strong> <a href="mailto:john@driveinsolution.com" rel="noopener">john@driveinsolution.com</a></p> |
| title | Hybrid Coherence-Field Simulation Engine: A Lightweight Model for Neural–Synthetic Alignment |
| topic | coherence field, sigma-coherence, telepresence, neural interface, latency control, simulation engine, biomedical AI, human–machine synchronisation, signal processing, adaptive systems, artificial intelligence, biomedical engineering, neural systems, human–computer interaction |
| url | https://doi.org/10.5281/zenodo.17593235 |