Coherent Resonant Netting
across biological scales
Interactive visualizations of noise-assisted transport on real connectome data — from nematode to human. Each page shows GKSL open-system dynamics and the inverted-U selectivity window predicted by CRN theory.
Research Focus
Biological agents face an energy-information bottleneck: inference requires rapid exploration of large hypothesis spaces, yet high-gain spiking is metabolically expensive. CRN proposes a two-regime decision architecture:
Stage-I (netting) — low-cost wave-like filtering via GKSL/Lindblad dynamics, with tunable dephasing κ and disorder ε, operating on the structural connectome to concentrate probability on target hypotheses.
Stage-II (fixation) — expensive spiking commitment that broadcasts the winner. By filtering before firing, the brain reduces the number of costly O(N) broadcast events to O(1).
We test whether Disorder-Enhanced Selectivity (DES) — a signature where moderate disorder improves target selectivity — emerges on real connectomes and depends on native topology. Degree-preserving rewiring destroys the effect. Classical random walks cannot reproduce it.
Evidence Scorecard
Publications & Data
Open Resources
Principal Investigator
Oleg Dolgikh
Background in applied optimization and distributed systems (20+ years). Since 2020, focused exclusively on theoretical neuroscience: Landauer bounds in biological networks, spectral graph theory, ENAQT regimes, and variational free energy minimization.