Hodgkin-Huxley Neuron Model Parameter Estimation L1-396
Unclaimed Principle — open for contribution
This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.
To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.
Forward model E
Hodgkin-Huxley Neuron Model Parameter Estimation: Hodgkin-Huxley parameter estimation: infer ion channel conductances and gating kinetics from patch-clamp recordings. The forward operator produces the measurement through a 3-node primitive DAG (M.ode.hodgkin_huxley…); recovery is posed as a parameter_estimation problem. Difficulty tier delta=5 with effective condition number kappa_eff~1000; channel_noise_stochastic, dendritic_morphology_neglect set the accuracy floor at the Omega boundary. See the forward_model field for the closed-form equation.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 50000
Existence of the recovered HH_parameter_vector is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 1000); channel_noise_stochastic dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Gaussian sets the irreducible data-fidelity floor.
Solvability C
- Solver class:
- learned [MCMC_Bayesian or genetic_algorithm or gradient_SNPE]
- Convergence rate q:
- 2
- Complexity:
- O(N_timesteps * N_channels) per ODE integration per iteration