L1 ⚛ L1-396 ⊙ Testnet

Hodgkin-Huxley Neuron Model Parameter Estimation

Computational Biology · Computational neuroscience

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-396.md

# ⚛ L1 Principle — Hodgkin-Huxley Neuron Model Parameter Estimation **ID:** `L1-396` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Biology — *Computational neuroscience* > **🎯 Problem class:** parameter estimation · **🧮 Solution space:** HH parameter vector > **📡 Carrier:** N/A · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41555199 --- ## 🧠 1. Introduction **Hodgkin-Huxley Neuron Model Parameter Estimation** is a **parameter-estimation problem** whose unknown lives in **HH parameter vector** space, within the **Computational neuroscience** sub-domain of **Computational Biology**. Measurements consist of N/A via a **patch clamp voltage measurement** sensing mechanism. The forward operator applies, in order: time evolution of the state; O · chi2 · voltage trace operator; S · mcmc · bayesian parameter operator. Observations are corrupted by additive Gaussian noise. 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. ## ⚙ 2. Forward Model Physical chain: **x** → Time derivative → O · chi2 · voltage trace → S · mcmc · bayesian parameter → **y** (detector). ``` y = `S.mcmc.bayesian_parameter` `O.chi2.voltage_trace` ∂_t x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `D.time` | Time evolution of the state | | `O.chi2.voltage_trace` | O · chi2 · voltage trace operator | | `S.mcmc.bayesian_parameter` | S · mcmc · bayesian parameter operator | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Biology | | Sub domain | Computational neuroscience | | Carrier | N/A | | Problem class | parameter_estimation | | Solution space | HH_parameter_vector | | Noise model | gaussian | | Integration axis | time | | Difficulty delta | 5 | | L dag | 3.5 | ## 📡 4. Measurement Model 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. | Metric | Value | |---|---| | Metric | voltage_trace_RMSE_mV | | Secondary | spike_timing_error_ms | ## 📏 5. Operating Range (Ω) **Center problem class:** `parameter_estimation` · **Forward operator:** `patch_clamp_voltage_measurement` **Center point:** | Parameter | Unit | Value | |---|---|---| | G na ratio | — | 120 | | I ext ua cm2 | — | 10 | | Temperature c | — | 6.3 | | N voltage samples | — | 10000 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | G na ratio | — | 50 – 500 | | I ext ua cm2 | — | 0 – 50 | | Temperature c | — | 0 – 37 | | N voltage samples | — | 1000 – 100000 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 2.0 voltage_trace_RMSE_mV | Metric | Range | |---|---| | Voltage trace rmse mv | 0.2 – 20.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **voltage_trace_RMSE_mV**, with κ = `50000.0` and δ = `5`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0x0074d8715b7c2c36083cafb128edee3a0f95e23f753fe1bdd627106c1bebb2c3` - **Chain tx hash:** `0xf8caebe1341877f30445bda4241cd04ad18b051ea44cf87f060b15574f17369b` - **Chain block:** `41555199` --- ## File Mapping This bundle consists of: `L1-396.md`, `L1-396.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-396.md` | Source of truth — edit this | Human or LLM | | `L1-396.json` | Structured metadata for the registry | LLM regenerates from the sections above | **Prompt for your LLM after editing this Markdown:** > Read the attached Markdown. Regenerate the sibling `.json` so every field matches. > Preserve the schema documented in the rows above. > Output each file in its own fenced code block tagged with the filename. > Output only the JSON object. _This Markdown was auto-synthesized from the catalog row for `L1-396`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-396.md).
Regenerate L1-396.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-396-001 Hodgkin-Huxley Neuron Model Parameter Estimation — Nominal + Mismatch Specs

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-396",
  "chain_block": 41555199,
  "chain_hash": "0x0074d8715b7c2c36083cafb128edee3a0f95e23f753fe1bdd627106c1bebb2c3",
  "chain_tx_hash": "0xf8caebe1341877f30445bda4241cd04ad18b051ea44cf87f060b15574f17369b",
  "domain": "Computational Biology",
  "hardness_fn": {
    "delta": 5,
    "kappa": 50000.0,
    "metric": "voltage_trace_RMSE_mV",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "voltage_trace_RMSE_mV",
    "regime": "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.",
    "secondary": "spike_timing_error_ms"
  },
  "physics_fingerprint": {
    "L_DAG": 3.5,
    "carrier": "N/A",
    "difficulty_delta": 5,
    "domain": "Computational Biology",
    "integration_axis": "time",
    "noise_model": "gaussian",
    "primitives": [
      "D.time",
      "O.chi2.voltage_trace",
      "S.mcmc.bayesian_parameter"
    ],
    "problem_class": "parameter_estimation",
    "sensing_mechanism": "patch_clamp_voltage_measurement",
    "solution_space": "HH_parameter_vector",
    "sub_domain": "Computational neuroscience",
    "title": "Hodgkin-Huxley Neuron Model Parameter Estimation"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "patch_clamp_voltage_measurement"
    ],
    "allowed_omega_dimensions": [
      "g_Na_ratio",
      "temperature_C",
      "I_ext_uA_cm2",
      "N_voltage_samples"
    ],
    "allowed_problem_classes": [
      "parameter_estimation"
    ],
    "center_spec": {
      "epsilon_fn_center": "2.0 voltage_trace_RMSE_mV",
      "forward_operator": "patch_clamp_voltage_measurement",
      "input_format": "measurement_only",
      "omega": {
        "I_ext_uA_cm2": 10,
        "N_voltage_samples": 10000,
        "g_Na_ratio": 120,
        "temperature_C": 6.3
      },
      "problem_class": "parameter_estimation"
    },
    "epsilon_bounds": {
      "voltage_trace_RMSE_mV": [
        0.2,
        20.0
      ]
    },
    "omega_bounds": {
      "I_ext_uA_cm2": [
        0,
        50
      ],
      "N_voltage_samples": [
        1000,
        100000
      ],
      "g_Na_ratio": [
        50,
        500
      ],
      "temperature_C": [
        0,
        37
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Computational neuroscience",
  "title": "Hodgkin-Huxley Neuron Model Parameter Estimation"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.