{
  "revision": "adaptive-centered-v6-live-v1",
  "phase": "training",
  "learning_enabled": true,
  "scientifically_validated": false,
  "survival_improvement_demonstrated": false,
  "study_started_at_ms": 1788653074439,
  "candidate": {
    "model": "adaptive-centered-v6-live-v1",
    "validated": false,
    "kernel": {
      "model": "adaptive-centered-v6",
      "source_sha256": "e2d4d584f4633613277bb1bf6ea7ba20855c01a332ae2e9344ed7782604c8509",
      "binary_sha256": "35fa502c39d86756e40d3f0a7c829f6bd0cf3dac961709e8ebdb2fe848018c44",
      "flags": [
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      "trace_dan_seconds": 1.0,
      "memory_decay_seconds": 1800.0,
      "weight_filter_seconds": 0.05,
      "minimum_fraction": 0.1,
      "maximum_fraction": 2.0,
      "maximum_rate_bin_ms": 10.0,
      "source": "https://doi.org/10.1038/s41586-024-07819-w",
      "interpretation": "Combines v4 KC adaptation/rest hypothesis with v5 baseline-centered anti-Hebbian rate-rule adaptation on the full graph. Tonic currents calibrated to published baseline rates; associative gain and trace constants remain hypotheses. No game-based parameter fitting.",
      "neural_dt_ms": 0.1,
      "modulator_delivery_trace_ms": 100.0,
      "kc_rest_mV": -60.0,
      "kc_adaptation_jump_mV": 8.0,
      "kc_adaptation_tau_ms": 200.0,
      "eta": 0.001
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      "modulation_mask": "914dcf25dae442aecbcf27565f7bc8be76ea8254e7fbc944f2efe6e2413e6d5a",
      "rule": {
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        "trace_dan_seconds": 1.0,
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        "maximum_fraction": 2.0,
        "maximum_rate_bin_ms": 10.0,
        "source": "https://doi.org/10.1038/s41586-024-07819-w",
        "interpretation": "Centered rate-rule adaptation on existing individual edges, not an exact reproduction of the published nine-unit model. Equal trace constants, efficacy bounds, gain and 50 ms weight filter require calibration."
      },
      "rule_sha256": "0df2ae1bf10b97a213064b3ac3934227539d08e51521a99cdb56fe7f5b44b725",
      "tonic": "9987b0e2557db3948dd617b9a70397a9ad7bf0367f8cf9e3e29af4d8d05f0420",
      "dan_baseline_hz": "986c1936fd3d3ff90de54565be34655fda3e5157d9bd1ee7e032be868fb9a799",
      "rest": "275bd935e4f1a56cbc2ca22b8f258f1cef511894f42f531e9a52476dd7b02946",
      "adaptation_jump": 8.0,
      "adaptation_tau": 200.0,
      "retina": "5ec145156d998f582e5d8f08c4174e10f7f853d34745118adab8589f192d04b4",
      "uv": "51f7dafd4355a06176418e22373ffcdf5bc066f75050da6dcbaaaccbad55a714",
      "lamina": "22d6fa8ac7aa40700d09394aea1cf35226711d2efc78bc716882920ba180e47e",
      "sugar": "89b057c7b57d5e783f608815b4219b17b54c812c3fa229bcb78b1cafa30d37a1",
      "pre": "a58bac7b66de7bbc48f47c4c5d12db216cbfbeed4a2148020ac4638dde143acd",
      "gain": "94a6bdd055ad6010d8e476c97eea5af1c240009c7068be0d82b4bfa07fdc5cfd",
      "kc_mask": "43e6ed840af2d547a3ae5848155e639f6e2abf60d5b7caa83c4298a3fb64c617",
      "dan_index": "2a62818a14eb7fb9a288eaf1dd4e93dec4e66de1428f12e844ca2ba5497823dd",
      "r8": "27c803c738f86c0509cabc57269e265bac832d8b4477773688e59a14d671f0ff",
      "r8_uv": "e567b93a289fa467ec1096295c4682dce0dfd4f7d92844159f7eb4a380e22719",
      "r8_channel": "350fc24c7884470b78254805a557e57bf12b89b71ea8a32af2f4929e951555ae",
      "corrected_edges": "64916a520f2d1e7daf30bfd027a67d53ceb0f8270097b17c0635572ce2db658d"
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      "DAN_current": 11.3125,
      "observed_MBON_hz": [
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        37
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      "observed_DAN_hz": [
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        19
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      "target_MBON_hz": 37.16625,
      "target_MBON_sd": 9.06014,
      "target_DAN_hz": 20.09,
      "target_DAN_sd": 4.30193,
      "evidence": "https://doi.org/10.1038/s41586-024-07819-w",
      "source_data": "Figure 1, panels d/e, n=20 flies per type",
      "limits": "A fitted background current is not identified pacemaker physiology. This only constrains baseline rates, not cue responses, burst statistics or learned behavior."
    },
    "visual": {
      "model": "r8-rgb-ame12-v1",
      "mapped_R8p": 330,
      "mapped_R8y": 481,
      "known_unmapped": 0,
      "projection_confidence_median": 1.0,
      "projection_below_80_percent": 22,
      "corrected_existing_edges": 390,
      "corrected_edge_sha256": "64916a520f2d1e7daf30bfd027a67d53ceb0f8270097b17c0635572ce2db658d",
      "coordinate_inference": "Modal column of all outgoing contacts to any column-annotated target; same viewport transform as R1-R6.",
      "spectrum": "Linear sRGB B for R8p; G for R8y. Display proxy, not calibrated photon flux or spectral sensitivity. R7, dorsal and untyped R8 receive no invented optical drive.",
      "physiology": "R8 to aMe12 net sign positive; original contact magnitudes retained. Other R8 targets retain baseline sign. Photoreceptors still use a LIF rate proxy, not graded in-vivo dynamics.",
      "evidence": [
        "https://doi.org/10.1038/s41586-023-06681-6",
        "https://doi.org/10.1038/s41467-024-49616-z",
        "https://doi.org/10.7554/eLife.71858"
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          0.43853339552879333
        ]
      ],
      "selection": "All existing KC-to-MBON11 edges; no graph pruning or added connections.",
      "compartment_assignment": "Cell-type-level gamma1/peduncle inference; aggregate edges do not resolve receptor localization.",
      "modulation": "PPL101 outgoing edges deliver a transmitter trace to every recorded target. Only the specified KC-to-MBON11 efficacy response is modeled. Other target responses remain unknown.",
      "baseline_difference": "Two PPL101 cells use a modulatory channel rather than the baseline default fast excitatory sign.",
      "evidence": [
        "https://doi.org/10.1016/j.neuron.2015.11.003",
        "https://doi.org/10.7554/eLife.16135",
        "https://male-cns.janelia.org/download/"
      ]
    },
    "source_sha256": {
      "doom_learning/controls.py": "301ffcb3896dcc2cc9c8def93adfb1ec1e40285584e7a88eccbfdda61cbe02dd",
      "doom_learning/run.py": "72b1304053ae8a03b55a719497003b640bb6ed76ad6c257b3c0cd6edf8c9661a",
      "doom_learning/export_iterations.py": "b04972d570c770f72b4031d2c549cc9666cc1c1e3e580e4a839f8e7e8b32916d",
      "doom_learning/circuit.py": "4ced05f329fcf8873eb2852a7fb349526efe5e7b2b9f24d2417fe719563edcdd",
      "doom_learning/benchmark.py": "7810c6b993c700191283024079f64aa9e4c405b84789b428bb7858e790198a8e",
      "doom_learning/vision.py": "3ae84010ba5ca5c4c691048be697972016e47b1ebf4ea144f0edacc9e3d499b8",
      "doom_learning/__init__.py": "6e55f234721223fd572f51189091607a5dcdc033828f17289250641c93b62345",
      "doom_learning/brain.py": "99bef17182f44c1647a1f02d591a4f66dbe8f9f5ed78e10145d0a9e53c4d6604",
      "doom_learning/common.py": "afb7564f452868d45ad3410e1c429b8edb1c4781f33ca23bd0b1c12e1ed99768",
      "doom_learning/conditioning.py": "9175d8ea1166e1f3fc38a71693069d225534d196fe90c69aee7724478d75028e",
      "doom_learning/sweep.py": "d3a2ec6e54aad0f73404f4484c9afeda878861bb09a0b222ef5b9d94561afad7",
      "doom_learning/survival_arena.py": "8eec7594986c846ce5c259eb950ab32e4d3377d840408f753f247a4f62a33eec",
      "doom_learning/analyze.py": "c68a51b59791eda4241e0eca2aef9f3023c406529de577091225a22e9ae0bd57",
      "doom_learning/kernel.cpp": "6eec74a9648c0e44d82218f1683a4ae363ceca8a226fee5a912cd3b13f18611d",
      "doom_learning_v6/stability.py": "8d6694ab570d49f9aaa952173e08e172c1375ada3e308a1cb896f2d3540181af",
      "doom_learning_v6/survival.py": "21339bbc71ea32622c00c5fccaa9767523064940c33b0e40befbea250d17637f",
      "doom_learning_v6/__init__.py": "81761cced1f9167f5f85509e8473fa234a9727af7358a2b7f44722dce3604de0",
      "doom_learning_v6/brain.py": "0c6ef6e3f80edd076c05cfaaea0e14d36b0e439c597f9456b00387062484593f",
      "doom_learning_v6/rule.py": "0df2ae1bf10b97a213064b3ac3934227539d08e51521a99cdb56fe7f5b44b725",
      "doom_learning_v6/visual.py": "788c06afd646528b0f5a9d3a8daa6838278709a15f1a90159b2ed4f9e6531c88",
      "doom_learning_v6/calibration.py": "a62951dc94ebf395933f656a002cf3765cae493bfe0337aea158b45d2487e5b6",
      "doom_learning_v6/analyze.py": "15623eab650fc2becd71754365dd3ffeab72af70fb5344644e99c8232522a9e1",
      "doom_learning_v6/kernel.cpp": "e2d4d584f4633613277bb1bf6ea7ba20855c01a332ae2e9344ed7782604c8509"
    },
    "reinforcement": "Nonfatal damage: next-step +4 mV-equivalent PPL101 current for 200 ms. Overlapping hits extend exposure. Terminal damage excluded; pending exposure cancelled at reset.",
    "rounds": "Continuous fast neural state, efficacy state and memory traces across normal game resets; no per-round equilibration. Different from the historical isolated-trial pilot.",
    "claim": "Experimental plasticity is enabled. Useful vision, associative learning and survival improvement have not been established."
  },
  "round_policy": "Death resets only the game. Continuous neural activity, memory traces and learned efficacies persist. Pending imposed reinforcement is cancelled at reset.",
  "recovery": "Full neural and learned state checkpointed; fresh arena and new run ID on recovery; interrupted round censored.",
  "controls": "Actual RGB -> inferred R1-R6/R8 input -> full retained MaleCNS model -> fixed DNp20/DNpe017 buttons. Nonfatal health loss only schedules PPL101 stimulation.",
  "display": "Actual memory efficacy histogram, changed-edge count and completed-round survival; no percent learned or asserted improvement.",
  "technical_checks": {
    "python_tests_passed": 28,
    "scientific_checks_passed": false
  },
  "prior_results": "The previous physiological and conditioning checks failed. A one-training-seed pilot changed 1,862 weights; learned and shuffled branches performed worse than frozen on one held-out start. This live combat environment is different and begins a separate study.",
  "limitations": [
    "Visual recovery, sparse cue coding and associative conditioning remain unvalidated.",
    "Neuron dynamics, R8 display input, modulation and memory parameters are hypotheses informed by literature, not measured properties of this individual.",
    "Repeated hits extend the 200 ms pulse. Terminal hits are recorded but not reinforced.",
    "No survival improvement, genuine fly cognition, literal biological brain or priority claim is established.",
    "Simulation is currently Mac-hosted; uptime and audience scaling on cloud compute remain unverified."
  ]
}