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+ "tick_validation": {
44
+ "mode": "protocol_defined_internal_trials",
45
+ "requested_ticks": 100000,
46
+ "status": "NOT_APPLICABLE"
47
+ },
48
+ "ticks_requested": 100000
49
+ },
50
+ "experiment_id": "EXP-SNN-001-R1",
51
+ "experiment_status": "completed",
52
+ "git": {
53
+ "branch": "main",
54
+ "commit": "4cfcaf64e55b5c33f6fd818605a39da2c92871f9",
55
+ "dirty": true
56
+ },
57
+ "hypotheses": [
58
+ "H-SNN-001-A"
59
+ ],
60
+ "network_mode": "OFFLINE",
61
+ "provenance_digests": {
62
+ "code": "824a8ef8529a5cc6a61601b5becbc8c932a7b33870a738c5d840ed38193b977a",
63
+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
64
+ "data": "08cce309fd61cc1030ce106d8166e493c33c7a2dbf260872a31fcbcc14789b36",
65
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
66
+ },
67
+ "research_questions": [
68
+ "RQ-SNN-001"
69
+ ],
70
+ "research_run_mode": "CONFIRMATORY",
71
+ "results": {
72
+ "protocol": "sustained_activity_stability_v1",
73
+ "run_count": 20,
74
+ "runner": "run_sustained_stability",
75
+ "tick_contract": {
76
+ "mode": "protocol_defined_internal_trials",
77
+ "requested_ticks": 100000,
78
+ "status": "NOT_APPLICABLE"
79
+ },
80
+ "ticks_requested": 100000
81
+ },
82
+ "runtime": {
83
+ "duration_seconds": 12.247346400050446
84
+ },
85
+ "runtime_errors": [],
86
+ "simulation": {
87
+ "protocol": "sustained_activity_stability_v1",
88
+ "seed": 42,
89
+ "seeds": [
90
+ 42,
91
+ 43,
92
+ 44,
93
+ 45,
94
+ 46,
95
+ 47,
96
+ 48,
97
+ 49,
98
+ 50,
99
+ 51
100
+ ],
101
+ "ticks": 100000
102
+ },
103
+ "software": {
104
+ "brain5d_version": "unknown",
105
+ "os": "Windows 11",
106
+ "python": "3.13.14"
107
+ },
108
+ "source_freeze_sha": "9b3639479307712f38bbb7b584b0fe50175aaee821ac9fe0b92ee3614ae91cdb",
109
+ "timestamp": "2026-09-06T21:23:53.295815",
110
+ "validity": {
111
+ "fatal_error_count": 0,
112
+ "reason": null,
113
+ "runtime_error_count": 0,
114
+ "valid": true
115
+ }
116
+ }
research/experiments/EXP-SNN-001-R1/report.md ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R1: Sustained activity stability v1 replication
2
+
3
+ ## Forschungszuordnung
4
+ Forschungsfrage: `RQ-SNN-001`
5
+ Hypothese: `H-SNN-001-A`
6
+ Aufgeloester Runner: `run_sustained_stability`
7
+
8
+ ## Protokoll und Einstellungen
9
+ Protokoll: `sustained_activity_stability_v1`
10
+ Angeforderte Mindest-Ticks: `100000`
11
+ Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
12
+ Tick-Vertrag: `{"mode": "protocol_defined_internal_trials", "requested_ticks": 100000, "status": "NOT_APPLICABLE"}`
13
+
14
+ ## Bedingungen
15
+ No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
16
+ Runs: 20; Dauer: 12.247346 s
17
+
18
+ ## Daten und Statistik
19
+ Kompakte Run-Projektion: `DATA/runs.json`
20
+ Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
21
+ KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
22
+ Deterministische deskriptive Statistik: `analysis/statistics.json`
23
+ Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
24
+
25
+ ## Evidenzstatus
26
+ DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
27
+
28
+ ## Hinweise
29
+ Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.
research/experiments/EXP-SNN-001-R1/summary.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R1: Wissenschaftliche Zusammenfassung
2
+
3
+ Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
4
+
5
+ ## 1. Identifikation und Status
6
+
7
+ - Experimentstatus: `completed`
8
+ - Forschungsfrage: `RQ-SNN-001`
9
+ - Hypothese: `H-SNN-001-A`
10
+ - Protokoll: `sustained_activity_stability_v1`
11
+ - Durchlaeufe: `20`
12
+ - Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
13
+ - Angeforderte Ticks: `100000`
14
+ - Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
15
+ - Tick-Vertrag: `SATISFIED`
16
+ - TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
17
+ - Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
18
+ - Laufmodus: `CONFIRMATORY`
19
+ - Netzwerkmodus: `OFFLINE`
20
+
21
+ ## 2. Semantische Konsistenz
22
+
23
+ - RQ/Condition-Pruefung: `MISMATCH`
24
+ - Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
25
+ - Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
26
+ - Beobachtete Conditions: `no_input_control, tonic_drive`
27
+
28
+ `DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
29
+
30
+ ## 3. Ausfuehrungsparameter
31
+
32
+ - Titel: Sustained activity stability v1 replication
33
+ - Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
34
+ - Notizen: Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.
35
+ - Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
36
+ - Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
37
+ - Git Commit: `4cfcaf64e55b5c33f6fd818605a39da2c92871f9`
38
+ - Git dirty: `True`
39
+ - Runtime: `12.247346400050446` s
40
+
41
+ ## 4. Deterministische Formeln
42
+
43
+ Die im Bericht verwendeten deskriptiven Groessen sind:
44
+
45
+ - Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
46
+ - Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
47
+ - Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
48
+ - Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
49
+ - Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
50
+
51
+ Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
52
+
53
+ ## 5. Ergebnisse nach Bedingung
54
+
55
+ | Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
56
+ | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
57
+ | no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
58
+ | tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
59
+
60
+ ### 5.1 Deskriptive Zwei-Bedingungs-Effekte
61
+
62
+ - `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
63
+
64
+ ## 6. Einzelne Läufe
65
+
66
+ | Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
67
+ | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
68
+ | 42 | no_input_control | 100000 | — | — | — | — | — | — |
69
+ | 42 | tonic_drive | 100000 | — | — | — | — | — | — |
70
+ | 43 | no_input_control | 100000 | — | — | — | — | — | — |
71
+ | 43 | tonic_drive | 100000 | — | — | — | — | — | — |
72
+ | 44 | no_input_control | 100000 | — | — | — | — | — | — |
73
+ | 44 | tonic_drive | 100000 | — | — | — | — | — | — |
74
+ | 45 | no_input_control | 100000 | — | — | — | — | — | — |
75
+ | 45 | tonic_drive | 100000 | — | — | — | — | — | — |
76
+ | 46 | no_input_control | 100000 | — | — | — | — | — | — |
77
+ | 46 | tonic_drive | 100000 | — | — | — | — | — | — |
78
+ | 47 | no_input_control | 100000 | — | — | — | — | — | — |
79
+ | 47 | tonic_drive | 100000 | — | — | — | — | — | — |
80
+ | 48 | no_input_control | 100000 | — | — | — | — | — | — |
81
+ | 48 | tonic_drive | 100000 | — | — | — | — | — | — |
82
+ | 49 | no_input_control | 100000 | — | — | — | — | — | — |
83
+ | 49 | tonic_drive | 100000 | — | — | — | — | — | — |
84
+ | 50 | no_input_control | 100000 | — | — | — | — | — | — |
85
+ | 50 | tonic_drive | 100000 | — | — | — | — | — | — |
86
+ | 51 | no_input_control | 100000 | — | — | — | — | — | — |
87
+ | 51 | tonic_drive | 100000 | — | — | — | — | — | — |
88
+
89
+ ## 7. Reproduzierbarkeit und Provenienz
90
+
91
+ Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
92
+
93
+ Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
94
+
95
+ ## 8. AI Research Report
96
+
97
+ - AIRR Status: `unavailable`
98
+ - Wissenschaftliche Evidenz durch KI: `false`
99
+ - Human Review: `PENDING`
100
+
101
+ ## 9. Artefakte
102
+
103
+ - [analysis/ai_packet.json](analysis/ai_packet.json)
104
+ - [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
105
+ - [analysis/statistics.json](analysis/statistics.json)
106
+ - [DATA/current_run.json](DATA/current_run.json)
107
+ - [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
108
+ - [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
109
+ - [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
110
+ - [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
111
+ - [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
112
+ - [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
113
+ - [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
114
+ - [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
115
+ - [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
116
+ - [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
117
+ - [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
118
+ - [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
119
+ - [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
120
+ - [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
121
+ - [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
122
+ - [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
123
+ - [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
124
+ - [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
125
+ - [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
126
+ - [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
127
+ - [DATA/runs.json](DATA/runs.json)
128
+ - [DATA/runs_index.json](DATA/runs_index.json)
129
+ - [manifest.json](manifest.json)
130
+ - [report.md](report.md)
131
+ - [workflow.json](workflow.json)
132
+
133
+ ## 10. Wissenschaftliche Grenze und Schlussfolgerung
134
+
135
+ Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
136
+
137
+ **Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
research/experiments/EXP-SNN-001-R1/workflow.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment_id": "EXP-SNN-001-R1",
3
+ "research_question": "RQ-SNN-001",
4
+ "hypothesis": "H-SNN-001-A",
5
+ "title": "Sustained activity stability v1 replication",
6
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
7
+ "ticks": 100000,
8
+ "ticks_contract": "minimum requested observation window",
9
+ "seeds": [
10
+ 42,
11
+ 43,
12
+ 44,
13
+ 45,
14
+ 46,
15
+ 47,
16
+ 48,
17
+ 49,
18
+ 50,
19
+ 51
20
+ ],
21
+ "protocol": "sustained_activity_stability_v1",
22
+ "resolved_runner": "run_sustained_stability",
23
+ "tick_validation": {
24
+ "status": "NOT_APPLICABLE",
25
+ "mode": "protocol_defined_internal_trials",
26
+ "requested_ticks": 100000
27
+ },
28
+ "notes": "Exact protocol replication after tick-contract registry correction. Frozen PREREG-SNN-001.",
29
+ "execution": "registered experiment_suite runner",
30
+ "assistant_policy": "AI is post-hoc interpretation only."
31
+ }
research/experiments/EXP-SNN-001-R2/DATA/current_run.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "last_archived_run": 19,
3
+ "raw_artifact": {
4
+ "format": "json.gz",
5
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
6
+ "sha256": "5d8f358b0dd07b4a4710d9cb4fad3740611d28864cf9f826b57213b2f34bfaa5",
7
+ "size_bytes": 24781,
8
+ "uncompressed_bytes": 2102883
9
+ },
10
+ "status": "idle"
11
+ }
research/experiments/EXP-SNN-001-R2/DATA/runs_index.json ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "run_count": 20,
3
+ "runs": [
4
+ {
5
+ "condition": "no_input_control",
6
+ "format": "json.gz",
7
+ "path": "DATA/raw/run-0000-no_input_control-seed-42.json.gz",
8
+ "run_index": 0,
9
+ "seed": 42,
10
+ "sha256": "1d380b138c3236b1f16fc5678c49bfe718e14dff866ba0d39a0bab38d8e1b833",
11
+ "size_bytes": 12759,
12
+ "uncompressed_bytes": 2102704
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+ },
14
+ {
15
+ "condition": "tonic_drive",
16
+ "format": "json.gz",
17
+ "path": "DATA/raw/run-0001-tonic_drive-seed-42.json.gz",
18
+ "run_index": 1,
19
+ "seed": 42,
20
+ "sha256": "44847da7b79eea41778b70c1e282defa6e65ba3d73cfc18f0a82d6a843d37103",
21
+ "size_bytes": 24782,
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+ "uncompressed_bytes": 2102883
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+ },
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+ {
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+ "condition": "no_input_control",
26
+ "format": "json.gz",
27
+ "path": "DATA/raw/run-0002-no_input_control-seed-43.json.gz",
28
+ "run_index": 2,
29
+ "seed": 43,
30
+ "sha256": "91710b7b6b48ba617f7f517c9492c328075dce9671aaf5f0539019637c3ffe71",
31
+ "size_bytes": 12763,
32
+ "uncompressed_bytes": 2102704
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+ },
34
+ {
35
+ "condition": "tonic_drive",
36
+ "format": "json.gz",
37
+ "path": "DATA/raw/run-0003-tonic_drive-seed-43.json.gz",
38
+ "run_index": 3,
39
+ "seed": 43,
40
+ "sha256": "a7eb31e524d2219b1eae76d97cf126f65d7165ec3a0f1ae1223c7290274564eb",
41
+ "size_bytes": 24782,
42
+ "uncompressed_bytes": 2102883
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44
+ {
45
+ "condition": "no_input_control",
46
+ "format": "json.gz",
47
+ "path": "DATA/raw/run-0004-no_input_control-seed-44.json.gz",
48
+ "run_index": 4,
49
+ "seed": 44,
50
+ "sha256": "3cbf3d98caee9bb4e5bd371aaf3dbf9bc2e008089be875bbd9a8ebb5ee25ead0",
51
+ "size_bytes": 12763,
52
+ "uncompressed_bytes": 2102704
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+ },
54
+ {
55
+ "condition": "tonic_drive",
56
+ "format": "json.gz",
57
+ "path": "DATA/raw/run-0005-tonic_drive-seed-44.json.gz",
58
+ "run_index": 5,
59
+ "seed": 44,
60
+ "sha256": "39c40b6b00fa1d20531ad3f529348a2622d6711113005203cd6080b1792161b4",
61
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+ "uncompressed_bytes": 2102883
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+ },
64
+ {
65
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+ {
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+ "ai_exposure": "none",
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+ "ai_interactions": [],
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+ "ai_protocol": {
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+ "bump_reason": "initial registration",
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+ "version": 1
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+ },
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+ "ai_reproducibility": "R0",
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+ "artifacts": {
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+ "ai_packet": "analysis/ai_packet.json",
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+ "ai_packet_digest": "analysis/ai_packet_digest.json",
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+ "current_run": "DATA/current_run.json",
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+ "data": "DATA/runs.json",
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+ "data_index": "DATA/runs_index.json",
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+ "preregistration": "preregistrations/PREREG-SNN-001.json",
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+ "raw_run_index": "DATA/runs_index.json",
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+ "report": "report.md",
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+ "statistics": "analysis/statistics.json",
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+ "workflow": "workflow.json"
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+ },
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+ "causal_card": {
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+ "classification": "PURE",
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+ "interaction_ids": [],
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+ "roles": []
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+ },
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+ "causal_taint": "PURE",
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+ "config": {
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+ "path": "F:\\Brain-5D\\configs\\learning_experiment.yaml",
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+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
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+ },
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+ "created_at": "2026-09-06T19:24:27.349674+00:00",
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+ "data_partitions": [],
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+ "execution_contract": {
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+ "hypothesis_id": "H-SNN-001-A",
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+ "protocol": "sustained_activity_stability_v1",
36
+ "question_id": "RQ-SNN-001",
37
+ "resolved_runner": "run_sustained_stability",
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+ "runtime_runner_bytecode_sha256": "5d72d56b50f32859bde16ee622629877e97a65c534109b5215a4dd20da46e025",
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+ "runtime_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
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+ "source_runtime_consistency": "MATCH",
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+ "source_summary_bytecode_sha256": "8d311e523e6093b1bbb23954dae163406f1836fb0defa5b8c4479ac68a929395",
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+ "tick_validation": {
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+ "mode": "minimum_per_run_with_control_and_treatment",
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+ "observed_max": 100000,
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+ "observed_min": 100000,
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+ "requested_ticks": 100000,
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+ "run_count": 20,
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+ "status": "SATISFIED"
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+ },
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+ "ticks_requested": 100000
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+ },
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+ "experiment_id": "EXP-SNN-001-R2",
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+ "experiment_status": "completed",
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+ "git": {
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+ "branch": "main",
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+ "commit": "4cfcaf64e55b5c33f6fd818605a39da2c92871f9",
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+ "dirty": true
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+ },
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+ "hypotheses": [
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+ "H-SNN-001-A"
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+ ],
63
+ "network_mode": "OFFLINE",
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+ "provenance_digests": {
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+ "code": "824a8ef8529a5cc6a61601b5becbc8c932a7b33870a738c5d840ed38193b977a",
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+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
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+ "data": "7c6b92100f5b5d479f5c541778394984affcf96541beae410e80e63b1580e956",
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+ },
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+ "research_questions": [
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+ "RQ-SNN-001"
72
+ ],
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+ "research_run_mode": "CONFIRMATORY",
74
+ "results": {
75
+ "protocol": "sustained_activity_stability_v1",
76
+ "run_count": 20,
77
+ "runner": "run_sustained_stability",
78
+ "tick_contract": {
79
+ "mode": "minimum_per_run_with_control_and_treatment",
80
+ "observed_max": 100000,
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+ "observed_min": 100000,
82
+ "requested_ticks": 100000,
83
+ "run_count": 20,
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+ "status": "SATISFIED"
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+ },
86
+ "ticks_requested": 100000
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+ },
88
+ "runtime": {
89
+ "duration_seconds": 11.832557399990037
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+ },
91
+ "runtime_errors": [],
92
+ "simulation": {
93
+ "protocol": "sustained_activity_stability_v1",
94
+ "seed": 42,
95
+ "seeds": [
96
+ 42,
97
+ 43,
98
+ 44,
99
+ 45,
100
+ 46,
101
+ 47,
102
+ 48,
103
+ 49,
104
+ 50,
105
+ 51
106
+ ],
107
+ "ticks": 100000
108
+ },
109
+ "software": {
110
+ "brain5d_version": "unknown",
111
+ "os": "Windows 11",
112
+ "python": "3.13.14"
113
+ },
114
+ "source_freeze_sha": "d308c6620ea1e1c7b9c4c8bc6b37e37e48d3de12a96df53f695af6d8627a8e32",
115
+ "timestamp": "2026-09-06T21:24:27.349646",
116
+ "validity": {
117
+ "fatal_error_count": 0,
118
+ "reason": null,
119
+ "runtime_error_count": 0,
120
+ "valid": true
121
+ }
122
+ }
research/experiments/EXP-SNN-001-R2/report.md ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R2: Sustained activity stability v1 final replication
2
+
3
+ ## Forschungszuordnung
4
+ Forschungsfrage: `RQ-SNN-001`
5
+ Hypothese: `H-SNN-001-A`
6
+ Aufgeloester Runner: `run_sustained_stability`
7
+
8
+ ## Protokoll und Einstellungen
9
+ Protokoll: `sustained_activity_stability_v1`
10
+ Angeforderte Mindest-Ticks: `100000`
11
+ Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
12
+ Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
13
+
14
+ ## Bedingungen
15
+ No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
16
+ Runs: 20; Dauer: 11.832557 s
17
+
18
+ ## Daten und Statistik
19
+ Kompakte Run-Projektion: `DATA/runs.json`
20
+ Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
21
+ KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
22
+ Deterministische deskriptive Statistik: `analysis/statistics.json`
23
+ Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
24
+
25
+ ## Evidenzstatus
26
+ DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
27
+
28
+ ## Hinweise
29
+ Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.
research/experiments/EXP-SNN-001-R2/summary.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R2: Wissenschaftliche Zusammenfassung
2
+
3
+ Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
4
+
5
+ ## 1. Identifikation und Status
6
+
7
+ - Experimentstatus: `completed`
8
+ - Forschungsfrage: `RQ-SNN-001`
9
+ - Hypothese: `H-SNN-001-A`
10
+ - Protokoll: `sustained_activity_stability_v1`
11
+ - Durchlaeufe: `20`
12
+ - Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
13
+ - Angeforderte Ticks: `100000`
14
+ - Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
15
+ - Tick-Vertrag: `SATISFIED`
16
+ - TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
17
+ - Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
18
+ - Laufmodus: `CONFIRMATORY`
19
+ - Netzwerkmodus: `OFFLINE`
20
+
21
+ ## 2. Semantische Konsistenz
22
+
23
+ - RQ/Condition-Pruefung: `MISMATCH`
24
+ - Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
25
+ - Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
26
+ - Beobachtete Conditions: `no_input_control, tonic_drive`
27
+
28
+ `DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
29
+
30
+ ## 3. Ausfuehrungsparameter
31
+
32
+ - Titel: Sustained activity stability v1 final replication
33
+ - Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
34
+ - Notizen: Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.
35
+ - Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
36
+ - Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
37
+ - Git Commit: `4cfcaf64e55b5c33f6fd818605a39da2c92871f9`
38
+ - Git dirty: `True`
39
+ - Runtime: `11.832557399990037` s
40
+
41
+ ## 4. Deterministische Formeln
42
+
43
+ Die im Bericht verwendeten deskriptiven Groessen sind:
44
+
45
+ - Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
46
+ - Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
47
+ - Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
48
+ - Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
49
+ - Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
50
+
51
+ Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
52
+
53
+ ## 5. Ergebnisse nach Bedingung
54
+
55
+ | Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
56
+ | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
57
+ | no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
58
+ | tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
59
+
60
+ ### 5.1 Deskriptive Zwei-Bedingungs-Effekte
61
+
62
+ - `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
63
+
64
+ ## 6. Einzelne Läufe
65
+
66
+ | Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
67
+ | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
68
+ | 42 | no_input_control | 100000 | — | — | — | — | — | — |
69
+ | 42 | tonic_drive | 100000 | — | — | — | — | — | — |
70
+ | 43 | no_input_control | 100000 | — | — | — | — | — | — |
71
+ | 43 | tonic_drive | 100000 | — | — | — | — | — | — |
72
+ | 44 | no_input_control | 100000 | — | — | — | — | — | — |
73
+ | 44 | tonic_drive | 100000 | — | — | — | — | — | — |
74
+ | 45 | no_input_control | 100000 | — | — | — | — | — | — |
75
+ | 45 | tonic_drive | 100000 | — | — | — | — | — | — |
76
+ | 46 | no_input_control | 100000 | — | — | — | — | — | — |
77
+ | 46 | tonic_drive | 100000 | — | — | — | — | — | — |
78
+ | 47 | no_input_control | 100000 | — | — | — | — | — | — |
79
+ | 47 | tonic_drive | 100000 | — | — | — | — | — | — |
80
+ | 48 | no_input_control | 100000 | — | — | — | — | — | — |
81
+ | 48 | tonic_drive | 100000 | — | — | — | — | — | — |
82
+ | 49 | no_input_control | 100000 | — | — | — | — | — | — |
83
+ | 49 | tonic_drive | 100000 | — | — | — | — | — | — |
84
+ | 50 | no_input_control | 100000 | — | — | — | — | — | — |
85
+ | 50 | tonic_drive | 100000 | — | — | — | — | — | — |
86
+ | 51 | no_input_control | 100000 | — | — | — | — | — | — |
87
+ | 51 | tonic_drive | 100000 | — | — | — | — | — | — |
88
+
89
+ ## 7. Reproduzierbarkeit und Provenienz
90
+
91
+ Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
92
+
93
+ Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
94
+
95
+ ## 8. AI Research Report
96
+
97
+ - AIRR Status: `unavailable`
98
+ - Wissenschaftliche Evidenz durch KI: `false`
99
+ - Human Review: `PENDING`
100
+
101
+ ## 9. Artefakte
102
+
103
+ - [analysis/ai_packet.json](analysis/ai_packet.json)
104
+ - [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
105
+ - [analysis/statistics.json](analysis/statistics.json)
106
+ - [DATA/current_run.json](DATA/current_run.json)
107
+ - [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
108
+ - [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
109
+ - [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
110
+ - [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
111
+ - [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
112
+ - [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
113
+ - [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
114
+ - [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
115
+ - [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
116
+ - [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
117
+ - [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
118
+ - [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
119
+ - [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
120
+ - [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
121
+ - [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
122
+ - [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
123
+ - [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
124
+ - [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
125
+ - [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
126
+ - [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
127
+ - [DATA/runs.json](DATA/runs.json)
128
+ - [DATA/runs_index.json](DATA/runs_index.json)
129
+ - [manifest.json](manifest.json)
130
+ - [report.md](report.md)
131
+ - [workflow.json](workflow.json)
132
+
133
+ ## 10. Wissenschaftliche Grenze und Schlussfolgerung
134
+
135
+ Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
136
+
137
+ **Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
research/experiments/EXP-SNN-001-R2/workflow.json ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment_id": "EXP-SNN-001-R2",
3
+ "research_question": "RQ-SNN-001",
4
+ "hypothesis": "H-SNN-001-A",
5
+ "title": "Sustained activity stability v1 final replication",
6
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
7
+ "ticks": 100000,
8
+ "ticks_contract": "minimum requested observation window",
9
+ "seeds": [
10
+ 42,
11
+ 43,
12
+ 44,
13
+ 45,
14
+ 46,
15
+ 47,
16
+ 48,
17
+ 49,
18
+ 50,
19
+ 51
20
+ ],
21
+ "protocol": "sustained_activity_stability_v1",
22
+ "resolved_runner": "run_sustained_stability",
23
+ "tick_validation": {
24
+ "status": "SATISFIED",
25
+ "mode": "minimum_per_run_with_control_and_treatment",
26
+ "requested_ticks": 100000,
27
+ "observed_min": 100000,
28
+ "observed_max": 100000,
29
+ "run_count": 20
30
+ },
31
+ "notes": "Final exact replication under frozen PREREG-SNN-001 with satisfied tick contract.",
32
+ "execution": "registered experiment_suite runner",
33
+ "assistant_policy": "AI is post-hoc interpretation only."
34
+ }
research/experiments/EXP-SNN-001-R3/DATA/current_run.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "last_archived_run": 19,
3
+ "raw_artifact": {
4
+ "format": "json.gz",
5
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
6
+ "sha256": "550e71ef31aeb7ab155160148702df4ed4287e8dc001b1a472163a568cd5a3cb",
7
+ "size_bytes": 24781,
8
+ "uncompressed_bytes": 2102883
9
+ },
10
+ "status": "idle"
11
+ }
research/experiments/EXP-SNN-001-R3/DATA/runs_index.json ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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research/experiments/EXP-SNN-001-R3/analysis/statistics.json ADDED
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+ {
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+ "tonic_drive": {
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+ ]
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+ }
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+ },
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+ "formulas": {
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+ "difference": "Delta_x = mean(x_B) - mean(x_A)",
56
+ "inter_spike_interval": "ISI_i = t_(i+1) - t_i",
57
+ "mean": "mean(x) = (1/n) * sum_i x_i",
58
+ "population_std": "sigma = sqrt((1/n) * sum_i (x_i - mean(x))^2)",
59
+ "ratio": "R_x = mean(x_B) / mean(x_A), defined only when mean(x_A) != 0"
60
+ },
61
+ "generated_by": "deterministic_statistics_engine",
62
+ "run_count": 20,
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+ "schema_version": "2.0",
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+ "two_condition_effects": {
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+ "ticks_executed": {
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+ "absolute_difference": 0.0,
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+ "comparison_condition": "tonic_drive",
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+ "ratio": 1.0,
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+ "reference_condition": "no_input_control"
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+ }
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+ }
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+ }
research/experiments/EXP-SNN-001-R3/manifest.json ADDED
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+ {
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+ "current_run": "DATA/current_run.json",
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+ "data": "DATA/runs.json",
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+ "data_index": "DATA/runs_index.json",
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+ "preregistration": "preregistrations/PREREG-SNN-001.json",
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+ "raw_run_index": "DATA/runs_index.json",
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+ "report": "report.md",
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+ "statistics": "analysis/statistics.json",
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+ "workflow": "workflow.json"
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+ },
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+ "causal_card": {
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+ "classification": "PURE",
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+ "roles": []
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+ },
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+ "causal_taint": "PURE",
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+ "path": "C:\\Users\\T_hei\\AppData\\Local\\Temp\\brain5d-clean-snn-r3\\configs\\learning_experiment.yaml",
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+ "sha256": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c"
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+ "created_at": "2026-09-07T14:09:01.933323+00:00",
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+ "data_partitions": [],
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+ "epistemic_layers": {
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+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
35
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
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+ "interpretation": "post-hoc AI or human interpretation; never execution input",
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+ "ui_state": "operator/dashboard progress only; not a scientific result"
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+ },
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+ "execution_contract": {
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+ "hypothesis_id": "H-SNN-001-A",
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+ "protocol": "sustained_activity_stability_v1",
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+ "question_id": "RQ-SNN-001",
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+ "resolved_runner": "run_sustained_stability",
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+ "source_runtime_consistency": "MATCH",
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+ "H-SNN-001-A"
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+ ],
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+ "network_mode": "OFFLINE",
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+ "config": "6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c",
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+ "data": "242d72dde8c16637f8ebda383292bc4fbd03a90a780ca63931a1a17c940e7306",
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+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
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+ },
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+ "research_questions": [
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+ "RQ-SNN-001"
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+ ],
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+ "research_run_mode": "CONFIRMATORY",
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+ "results": {
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+ "protocol": "sustained_activity_stability_v1",
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+ "run_count": 20,
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+ "runner": "run_sustained_stability",
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+ "tick_contract": {
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+ "mode": "minimum_per_run_with_control_and_treatment",
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+ },
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+ },
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+ "runtime": {
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+ "duration_seconds": 10.707760399905965
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+ },
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+ "runtime_errors": [],
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+ "simulation": {
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+ "protocol": "sustained_activity_stability_v1",
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+ "seed": 42,
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+ "seeds": [
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+ 42,
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+ ],
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+ "ticks": 100000
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+ },
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+ "software": {
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+ "brain5d_version": "unknown",
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+ "os": "Windows 11",
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+ "python": "3.13.14"
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+ },
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+ "source_freeze_sha": "75c6ee0d7614a47f00540dc5d29b19aba3e4062b0b3cd4f6b8747f23f943ba87",
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+ "timestamp": "2026-09-07T16:09:01.933291",
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+ "validity": {
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+ "fatal_error_count": 0,
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+ "reason": null,
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+ "runtime_error_count": 0,
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+ "valid": true
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+ }
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+ }
research/experiments/EXP-SNN-001-R3/report.md ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R3: RQ-SNN-001 frozen clean-tree replication
2
+
3
+ ## Forschungszuordnung
4
+ Forschungsfrage: `RQ-SNN-001`
5
+ Hypothese: `H-SNN-001-A`
6
+ Aufgeloester Runner: `run_sustained_stability`
7
+
8
+ ## Protokoll und Einstellungen
9
+ Protokoll: `sustained_activity_stability_v1`
10
+ Angeforderte Mindest-Ticks: `100000`
11
+ Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
12
+ Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
13
+
14
+ ## Bedingungen
15
+ Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
16
+ Runs: 20; Dauer: 10.707760 s
17
+
18
+ ## Daten und Statistik
19
+ Kompakte Run-Projektion: `DATA/runs.json`
20
+ Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
21
+ KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
22
+ Deterministische deskriptive Statistik: `analysis/statistics.json`
23
+ Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
24
+
25
+ ## Epistemische Ebenen
26
+ UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
27
+ DATA: Rohdaten, Run-Index und deterministische Statistik.
28
+ EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
29
+ Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
30
+
31
+ ## Evidenzstatus
32
+ DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
33
+
34
+ ## Hinweise
35
+ Exact frozen protocol rerun from a clean Git worktree.
research/experiments/EXP-SNN-001-R3/summary.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R3: Wissenschaftliche Zusammenfassung
2
+
3
+ Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
4
+
5
+ ## 1. Identifikation und Status
6
+
7
+ - Experimentstatus: `completed`
8
+ - Forschungsfrage: `RQ-SNN-001`
9
+ - Hypothese: `H-SNN-001-A`
10
+ - Protokoll: `sustained_activity_stability_v1`
11
+ - Durchlaeufe: `20`
12
+ - Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
13
+ - Angeforderte Ticks: `100000`
14
+ - Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
15
+ - Tick-Vertrag: `SATISFIED`
16
+ - TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
17
+ - Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
18
+ - Laufmodus: `CONFIRMATORY`
19
+ - Netzwerkmodus: `OFFLINE`
20
+
21
+ ## 2. Semantische Konsistenz
22
+
23
+ - RQ/Condition-Pruefung: `MISMATCH`
24
+ - Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
25
+ - Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
26
+ - Beobachtete Conditions: `no_input_control, tonic_drive`
27
+
28
+ `DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
29
+
30
+ ## 3. Ausfuehrungsparameter
31
+
32
+ - Titel: RQ-SNN-001 frozen clean-tree replication
33
+ - Bedingungen: Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
34
+ - Notizen: Exact frozen protocol rerun from a clean Git worktree.
35
+ - Konfiguration: `C:\Users\T_hei\AppData\Local\Temp\brain5d-clean-snn-r3\configs\learning_experiment.yaml`
36
+ - Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
37
+ - Git Commit: `23dbbd2607d8d6075b57474184d6e6cc3e217b85`
38
+ - Git dirty: `True`
39
+ - Runtime: `10.707760399905965` s
40
+
41
+ ## 4. Deterministische Formeln
42
+
43
+ Die im Bericht verwendeten deskriptiven Groessen sind:
44
+
45
+ - Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
46
+ - Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
47
+ - Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
48
+ - Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
49
+ - Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
50
+
51
+ Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
52
+
53
+ ## 5. Ergebnisse nach Bedingung
54
+
55
+ | Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
56
+ | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
57
+ | no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
58
+ | tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
59
+
60
+ ### 5.1 Deskriptive Zwei-Bedingungs-Effekte
61
+
62
+ - `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
63
+
64
+ ## 6. Einzelne Läufe
65
+
66
+ | Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
67
+ | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
68
+ | 42 | no_input_control | 100000 | — | — | — | — | — | — |
69
+ | 42 | tonic_drive | 100000 | — | — | — | — | — | — |
70
+ | 43 | no_input_control | 100000 | — | — | — | — | — | — |
71
+ | 43 | tonic_drive | 100000 | — | — | — | — | — | — |
72
+ | 44 | no_input_control | 100000 | — | — | — | — | — | — |
73
+ | 44 | tonic_drive | 100000 | — | — | — | — | — | — |
74
+ | 45 | no_input_control | 100000 | — | — | — | — | — | — |
75
+ | 45 | tonic_drive | 100000 | — | — | — | — | — | — |
76
+ | 46 | no_input_control | 100000 | — | — | — | — | — | — |
77
+ | 46 | tonic_drive | 100000 | — | — | — | — | — | — |
78
+ | 47 | no_input_control | 100000 | — | — | — | — | — | — |
79
+ | 47 | tonic_drive | 100000 | — | — | — | — | — | — |
80
+ | 48 | no_input_control | 100000 | — | — | — | — | — | — |
81
+ | 48 | tonic_drive | 100000 | — | — | — | — | — | — |
82
+ | 49 | no_input_control | 100000 | — | — | — | — | — | — |
83
+ | 49 | tonic_drive | 100000 | — | — | — | — | — | — |
84
+ | 50 | no_input_control | 100000 | — | — | — | — | — | — |
85
+ | 50 | tonic_drive | 100000 | — | — | — | — | — | — |
86
+ | 51 | no_input_control | 100000 | — | — | — | — | — | — |
87
+ | 51 | tonic_drive | 100000 | — | — | — | — | — | — |
88
+
89
+ ## 7. Reproduzierbarkeit und Provenienz
90
+
91
+ Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
92
+
93
+ Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
94
+
95
+ ## 8. AI Research Report
96
+
97
+ - AIRR Status: `unavailable`
98
+ - Wissenschaftliche Evidenz durch KI: `false`
99
+ - Human Review: `PENDING`
100
+
101
+ ## 9. Artefakte
102
+
103
+ - [analysis/ai_packet.json](analysis/ai_packet.json)
104
+ - [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
105
+ - [analysis/statistics.json](analysis/statistics.json)
106
+ - [DATA/current_run.json](DATA/current_run.json)
107
+ - [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
108
+ - [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
109
+ - [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
110
+ - [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
111
+ - [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
112
+ - [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
113
+ - [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
114
+ - [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
115
+ - [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
116
+ - [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
117
+ - [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
118
+ - [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
119
+ - [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
120
+ - [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
121
+ - [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
122
+ - [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
123
+ - [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
124
+ - [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
125
+ - [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
126
+ - [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
127
+ - [DATA/runs.json](DATA/runs.json)
128
+ - [DATA/runs_index.json](DATA/runs_index.json)
129
+ - [manifest.json](manifest.json)
130
+ - [report.md](report.md)
131
+ - [workflow.json](workflow.json)
132
+
133
+ ## 10. Wissenschaftliche Grenze und Schlussfolgerung
134
+
135
+ Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
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+
137
+ **Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
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+ "title": "RQ-SNN-001 frozen clean-tree replication",
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+ "conditions": "Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks",
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+ "ticks": 100000,
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research/experiments/EXP-SNN-001-R5/report.md ADDED
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1
+ # EXP-SNN-001-R5: RQ-SNN-001 frozen clean-tree replication
2
+
3
+ ## Forschungszuordnung
4
+ Forschungsfrage: `RQ-SNN-001`
5
+ Hypothese: `H-SNN-001-A`
6
+ Aufgeloester Runner: `run_sustained_stability`
7
+
8
+ ## Protokoll und Einstellungen
9
+ Protokoll: `sustained_activity_stability_v1`
10
+ Angeforderte Mindest-Ticks: `100000`
11
+ Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
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+ Tick-Vertrag: `{"mode": "minimum_per_run_with_control_and_treatment", "observed_max": 100000, "observed_min": 100000, "requested_ticks": 100000, "run_count": 20, "status": "SATISFIED"}`
13
+
14
+ ## Bedingungen
15
+ Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
16
+ Runs: 20; Dauer: 10.860079 s
17
+
18
+ ## Daten und Statistik
19
+ Kompakte Run-Projektion: `DATA/runs.json`
20
+ Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
21
+ KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
22
+ Deterministische deskriptive Statistik: `analysis/statistics.json`
23
+ Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
24
+
25
+ ## Epistemische Ebenen
26
+ UI-Zustand: Dashboard-Steuerung und Fortschritt; kein wissenschaftliches Ergebnis.
27
+ DATA: Rohdaten, Run-Index und deterministische Statistik.
28
+ EVID: nicht erzeugt; Clean Freeze, semantische Zuordnung und Human Review erforderlich.
29
+ Interpretation: nachgelagerte KI-/Human-Interpretation; keine Ausfuehrungseingabe.
30
+
31
+ ## Evidenzstatus
32
+ DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
33
+
34
+ ## Hinweise
35
+ Exact frozen protocol rerun from a clean Git worktree.
research/experiments/EXP-SNN-001-R5/summary.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001-R5: Wissenschaftliche Zusammenfassung
2
+
3
+ Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
4
+
5
+ ## 1. Identifikation und Status
6
+
7
+ - Experimentstatus: `completed`
8
+ - Forschungsfrage: `RQ-SNN-001`
9
+ - Hypothese: `H-SNN-001-A`
10
+ - Protokoll: `sustained_activity_stability_v1`
11
+ - Durchlaeufe: `20`
12
+ - Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
13
+ - Angeforderte Ticks: `100000`
14
+ - Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
15
+ - Tick-Vertrag: `SATISFIED`
16
+ - TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
17
+ - Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
18
+ - Laufmodus: `CONFIRMATORY`
19
+ - Netzwerkmodus: `OFFLINE`
20
+
21
+ ## 2. Semantische Konsistenz
22
+
23
+ - RQ/Condition-Pruefung: `DIRECT_MATCH`
24
+ - Evidence Readiness: `BLOCKED_HUMAN_REVIEW`
25
+ - Begründung: RQ-SNN-001 verwendet das dedizierte Sustained-Activity-Protokoll mit Kontroll- und Tonic-Drive-Bedingung. Clean-Freeze, semantische Zuordnung und Tickvertrag sind erfüllt; Human Review und EVID-Gate bleiben offen.
26
+ - Beobachtete Conditions: `no_input_control, tonic_drive`
27
+
28
+ `DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
29
+
30
+ ## 3. Ausfuehrungsparameter
31
+
32
+ - Titel: RQ-SNN-001 frozen clean-tree replication
33
+ - Bedingungen: Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks
34
+ - Notizen: Exact frozen protocol rerun from a clean Git worktree.
35
+ - Konfiguration: `configs/learning_experiment.yaml`
36
+ - Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
37
+ - Git Commit: `4300e43de48ae08d59381830faf0a633e1ccfebf`
38
+ - Git dirty: `False`
39
+ - Runtime: `10.860079000005499` s
40
+
41
+ ## 4. Deterministische Formeln
42
+
43
+ Die im Bericht verwendeten deskriptiven Groessen sind:
44
+
45
+ - Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
46
+ - Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
47
+ - Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
48
+ - Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
49
+ - Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
50
+
51
+ Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
52
+
53
+ ## 5. Ergebnisse nach Bedingung
54
+
55
+ | Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
56
+ | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
57
+ | no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
58
+ | tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
59
+
60
+ ### 5.1 Deskriptive Zwei-Bedingungs-Effekte
61
+
62
+ - `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
63
+
64
+ ## 6. Einzelne Läufe
65
+
66
+ | Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
67
+ | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
68
+ | 42 | no_input_control | 100000 | — | — | — | — | — | — |
69
+ | 42 | tonic_drive | 100000 | — | — | — | — | — | — |
70
+ | 43 | no_input_control | 100000 | — | — | — | — | — | — |
71
+ | 43 | tonic_drive | 100000 | — | — | — | — | — | — |
72
+ | 44 | no_input_control | 100000 | — | — | — | — | — | — |
73
+ | 44 | tonic_drive | 100000 | — | — | — | — | — | — |
74
+ | 45 | no_input_control | 100000 | — | — | — | — | — | — |
75
+ | 45 | tonic_drive | 100000 | — | — | — | — | — | — |
76
+ | 46 | no_input_control | 100000 | — | — | — | — | — | — |
77
+ | 46 | tonic_drive | 100000 | — | — | — | — | — | — |
78
+ | 47 | no_input_control | 100000 | — | — | — | — | — | — |
79
+ | 47 | tonic_drive | 100000 | — | — | — | — | — | — |
80
+ | 48 | no_input_control | 100000 | — | — | — | — | — | — |
81
+ | 48 | tonic_drive | 100000 | — | — | — | — | — | — |
82
+ | 49 | no_input_control | 100000 | — | — | — | — | — | — |
83
+ | 49 | tonic_drive | 100000 | — | — | — | — | — | — |
84
+ | 50 | no_input_control | 100000 | — | — | — | — | — | — |
85
+ | 50 | tonic_drive | 100000 | — | — | — | — | — | — |
86
+ | 51 | no_input_control | 100000 | — | — | — | — | — | — |
87
+ | 51 | tonic_drive | 100000 | — | — | — | — | — | — |
88
+
89
+ ## 7. Reproduzierbarkeit und Provenienz
90
+
91
+ Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
92
+
93
+ Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
94
+
95
+ ## 8. AI Research Report
96
+
97
+ - AIRR Status: `unavailable`
98
+ - Wissenschaftliche Evidenz durch KI: `false`
99
+ - Human Review: `PENDING`
100
+
101
+ ## 9. Artefakte
102
+
103
+ - [analysis/ai_packet.json](analysis/ai_packet.json)
104
+ - [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
105
+ - [analysis/statistics.json](analysis/statistics.json)
106
+ - [DATA/current_run.json](DATA/current_run.json)
107
+ - [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
108
+ - [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
109
+ - [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
110
+ - [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
111
+ - [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
112
+ - [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
113
+ - [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
114
+ - [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
115
+ - [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
116
+ - [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
117
+ - [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
118
+ - [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
119
+ - [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
120
+ - [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
121
+ - [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
122
+ - [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
123
+ - [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
124
+ - [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
125
+ - [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
126
+ - [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
127
+ - [DATA/runs.json](DATA/runs.json)
128
+ - [DATA/runs_index.json](DATA/runs_index.json)
129
+ - [manifest.json](manifest.json)
130
+ - [report.md](report.md)
131
+ - [workflow.json](workflow.json)
132
+
133
+ ## 10. Wissenschaftliche Grenze und Schlussfolgerung
134
+
135
+ Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
136
+
137
+ **Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `DIRECT_MATCH`, Clean Freeze `dirty=false`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
research/experiments/EXP-SNN-001-R5/workflow.json ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment_id": "EXP-SNN-001-R5",
3
+ "research_question": "RQ-SNN-001",
4
+ "hypothesis": "H-SNN-001-A",
5
+ "title": "RQ-SNN-001 frozen clean-tree replication",
6
+ "conditions": "Frozen sustained_activity_stability_v1; no-input control versus tonic drive; 10 independent seeds; 100000 ticks",
7
+ "ticks": 100000,
8
+ "ticks_contract": "minimum requested observation window",
9
+ "seeds": [
10
+ 42,
11
+ 43,
12
+ 44,
13
+ 45,
14
+ 46,
15
+ 47,
16
+ 48,
17
+ 49,
18
+ 50,
19
+ 51
20
+ ],
21
+ "protocol": "sustained_activity_stability_v1",
22
+ "resolved_runner": "run_sustained_stability",
23
+ "tick_validation": {
24
+ "status": "SATISFIED",
25
+ "mode": "minimum_per_run_with_control_and_treatment",
26
+ "requested_ticks": 100000,
27
+ "observed_min": 100000,
28
+ "observed_max": 100000,
29
+ "run_count": 20
30
+ },
31
+ "notes": "Exact frozen protocol rerun from a clean Git worktree.",
32
+ "execution": "registered experiment_suite runner",
33
+ "assistant_policy": "AI is post-hoc interpretation only.",
34
+ "epistemic_layers": {
35
+ "ui_state": "operator/dashboard progress only; not a scientific result",
36
+ "data": "DATA artifacts and deterministic statistics are authoritative run outputs",
37
+ "evid": "not_created; requires semantic validation, clean freeze and human review",
38
+ "interpretation": "post-hoc AI or human interpretation; never execution input"
39
+ }
40
+ }
research/experiments/EXP-SNN-001/DATA/current_run.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "last_archived_run": 19,
3
+ "raw_artifact": {
4
+ "format": "json.gz",
5
+ "path": "DATA/raw/run-0019-tonic_drive-seed-51.json.gz",
6
+ "sha256": "4a7459aac83e21e1dc10c14f3630e845847defc8a60187bd1c3e520d059d65d7",
7
+ "size_bytes": 24779,
8
+ "uncompressed_bytes": 2102880
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+ },
10
+ "status": "idle"
11
+ }
research/experiments/EXP-SNN-001/DATA/runs_index.json ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "run_count": 20,
3
+ "runs": [
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+ {
5
+ "condition": "no_input_control",
6
+ "format": "json.gz",
7
+ "path": "DATA/raw/run-0000-no_input_control-seed-42.json.gz",
8
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+ {
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+ "condition": "tonic_drive",
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+ "format": "json.gz",
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18
+ "run_index": 1,
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+ "seed": 42,
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27
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28
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36
+ "format": "json.gz",
37
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38
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+ "seed": 44,
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+ "condition": "tonic_drive",
56
+ "format": "json.gz",
57
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+ "seed": 44,
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67
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+ "seed": 45,
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+ {
95
+ "condition": "tonic_drive",
96
+ "format": "json.gz",
97
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98
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+ "seed": 46,
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106
+ "format": "json.gz",
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108
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+ {
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+ "condition": "tonic_drive",
116
+ "format": "json.gz",
117
+ "path": "DATA/raw/run-0011-tonic_drive-seed-47.json.gz",
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+ "run_index": 11,
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+ "seed": 47,
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125
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+ "format": "json.gz",
127
+ "path": "DATA/raw/run-0012-no_input_control-seed-48.json.gz",
128
+ "run_index": 12,
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+ "seed": 48,
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+ "data": "101afffba2e4c59fb9264d9d3c51c5f1a5e3384157dc2b7e04e632692e4c563b",
65
+ "prompt": "dd18e473908e7e75b1717c240bac221729ea38753abfd28b701b37b20d5f5e14"
66
+ },
67
+ "research_questions": [
68
+ "RQ-SNN-001"
69
+ ],
70
+ "research_run_mode": "EXPLORATORY",
71
+ "results": {
72
+ "protocol": "sustained_activity_stability_v1",
73
+ "run_count": 20,
74
+ "runner": "run_sustained_stability",
75
+ "tick_contract": {
76
+ "mode": "protocol_defined_internal_trials",
77
+ "requested_ticks": 100000,
78
+ "status": "NOT_APPLICABLE"
79
+ },
80
+ "ticks_requested": 100000
81
+ },
82
+ "runtime": {
83
+ "duration_seconds": 12.384917100076564
84
+ },
85
+ "runtime_errors": [],
86
+ "simulation": {
87
+ "protocol": "sustained_activity_stability_v1",
88
+ "seed": 42,
89
+ "seeds": [
90
+ 42,
91
+ 43,
92
+ 44,
93
+ 45,
94
+ 46,
95
+ 47,
96
+ 48,
97
+ 49,
98
+ 50,
99
+ 51
100
+ ],
101
+ "ticks": 100000
102
+ },
103
+ "software": {
104
+ "brain5d_version": "unknown",
105
+ "os": "Windows 11",
106
+ "python": "3.13.14"
107
+ },
108
+ "source_freeze_sha": "da8f47e06ed2206157973cf458b1ca1624cdc8b752142698a80e4cec7b374d21",
109
+ "timestamp": "2026-09-06T21:07:05.930530",
110
+ "validity": {
111
+ "fatal_error_count": 0,
112
+ "reason": null,
113
+ "runtime_error_count": 0,
114
+ "valid": true
115
+ }
116
+ }
research/experiments/EXP-SNN-001/report.md ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001: Sustained activity stability v1
2
+
3
+ ## Forschungszuordnung
4
+ Forschungsfrage: `RQ-SNN-001`
5
+ Hypothese: `H-SNN-001-A`
6
+ Aufgeloester Runner: `run_sustained_stability`
7
+
8
+ ## Protokoll und Einstellungen
9
+ Protokoll: `sustained_activity_stability_v1`
10
+ Angeforderte Mindest-Ticks: `100000`
11
+ Seeds: `42, 43, 44, 45, 46, 47, 48, 49, 50, 51`
12
+ Tick-Vertrag: `{"mode": "protocol_defined_internal_trials", "requested_ticks": 100000, "status": "NOT_APPLICABLE"}`
13
+
14
+ ## Bedingungen
15
+ No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
16
+ Runs: 20; Dauer: 12.384917 s
17
+
18
+ ## Daten und Statistik
19
+ Kompakte Run-Projektion: `DATA/runs.json`
20
+ Unveränderlicher Rohdatenindex: `DATA/runs_index.json`
21
+ KI-Eingabepaket: `analysis/ai_packet.json` (hart begrenzt)
22
+ Deterministische deskriptive Statistik: `analysis/statistics.json`
23
+ Die Summary verbindet Rohdaten, Formeln, Einzelruns, Bedingungen, Reproduzierbarkeit und AIRR ohne KI-generierte Statistik.
24
+
25
+ ## Evidenzstatus
26
+ DATA, Manifest, Workflow und deterministische Statistik sind erzeugt. Wissenschaftliche EVID entsteht erst nach passender semantischer Zuordnung, Clean Freeze und Human Review.
27
+
28
+ ## Hinweise
29
+ Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.
research/experiments/EXP-SNN-001/summary.md ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # EXP-SNN-001: Wissenschaftliche Zusammenfassung
2
+
3
+ Diese Zusammenfassung wird deterministisch aus Manifest, Workflow, DATA und — sofern vorhanden — dem AIRR aufgebaut. Zahlen und Formeln stammen aus den gespeicherten Laufdaten beziehungsweise dem deterministischen Statistics Engine; KI-Text bleibt davon getrennt.
4
+
5
+ ## 1. Identifikation und Status
6
+
7
+ - Experimentstatus: `completed`
8
+ - Forschungsfrage: `RQ-SNN-001`
9
+ - Hypothese: `H-SNN-001-A`
10
+ - Protokoll: `sustained_activity_stability_v1`
11
+ - Durchlaeufe: `20`
12
+ - Seeds: `[42, 43, 44, 45, 46, 47, 48, 49, 50, 51]`
13
+ - Angeforderte Ticks: `100000`
14
+ - Tickgebundene SNN-Läufe (PING/TEMP/5D): `100000 .. 100000` ausgeführte Ticks
15
+ - Tick-Vertrag: `SATISFIED`
16
+ - TIME-Ladder: Der angeforderte Endpunkt wird je Seed separat validiert; Zwischenstufen duerfen kleiner sein.
17
+ - Trial-/Regulationsprotokolle: Interne Versuchszyklen sind nicht mit `ticks_executed` gleichzusetzen und werden nicht in die SNN-Tickspanne eingerechnet.
18
+ - Laufmodus: `EXPLORATORY`
19
+ - Netzwerkmodus: `OFFLINE`
20
+
21
+ ## 2. Semantische Konsistenz
22
+
23
+ - RQ/Condition-Pruefung: `MISMATCH`
24
+ - Evidence Readiness: `BLOCKED_SEMANTIC_MISMATCH`
25
+ - Begründung: RQ-SNN-001 fordert langfristig stabile Spike-Dynamik unter fortlaufender Aktivitaet. science_suite_v1/science_all_v1 bleiben dafuer diagnostisch; eine Primaerpruefung erfordert weiterhin ein dediziertes Sustained-Activity-Protokoll.
26
+ - Beobachtete Conditions: `no_input_control, tonic_drive`
27
+
28
+ `DIRECT_MATCH` bezeichnet einen gezielten RQ-spezifischen Lauf. `CONTAINS_MATCH` bedeutet, dass die passende Teilstudie innerhalb einer Gesamtsuite enthalten ist; nur diese Teilstudie ist primaer fuer die registrierte RQ auszuwerten. `MISMATCH` blockiert die Nutzung als Evidenz fuer die registrierte Forschungsfrage, auch wenn die technische Ausfuehrung fehlerfrei war.
29
+
30
+ ## 3. Ausfuehrungsparameter
31
+
32
+ - Titel: Sustained activity stability v1
33
+ - Bedingungen: No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.
34
+ - Notizen: Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.
35
+ - Konfiguration: `F:\Brain-5D\configs\learning_experiment.yaml`
36
+ - Config SHA-256: `6f6cd457caf6216c286fee47ef03b529a0b496ed2dd53ee96b85d3b51e7b3a1c`
37
+ - Git Commit: `68c72acda0cad461c5adb2b8b7bb5e70a3d15193`
38
+ - Git dirty: `True`
39
+ - Runtime: `12.384917100076564` s
40
+
41
+ ## 4. Deterministische Formeln
42
+
43
+ Die im Bericht verwendeten deskriptiven Groessen sind:
44
+
45
+ - Mittelwert: $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}x_i$
46
+ - Populationsstandardabweichung: $\sigma=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i-\bar{x})^2}$
47
+ - Absolute Differenz: $\Delta_x=\bar{x}_B-\bar{x}_A$
48
+ - Verhältnis: $R_x=\bar{x}_B/\bar{x}_A$ fuer $\bar{x}_A\neq0$
49
+ - Inter-Spike-Intervall: $ISI_i=t_{i+1}-t_i$
50
+
51
+ Diese Formeln sind deskriptiv. Ohne registrierten Inferenztest, unabhaengige Stichprobenannahme und passende Versuchsplanung werden daraus keine Signifikanz- oder Kausalbehauptungen abgeleitet.
52
+
53
+ ## 5. Ergebnisse nach Bedingung
54
+
55
+ | Condition | n | Seeds | Ticks mean | Spikes mean | Syn. events mean | Aktivierte Neuronen mean | Recurrent events mean | Propagation depth mean |
56
+ | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: |
57
+ | no_input_control | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
58
+ | tonic_drive | 10 | 42,43,44,45,46,47,48,49,50,51 | 100000 | — | — | — | — | — |
59
+
60
+ ### 5.1 Deskriptive Zwei-Bedingungs-Effekte
61
+
62
+ - `ticks_executed`: $\Delta=0$; $R=1$; Referenz `no_input_control`, Vergleich `tonic_drive`.
63
+
64
+ ## 6. Einzelne Läufe
65
+
66
+ | Seed | Condition | Ticks | Spikes | Syn. events | Aktivierte Neuronen | Recurrent events | Depth | Runtime error |
67
+ | ---: | --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |
68
+ | 42 | no_input_control | 100000 | — | — | — | — | — | — |
69
+ | 42 | tonic_drive | 100000 | — | — | — | — | — | — |
70
+ | 43 | no_input_control | 100000 | — | — | — | — | — | — |
71
+ | 43 | tonic_drive | 100000 | — | — | — | — | — | — |
72
+ | 44 | no_input_control | 100000 | — | — | — | — | — | — |
73
+ | 44 | tonic_drive | 100000 | — | — | — | — | — | — |
74
+ | 45 | no_input_control | 100000 | — | — | — | — | — | — |
75
+ | 45 | tonic_drive | 100000 | — | — | — | — | — | — |
76
+ | 46 | no_input_control | 100000 | — | — | — | — | — | — |
77
+ | 46 | tonic_drive | 100000 | — | — | — | — | — | — |
78
+ | 47 | no_input_control | 100000 | — | — | — | — | — | — |
79
+ | 47 | tonic_drive | 100000 | — | — | — | — | — | — |
80
+ | 48 | no_input_control | 100000 | — | — | — | — | — | — |
81
+ | 48 | tonic_drive | 100000 | — | — | — | — | — | — |
82
+ | 49 | no_input_control | 100000 | — | — | — | — | — | — |
83
+ | 49 | tonic_drive | 100000 | — | — | — | — | — | — |
84
+ | 50 | no_input_control | 100000 | — | — | — | — | — | — |
85
+ | 50 | tonic_drive | 100000 | — | — | — | — | — | — |
86
+ | 51 | no_input_control | 100000 | — | — | — | �� | — | — |
87
+ | 51 | tonic_drive | 100000 | — | — | — | — | — | — |
88
+
89
+ ## 7. Reproduzierbarkeit und Provenienz
90
+
91
+ Identische Ausgaben ueber mehrere Seeds dokumentieren reproduzierbare Modelltrajektorien unter diesen Bedingungen. Sie sind nicht automatisch statistisch unabhaengige Replikate. State-Digests sind Integritaets-/Identitaetsmarker und keine metrischen Zustandsabstaende.
92
+
93
+ Deterministische Statistikdatei: [`analysis/statistics.json`](analysis/statistics.json)
94
+
95
+ ## 8. AI Research Report
96
+
97
+ - AIRR Status: `unavailable`
98
+ - Wissenschaftliche Evidenz durch KI: `false`
99
+ - Human Review: `PENDING`
100
+
101
+ ## 9. Artefakte
102
+
103
+ - [analysis/ai_packet.json](analysis/ai_packet.json)
104
+ - [analysis/ai_packet_digest.json](analysis/ai_packet_digest.json)
105
+ - [analysis/statistics.json](analysis/statistics.json)
106
+ - [DATA/current_run.json](DATA/current_run.json)
107
+ - [DATA/raw/run-0000-no_input_control-seed-42.json.gz](DATA/raw/run-0000-no_input_control-seed-42.json.gz)
108
+ - [DATA/raw/run-0001-tonic_drive-seed-42.json.gz](DATA/raw/run-0001-tonic_drive-seed-42.json.gz)
109
+ - [DATA/raw/run-0002-no_input_control-seed-43.json.gz](DATA/raw/run-0002-no_input_control-seed-43.json.gz)
110
+ - [DATA/raw/run-0003-tonic_drive-seed-43.json.gz](DATA/raw/run-0003-tonic_drive-seed-43.json.gz)
111
+ - [DATA/raw/run-0004-no_input_control-seed-44.json.gz](DATA/raw/run-0004-no_input_control-seed-44.json.gz)
112
+ - [DATA/raw/run-0005-tonic_drive-seed-44.json.gz](DATA/raw/run-0005-tonic_drive-seed-44.json.gz)
113
+ - [DATA/raw/run-0006-no_input_control-seed-45.json.gz](DATA/raw/run-0006-no_input_control-seed-45.json.gz)
114
+ - [DATA/raw/run-0007-tonic_drive-seed-45.json.gz](DATA/raw/run-0007-tonic_drive-seed-45.json.gz)
115
+ - [DATA/raw/run-0008-no_input_control-seed-46.json.gz](DATA/raw/run-0008-no_input_control-seed-46.json.gz)
116
+ - [DATA/raw/run-0009-tonic_drive-seed-46.json.gz](DATA/raw/run-0009-tonic_drive-seed-46.json.gz)
117
+ - [DATA/raw/run-0010-no_input_control-seed-47.json.gz](DATA/raw/run-0010-no_input_control-seed-47.json.gz)
118
+ - [DATA/raw/run-0011-tonic_drive-seed-47.json.gz](DATA/raw/run-0011-tonic_drive-seed-47.json.gz)
119
+ - [DATA/raw/run-0012-no_input_control-seed-48.json.gz](DATA/raw/run-0012-no_input_control-seed-48.json.gz)
120
+ - [DATA/raw/run-0013-tonic_drive-seed-48.json.gz](DATA/raw/run-0013-tonic_drive-seed-48.json.gz)
121
+ - [DATA/raw/run-0014-no_input_control-seed-49.json.gz](DATA/raw/run-0014-no_input_control-seed-49.json.gz)
122
+ - [DATA/raw/run-0015-tonic_drive-seed-49.json.gz](DATA/raw/run-0015-tonic_drive-seed-49.json.gz)
123
+ - [DATA/raw/run-0016-no_input_control-seed-50.json.gz](DATA/raw/run-0016-no_input_control-seed-50.json.gz)
124
+ - [DATA/raw/run-0017-tonic_drive-seed-50.json.gz](DATA/raw/run-0017-tonic_drive-seed-50.json.gz)
125
+ - [DATA/raw/run-0018-no_input_control-seed-51.json.gz](DATA/raw/run-0018-no_input_control-seed-51.json.gz)
126
+ - [DATA/raw/run-0019-tonic_drive-seed-51.json.gz](DATA/raw/run-0019-tonic_drive-seed-51.json.gz)
127
+ - [DATA/runs.json](DATA/runs.json)
128
+ - [DATA/runs_index.json](DATA/runs_index.json)
129
+ - [manifest.json](manifest.json)
130
+ - [report.md](report.md)
131
+ - [workflow.json](workflow.json)
132
+
133
+ ## 10. Wissenschaftliche Grenze und Schlussfolgerung
134
+
135
+ Die technischen Laufdaten duerfen deskriptiv ausgewertet werden. Eine Hypothese gilt dadurch nicht automatisch als bestaetigt oder widerlegt. Kausale Aussagen sind nur fuer explizit kontrollierte Interventionen und nur innerhalb des simulierten Systems zulaessig; biologische Generalisierung erfordert zusaetzliche Evidenz. Die KI-Auswertung ist post-hoc und besitzt keine Evidenzfreigabe.
136
+
137
+ **Gesamtstatus:** technische Ausfuehrung `completed`, Tick-Vertrag `SATISFIED`, semantische Zuordnung `MISMATCH`, wissenschaftliche Evidenz `false` bis zur menschlichen Review/Freigabe.
research/experiments/EXP-SNN-001/workflow.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "experiment_id": "EXP-SNN-001",
3
+ "research_question": "RQ-SNN-001",
4
+ "hypothesis": "H-SNN-001-A",
5
+ "title": "Sustained activity stability v1",
6
+ "conditions": "No-input control vs tonic drive 50.0; 100000 ticks; 10000 tick burn-in; 1000 tick windows.",
7
+ "ticks": 100000,
8
+ "ticks_contract": "minimum requested observation window",
9
+ "seeds": [
10
+ 42,
11
+ 43,
12
+ 44,
13
+ 45,
14
+ 46,
15
+ 47,
16
+ 48,
17
+ 49,
18
+ 50,
19
+ 51
20
+ ],
21
+ "protocol": "sustained_activity_stability_v1",
22
+ "resolved_runner": "run_sustained_stability",
23
+ "tick_validation": {
24
+ "status": "NOT_APPLICABLE",
25
+ "mode": "protocol_defined_internal_trials",
26
+ "requested_ticks": 100000
27
+ },
28
+ "notes": "Frozen confirmatory protocol PREREG-SNN-001. Full per-tick spike trace retained; AI post-hoc only.",
29
+ "execution": "registered experiment_suite runner",
30
+ "assistant_policy": "AI is post-hoc interpretation only."
31
+ }