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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
meta: struct<created: string, type: string, session_id: string, game_id: string>
child 0, created: string
child 1, type: string
child 2, session_id: string
child 3, game_id: string
nxer: struct<id: int64, name: string, color: list<item: int64>, pos: list<item: int64>, can_land: bool, ca (... 53088 chars omitted)
child 0, id: int64
child 1, name: string
child 2, color: list<item: int64>
child 0, item: int64
child 3, pos: list<item: int64>
child 0, item: int64
child 4, can_land: bool
child 5, can_sea: bool
child 6, food: double
child 7, is_male: bool
child 8, alive: bool
child 9, born_ts: double
child 10, died_ts: null
child 11, last_inputs: list<item: int64>
child 0, item: int64
child 12, last_outputs: list<item: int64>
child 0, item: int64
child 13, ticks_per_action: int64
child 14, tick_accum: int64
child 15, harvesting: null
child 16, mating_with: null
child 17, mating_end_tick: null
child 18, visited: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 19, dopamine_boost_ticks: int64
child 20, _last_O4: int64
child 21, mating_intent_until_tick: int64
child 22, parents: list<item: null>
child 0, item: null
child 23, parent_ids: list<item: null>
child 0, item: null
child 24, parent_0: null
child 25, parent_1: null
child 26, parent_name_0: null
child 27, parent_name_1: null
child 28, ancestors: list<item: null>
child
...
child 5, trial_id: int64
child 6, fitness: double
child 7, saved_at: timestamp[s]
child 8, notes: list<item: string>
child 0, item: string
genetic_lottery: struct<_doc: string, metabolic_rate_multiplier_range: list<item: double>, _doc_metabolic_rate_multip (... 226 chars omitted)
child 0, _doc: string
child 1, metabolic_rate_multiplier_range: list<item: double>
child 0, item: double
child 2, _doc_metabolic_rate_multiplier_range: string
child 3, intrinsic_timescale_jitter: double
child 4, _doc_intrinsic_timescale_jitter: string
child 5, firing_threshold_jitter: double
child 6, _doc_firing_threshold_jitter: string
child 7, mutation_strength: double
child 8, _doc_mutation_strength: string
biology: struct<_doc: string, metabolic_ramp_per_sec: double, _doc_metabolic_ramp_per_sec: string, max_atroph (... 374 chars omitted)
child 0, _doc: string
child 1, metabolic_ramp_per_sec: double
child 2, _doc_metabolic_ramp_per_sec: string
child 3, max_atrophy: double
child 4, _doc_max_atrophy: string
child 5, metabolic_rate_abs_cap_multiple: double
child 6, _doc_metabolic_rate_abs_cap_multiple: string
child 7, start_food_default: double
child 8, food_respawn_default: int64
child 9, food_sources_default: int64
child 10, _doc_food: string
child 11, mate_cooldown_seconds: int64
child 12, circadian_cycle_ticks: int64
child 13, idle_explore_seconds: double
child 14, explore_probability: double
child 15, _doc_idle_explore: string
to
{'_meta': {'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'source': Value('string'), 'rank': Value('int64'), 'trial_id': Value('int64'), 'fitness': Value('float64'), 'saved_at': Value('timestamp[s]'), 'notes': List(Value('string'))}, 'biology': {'_doc': Value('string'), 'metabolic_ramp_per_sec': Value('float64'), '_doc_metabolic_ramp_per_sec': Value('string'), 'max_atrophy': Value('float64'), '_doc_max_atrophy': Value('string'), 'metabolic_rate_abs_cap_multiple': Value('float64'), '_doc_metabolic_rate_abs_cap_multiple': Value('string'), 'start_food_default': Value('float64'), 'food_respawn_default': Value('int64'), 'food_sources_default': Value('int64'), '_doc_food': Value('string'), 'mate_cooldown_seconds': Value('int64'), 'circadian_cycle_ticks': Value('int64'), 'idle_explore_seconds': Value('float64'), 'explore_probability': Value('float64'), '_doc_idle_explore': Value('string')}, 'neural': {'_doc': Value('string'), 'num_input_neurons': Value('int64'), 'num_output_neurons': Value('int64'), 'num_hidden_neurons_default': Value('int64'), '_doc_neuron_counts': Value('string'), 'connection_probability': Value('float64'), 'afferent_synapse_strength': Value('float64'), 'proprioceptive_afferent_gain': Value('float64'), 'sensory_input_gain': Value('float64'), 'firing_threshold_excitatory': Value('float64'), 'firing_threshold_inhibitory': Value('float64'), 'spontaneous_firing_rate': Value('float64'), 'intrinsic_timescale_default': Value('float64
...
at64'), 'autoreceptor_rate_coeff': Value('float64'), '_doc_autoreceptor': Value('string'), 'sensory_boost_function': Value('string'), 'sensory_boost_scale': Value('float64'), '_doc_sensory_boost': Value('string'), 'plasticity_brake_threshold': Value('float64'), 'plasticity_brake_slope': Value('float64'), 'plasticity_brake_floor': Value('float64'), '_doc_plasticity_brake': Value('string')}, 'genetic_lottery': {'_doc': Value('string'), 'metabolic_rate_multiplier_range': List(Value('float64')), '_doc_metabolic_rate_multiplier_range': Value('string'), 'intrinsic_timescale_jitter': Value('float64'), '_doc_intrinsic_timescale_jitter': Value('string'), 'firing_threshold_jitter': Value('float64'), '_doc_firing_threshold_jitter': Value('string'), 'mutation_strength': Value('float64'), '_doc_mutation_strength': Value('string')}, 'healthy_bands': {'_doc': Value('string'), 'M1_excitatory_fraction': List(Value('float64')), 'M2_mean_gate': List(Value('float64')), 'M3_pac_modulation_idx': List(Value('float64')), 'M5_branching_ratio': List(Value('float64')), 'M6_spontaneous_fraction': List(Value('float64')), 'M7_zero_input_mi_ratio': List(Value('float64')), 'M9_transfer_ratio': List(Value('float64')), 'M10_heritability_r': List(Value('float64')), 'sensory_motor_corr': List(Value('float64')), 'pop_mean_idle_seconds': List(Value('float64')), 'input_saturation_fraction': List(Value('float64')), 'input_locked_fraction': List(Value('float64')), 'exploration_trigger_rate': List(Value('float64'))}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
meta: struct<created: string, type: string, session_id: string, game_id: string>
child 0, created: string
child 1, type: string
child 2, session_id: string
child 3, game_id: string
nxer: struct<id: int64, name: string, color: list<item: int64>, pos: list<item: int64>, can_land: bool, ca (... 53088 chars omitted)
child 0, id: int64
child 1, name: string
child 2, color: list<item: int64>
child 0, item: int64
child 3, pos: list<item: int64>
child 0, item: int64
child 4, can_land: bool
child 5, can_sea: bool
child 6, food: double
child 7, is_male: bool
child 8, alive: bool
child 9, born_ts: double
child 10, died_ts: null
child 11, last_inputs: list<item: int64>
child 0, item: int64
child 12, last_outputs: list<item: int64>
child 0, item: int64
child 13, ticks_per_action: int64
child 14, tick_accum: int64
child 15, harvesting: null
child 16, mating_with: null
child 17, mating_end_tick: null
child 18, visited: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 19, dopamine_boost_ticks: int64
child 20, _last_O4: int64
child 21, mating_intent_until_tick: int64
child 22, parents: list<item: null>
child 0, item: null
child 23, parent_ids: list<item: null>
child 0, item: null
child 24, parent_0: null
child 25, parent_1: null
child 26, parent_name_0: null
child 27, parent_name_1: null
child 28, ancestors: list<item: null>
child
...
child 5, trial_id: int64
child 6, fitness: double
child 7, saved_at: timestamp[s]
child 8, notes: list<item: string>
child 0, item: string
genetic_lottery: struct<_doc: string, metabolic_rate_multiplier_range: list<item: double>, _doc_metabolic_rate_multip (... 226 chars omitted)
child 0, _doc: string
child 1, metabolic_rate_multiplier_range: list<item: double>
child 0, item: double
child 2, _doc_metabolic_rate_multiplier_range: string
child 3, intrinsic_timescale_jitter: double
child 4, _doc_intrinsic_timescale_jitter: string
child 5, firing_threshold_jitter: double
child 6, _doc_firing_threshold_jitter: string
child 7, mutation_strength: double
child 8, _doc_mutation_strength: string
biology: struct<_doc: string, metabolic_ramp_per_sec: double, _doc_metabolic_ramp_per_sec: string, max_atroph (... 374 chars omitted)
child 0, _doc: string
child 1, metabolic_ramp_per_sec: double
child 2, _doc_metabolic_ramp_per_sec: string
child 3, max_atrophy: double
child 4, _doc_max_atrophy: string
child 5, metabolic_rate_abs_cap_multiple: double
child 6, _doc_metabolic_rate_abs_cap_multiple: string
child 7, start_food_default: double
child 8, food_respawn_default: int64
child 9, food_sources_default: int64
child 10, _doc_food: string
child 11, mate_cooldown_seconds: int64
child 12, circadian_cycle_ticks: int64
child 13, idle_explore_seconds: double
child 14, explore_probability: double
child 15, _doc_idle_explore: string
to
{'_meta': {'name': Value('string'), 'version': Value('string'), 'description': Value('string'), 'source': Value('string'), 'rank': Value('int64'), 'trial_id': Value('int64'), 'fitness': Value('float64'), 'saved_at': Value('timestamp[s]'), 'notes': List(Value('string'))}, 'biology': {'_doc': Value('string'), 'metabolic_ramp_per_sec': Value('float64'), '_doc_metabolic_ramp_per_sec': Value('string'), 'max_atrophy': Value('float64'), '_doc_max_atrophy': Value('string'), 'metabolic_rate_abs_cap_multiple': Value('float64'), '_doc_metabolic_rate_abs_cap_multiple': Value('string'), 'start_food_default': Value('float64'), 'food_respawn_default': Value('int64'), 'food_sources_default': Value('int64'), '_doc_food': Value('string'), 'mate_cooldown_seconds': Value('int64'), 'circadian_cycle_ticks': Value('int64'), 'idle_explore_seconds': Value('float64'), 'explore_probability': Value('float64'), '_doc_idle_explore': Value('string')}, 'neural': {'_doc': Value('string'), 'num_input_neurons': Value('int64'), 'num_output_neurons': Value('int64'), 'num_hidden_neurons_default': Value('int64'), '_doc_neuron_counts': Value('string'), 'connection_probability': Value('float64'), 'afferent_synapse_strength': Value('float64'), 'proprioceptive_afferent_gain': Value('float64'), 'sensory_input_gain': Value('float64'), 'firing_threshold_excitatory': Value('float64'), 'firing_threshold_inhibitory': Value('float64'), 'spontaneous_firing_rate': Value('float64'), 'intrinsic_timescale_default': Value('float64
...
at64'), 'autoreceptor_rate_coeff': Value('float64'), '_doc_autoreceptor': Value('string'), 'sensory_boost_function': Value('string'), 'sensory_boost_scale': Value('float64'), '_doc_sensory_boost': Value('string'), 'plasticity_brake_threshold': Value('float64'), 'plasticity_brake_slope': Value('float64'), 'plasticity_brake_floor': Value('float64'), '_doc_plasticity_brake': Value('string')}, 'genetic_lottery': {'_doc': Value('string'), 'metabolic_rate_multiplier_range': List(Value('float64')), '_doc_metabolic_rate_multiplier_range': Value('string'), 'intrinsic_timescale_jitter': Value('float64'), '_doc_intrinsic_timescale_jitter': Value('string'), 'firing_threshold_jitter': Value('float64'), '_doc_firing_threshold_jitter': Value('string'), 'mutation_strength': Value('float64'), '_doc_mutation_strength': Value('string')}, 'healthy_bands': {'_doc': Value('string'), 'M1_excitatory_fraction': List(Value('float64')), 'M2_mean_gate': List(Value('float64')), 'M3_pac_modulation_idx': List(Value('float64')), 'M5_branching_ratio': List(Value('float64')), 'M6_spontaneous_fraction': List(Value('float64')), 'M7_zero_input_mi_ratio': List(Value('float64')), 'M9_transfer_ratio': List(Value('float64')), 'M10_heritability_r': List(Value('float64')), 'sensory_motor_corr': List(Value('float64')), 'pop_mean_idle_seconds': List(Value('float64')), 'input_saturation_fraction': List(Value('float64')), 'input_locked_fraction': List(Value('float64')), 'exploration_trigger_rate': List(Value('float64'))}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MultiNxon2 — NAS Runs for the Neuraxon Game of Life
This dataset contains the raw outputs of many Neural Architecture Search (NAS)
runs for the Neuraxon Game of Life — an artificial-life simulation where
small spiking "brains" (NxErs) live, eat, mate and explore a world. Each NAS run
searches over brain hyper-parameters to find the architecture that best survives
and behaves, scored by a single fitness value plus a set of biology-inspired
metrics (M1–M10).
The runs were collected over several days and span Neuraxon internal versions v166 → v195. Each version tweaks the search space or the fitness/selection logic, so the collection is also a longitudinal record of how the search itself evolved.
Folder layout
One NAS run = one folder named by its start timestamp:
20260512_212222/
20260514_075838/
...
20260531_163524/
Each run folder contains:
| File / folder | What it is |
|---|---|
nas_log.csv |
Main results table — one row per trial (architecture). Start here. |
nas_best.json |
The single best architecture of the run. |
nas_top1.json, nas_top2.json, nas_top3.json |
The top-3 architectures. |
trial_NNN__arch.json |
The full hyper-parameters for trial NNN. |
trial_NNN/ |
Per-trial game outputs (the actual simulation logs for that architecture). |
Inside a trial_NNN/ folder (newer runs nest seed repeats as rep0/, rep1/,
rep2/; older runs use sibling trial_NNN_s<seed>/ folders):
| File | What it is |
|---|---|
nxon2_<id>__BestFitness.json |
Best-of-run game state for each scoring dimension: |
..._BestFoodFound.json, ..._BestFoodTaken.json |
…food found / taken, |
..._BestMates.json, ..._BestTimeLived.json, ..._BestWorldExplorer.json |
…mates, lifespan, exploration. |
..._KeyMetrics.txt |
Human-readable summary of the run's key metrics. |
..._LifespanLog.txt |
Per-agent lifespan / population log. |
..._MembraneDiag.txt |
Membrane / firing diagnostics. |
..._Completed_<timestamp>.json |
Final completed game record. |
nas_log.csv columns (the important ones)
nas_log.csv has ~52 columns. The key ones:
trial_id— index of the trial within the run.fitness— the value being optimised (higher is better).arch_summary— the full hyper-parameter string for that architecture (firing thresholds, connection probability, learning rate, topology, etc.).is_global_best—1for the run's winning architecture.M1–M10— paper-fidelity metrics (e.g.M1= excitatory firing band,M1_neutral/M1_inh= rest / inhibition fractions, plus synchrony, plasticity and other dynamics measures).- Survival / behaviour:
final_alive,peak_alive,alive_mean,went_extinct,surv_score,expl_rate. - Reliability:
n_repeats,n_repeats_ok,fitness_std_reps,M1_std(later versions re-run the same architecture across seeds to average out noise). - Bookkeeping:
completed_at_iso,wall_actual_s,total_rounds,error.
Quick start
import pandas as pd
# point at any run folder
df = pd.read_csv("20260531_163524/nas_log.csv")
# best architecture in that run
best = df.sort_values("fitness", ascending=False).iloc[0]
print(best["fitness"], best["arch_summary"])
Notes
- All paths in the original logs use
\...; that prefix is just the source drive and can be ignored. - This is raw research output: column sets and folder conventions shift slightly between versions as the search evolved.
License
Released under CC-BY-4.0-SA
Citation
@dataset{NeuraxonGameOFLifeResearhNAS-5-MultiNxon2NAS
title={Neuraxon Game of Life 5 Research Dataset: NAS Multi Nxon Exploration},
author={Vivancos, David and Sanchez, Jose},
year={2026},
publisher={Hugging Face},
version={1.0.0},
url={https://huggingface.co/datasets/DavidVivancos/MultiNxon2NAS}
}
Authors & Curators
- David Vivancos / Artificiology Research (https://artificiology.com) - Qubic Science
- Dr. Jose Sanchez / UNIR - Qubic Science Contact: For questions or issues, please open a GitHub issue at https://github.com/DavidVivancos/Neuraxon.
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