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+ "description": "Parquet dataset converted from Neuraxon Game of Life JSON save files. Contains comprehensive neural network state, agent behavior, and evolutionary data for validating the Neuraxon paper: 'A New Neural Growth & Computation Blueprint' by David Vivancos & Dr. Jose Sanchez.",
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+ "step_tick",
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+ "global_time_steps",
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+ "births_count",
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+ "deaths_count",
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+ "total_ltd_events",
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+ "peak_network_activity",
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+ "average_branching_ratio",
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+ "branching_ratio_samples",
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+ "peak_dopamine",
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+ "peak_serotonin",
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+ "peak_acetylcholine",
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+ "peak_norepinephrine",
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+ "total_silent_synapse_activations",
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+ "total_spontaneous_events",
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+ "total_dendritic_spikes",
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+ "total_homeostatic_adjustments",
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+ "peak_phase_coherence",
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+ "total_threshold_modulations",
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+ "description": "Main game metadata including summary statistics, neuromodulator peaks, and NxEr summaries. Key fields for paper validation across all sections.",
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+ "can_sea",
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+ "visited_count",
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+ "description": "Individual NxEr (agent) data including identity, position, state, sensory attributes, statistics, and I/O history. Essential for behavioral analysis.",
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+ "connection_probability",
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+ "w_slow_init_min",
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+ "w_slow_init_max",
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+ "w_meta_init_max",
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+ "learning_rate",
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+ "stdp_window",
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+ "plasticity_threshold",
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+ "associativity_strength",
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+ "synapse_integrity_threshold",
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+ "synapse_formation_prob",
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+ "synapse_death_prob",
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+ "neuron_death_threshold",
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+ "dopamine_baseline",
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+ "dopamine_high_affinity_threshold",
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+ "dopamine_low_affinity_threshold",
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+ "serotonin_baseline",
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+ "serotonin_high_affinity_threshold",
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+ "serotonin_low_affinity_threshold",
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+ "acetylcholine_baseline",
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+ "acetylcholine_high_affinity_threshold",
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+ "acetylcholine_low_affinity_threshold",
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+ "norepinephrine_baseline",
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+ "norepinephrine_high_affinity_threshold",
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+ "norepinephrine_low_affinity_threshold",
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+ "neuromod_decay_rate",
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+ "diffusion_rate",
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+ "oscillator_low_freq",
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+ "oscillator_mid_freq",
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+ "oscillator_high_freq",
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+ "oscillator_strength",
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+ "phase_coupling_strength",
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+ "energy_baseline",
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+ "firing_energy_cost",
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+ "plasticity_energy_cost",
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+ "metabolic_rate",
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+ "recovery_rate",
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+ "target_firing_rate",
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+ "homeostatic_plasticity_rate",
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+ "itu_circle_radius",
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+ "evolution_interval",
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+ "fitness_temporal_weight",
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+ "fitness_energy_weight",
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+ "fitness_pattern_weight",
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+ "max_axonal_delay",
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+ "dt",
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+ "simulation_steps",
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+ "activity_threshold",
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+ "net_time",
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+ "net_step_count",
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+ "net_energy_consumed",
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+ "net_branching_ratio",
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+ "net_num_synapses",
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+ "net_num_neurons"
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+ ],
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+ "description": "Complete network parameters for each NxEr including architecture, topology, neuron properties, synaptic time constants (Section 3), plasticity rates (Section 4), neuromodulation baselines (Section 1), oscillator frequencies (Section 7), and ITU config (Section 8).",
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+ "game_id",
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+ "neuron_id",
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+ "neuron_type",
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+ "membrane_potential",
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+ "trinary_state",
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+ "adaptation",
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+ "autoreceptor",
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+ "health",
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+ "is_active",
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+ "energy_level",
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+ "phase",
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+ "natural_frequency",
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+ "intrinsic_timescale",
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+ "fitness_score",
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+ "circle_id",
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+ "last_firing_time",
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+ "membrane_time_constant",
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+ "firing_threshold_excitatory",
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+ "adaptation_rate",
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+ "neuron_health_decay",
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+ "energy_baseline",
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+ "firing_energy_cost",
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+ "plasticity_energy_cost",
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+ "metabolic_rate",
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+ "recovery_rate",
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+ "state_history_json",
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+ "state_history_length",
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+ "dendritic_branches_json"
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+ ],
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+ "description": "Individual neuron state snapshots including membrane potential, trinary state, adaptation, autoreceptor levels, energy, phase, and individualized parameters. Relevant to Section 1 (trinary states), Section 2 (temporal dynamics), Section 6 (spontaneous activity).",
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+ "w_slow",
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+ "w_meta",
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+ "is_silent",
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+ "is_modulatory",
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+ "integrity",
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+ "axonal_delay",
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+ "learning_rate_mod",
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+ "synapse_type",
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+ "potential_delta_w",
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+ "tau_fast",
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+ "tau_slow",
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+ "tau_meta",
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+ "tau_ltp",
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+ "tau_ltd",
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+ "learning_rate",
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+ "plasticity_threshold",
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+ "pre_trace",
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+ "post_trace",
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+ "pre_trace_ltd",
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+ "associative_strength",
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+ "neighbor_synapse_ids_json",
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+ "neighbor_count"
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+ ],
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+ "description": "Synapse data including multi-timescale weights (w_fast, w_slow, w_meta), traces, integrity, and individualized time constants. Key for validating Section 3 (synaptic computation) and Section 4 (plasticity).",
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+ "filepath": "foods.parquet",
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+ "num_rows": 59490,
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+ "respawn_at_tick",
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+ "remaining",
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+ ],
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+ "description": "Food source state including position, remaining amount, and respawn status.",
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+ "filepath": "time_series.parquet",
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+ "total_energy",
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+ "energy_efficiency",
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+ "temporal_sync",
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+ "dopamine",
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+ "oscillator_drive",
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+ "phase_coherence",
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+ "cfc_low_mid",
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+ "excitatory_fraction",
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+ "spontaneous_firing_count",
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+ "driven_firing_count",
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+ "silent_synapse_count",
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+ "active_synapse_count",
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+ "modulatory_synapse_count",
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+ "mean_synapse_integrity",
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+ "mean_plateau_potential",
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+ "mean_branch_potential",
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+ "dendritic_spike_count",
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+ "mean_intrinsic_timescale",
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+ "timescale_heterogeneity",
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+ "membrane_potential_mean",
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+ "membrane_potential_std",
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+ "mean_w_fast",
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+ "mean_w_slow",
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+ "mean_w_meta",
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+ "std_w_fast",
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+ "std_w_slow",
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+ "std_w_meta",
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+ "mean_pre_trace",
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+ "mean_post_trace",
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+ "mean_pre_trace_ltd",
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+ "std_pre_trace",
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+ "mean_delta_w",
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+ "ltp_rate",
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+ "ltd_rate",
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+ "mean_associativity_contribution",
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+ "associativity_event_count",
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+ "mean_learning_rate_mod",
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+ "std_learning_rate_mod",
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+ "mean_autocorrelation_window",
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+ "std_autocorrelation_window",
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+ "autocorrelation_coefficient_mean",
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+ "mean_threshold_excitatory_effective",
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+ "mean_threshold_inhibitory_effective",
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+ "threshold_modulation_by_ach",
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+ "threshold_modulation_by_autoreceptor",
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+ "ionotropic_contribution_mean",
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+ "stats_food_found",
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+ "stats_food_taken",
1588
+ "stats_explored",
1589
+ "stats_time_lived_s",
1590
+ "stats_mates_performed",
1591
+ "stats_energy_efficiency",
1592
+ "stats_temporal_sync_score",
1593
+ "stats_fitness_score"
1594
+ ]
1595
+ }
1596
+ },
1597
+ "huggingface_compatibility": {
1598
+ "format": "parquet",
1599
+ "compression": "snappy",
1600
+ "load_example": "from datasets import load_dataset\ndataset = load_dataset('parquet', data_files={'train': '*.parquet'})",
1601
+ "pandas_example": "import pandas as pd\ndf = pd.read_parquet('games.parquet')"
1602
+ },
1603
+ "usage_notes": [
1604
+ "Time series data is indexed by tick number for temporal analysis",
1605
+ "Event tables can be joined to time_series via tick for correlation",
1606
+ "Network parameters table contains full configuration for each NxEr",
1607
+ "Use paper_section_mapping to identify relevant fields for each paper section",
1608
+ "game_id and round_number are consistent keys across all tables"
1609
+ ]
1610
+ }
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