Datasets:
Modalities:
Tabular
Formats:
parquet
Size:
1B - 10B
Tags:
time-series
artificial-life
neural-networks
evolutionary-algorithms
reinforcement-learning
biology
License:
Search is not available for this dataset
game_id int64 359k 1,000M | round_number int64 1 329 | tick int64 0 0 | pre_id int64 0 62 | post_id int64 0 63 | own_delta_w float64 -13.51 5.33 | neighbor_contribution float64 -4.68 2.45 | final_delta_w float64 -14.48 6.73 | amplification_factor float64 -317,101,804,803,835,760,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 61,640,320,963,234,905,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000B |
|---|---|---|---|---|---|---|---|---|
2,110,533 | 218 | 0 | 38 | 40 | 0.332971 | 0.148553 | 0.481524 | 1.446143 |
2,110,533 | 218 | 0 | 38 | 41 | 0.248502 | 0.161092 | 0.409594 | 1.648251 |
2,110,533 | 218 | 0 | 40 | 41 | 0.226206 | 0.077858 | 0.304064 | 1.344191 |
2,110,533 | 218 | 0 | 0 | 30 | 0.241574 | -0.742959 | -0.501384 | -2.075485 |
2,110,533 | 218 | 0 | 3 | 0 | -0 | -0.184321 | -0.184321 | 1,335,436.908886 |
2,110,533 | 218 | 0 | 4 | 29 | -0 | 0.090918 | 0.090918 | -21,301,543.248917 |
2,110,533 | 218 | 0 | 5 | 0 | -0 | 0.19569 | 0.19569 | -2,227,796.357294 |
2,110,533 | 218 | 0 | 23 | 1 | -2.918083 | 0.158911 | -2.759172 | 0.945543 |
2,110,533 | 218 | 0 | 23 | 19 | 0.267943 | 0.158911 | 0.426854 | 1.593075 |
2,110,533 | 218 | 0 | 26 | 31 | 0.190078 | 0.467173 | 0.657251 | 3.457791 |
2,110,533 | 218 | 0 | 30 | 31 | 0.302355 | -0.731113 | -0.428759 | -1.418066 |
2,110,533 | 218 | 0 | 30 | 40 | 0.215126 | -0.731113 | -0.515987 | -2.398536 |
2,110,533 | 218 | 0 | 37 | 8 | 0.277496 | 0.10721 | 0.384706 | 1.386348 |
2,110,533 | 218 | 0 | 38 | 33 | 0.282634 | 0.157714 | 0.440348 | 1.558014 |
2,110,533 | 218 | 0 | 39 | 38 | 0.231267 | 0.368203 | 0.59947 | 2.592112 |
2,110,533 | 218 | 0 | 40 | 39 | 0.262246 | 0.067158 | 0.329404 | 1.256088 |
2,110,533 | 218 | 0 | 41 | 26 | 0.227503 | 0.33446 | 0.561963 | 2.470135 |
2,110,533 | 218 | 0 | 0 | 2 | -0.189964 | -0.119565 | -0.309528 | 1.629408 |
2,110,533 | 218 | 0 | 0 | 3 | -0.518617 | -0.096492 | -0.615109 | 1.186057 |
2,110,533 | 218 | 0 | 1 | 2 | 0 | -0.108597 | -0.108597 | 0 |
2,110,533 | 218 | 0 | 1 | 14 | 0 | -0.108597 | -0.108597 | 0 |
2,110,533 | 218 | 0 | 1 | 4 | -0.960828 | -0.041144 | -1.001972 | 1.042822 |
2,110,533 | 218 | 0 | 4 | 5 | 0 | -0.032965 | -0.032965 | 0 |
2,110,533 | 218 | 0 | 16 | 17 | 0 | -0.106656 | -0.106656 | 0 |
2,110,533 | 218 | 0 | 16 | 19 | 0 | -0.106656 | -0.106656 | 0 |
2,110,533 | 218 | 0 | 1 | 5 | -0.879122 | -0.044968 | -0.92409 | 1.051151 |
2,110,533 | 218 | 0 | 4 | 27 | -0 | -0.016483 | -0.016483 | 50,400.484249 |
2,110,533 | 218 | 0 | 0 | 1 | -0.394992 | -0.158075 | -0.553067 | 1.400199 |
2,110,533 | 218 | 0 | 0 | 12 | -0.315692 | -0.170247 | -0.485939 | 1.539281 |
2,110,533 | 218 | 0 | 4 | 6 | 0 | -0.287038 | -0.287038 | 0 |
2,110,533 | 218 | 0 | 9 | 3 | -1.524671 | 0.002445 | -1.522226 | 0.998396 |
2,110,533 | 218 | 0 | 12 | 14 | 0 | 0.006811 | 0.006811 | 0 |
2,110,533 | 218 | 0 | 19 | 2 | -1.258188 | 0.003999 | -1.254189 | 0.996822 |
2,110,533 | 218 | 0 | 19 | 21 | 0 | -0.189117 | -0.189117 | 0 |
2,110,533 | 218 | 0 | 25 | 26 | 0 | 0.003575 | 0.003575 | 0 |
2,110,533 | 218 | 0 | 25 | 27 | 0 | 0.003575 | 0.003575 | 0 |
2,110,533 | 218 | 0 | 0 | 18 | -0.192639 | -0.151363 | -0.344002 | 1.785735 |
2,110,533 | 218 | 0 | 1 | 6 | -0.000001 | 0.00241 | 0.002409 | -4,744.028924 |
2,110,533 | 218 | 0 | 4 | 21 | 0 | -0.287038 | -0.287038 | 0 |
2,110,533 | 218 | 0 | 1 | 2 | 0 | -0.161245 | -0.161245 | 0 |
2,110,533 | 218 | 0 | 1 | 4 | 0 | -0.080622 | -0.080622 | 0 |
2,110,533 | 218 | 0 | 1 | 5 | 0 | -0.080622 | -0.080622 | 0 |
2,110,533 | 218 | 0 | 3 | 4 | -0.004044 | -0.001117 | -0.005161 | 1.276193 |
2,110,533 | 218 | 0 | 3 | 6 | -0.0008 | -0.001183 | -0.001983 | 2.478062 |
2,110,533 | 218 | 0 | 3 | 24 | -0.001563 | -0.001079 | -0.002642 | 1.690402 |
2,110,533 | 218 | 0 | 3 | 28 | -0.002946 | -0.001079 | -0.004025 | 1.366198 |
2,110,533 | 218 | 0 | 0 | 1 | 0 | -0.435526 | -0.435526 | 0 |
2,110,533 | 218 | 0 | 0 | 2 | -2.10444 | -0.106296 | -2.210735 | 1.05051 |
2,110,533 | 218 | 0 | 0 | 3 | -1.358888 | -0.164615 | -1.523503 | 1.121139 |
2,110,533 | 218 | 0 | 1 | 2 | -2.175396 | -0.301921 | -2.477317 | 1.138789 |
2,110,533 | 218 | 0 | 1 | 3 | -1.929883 | -0.340331 | -2.270214 | 1.176348 |
2,110,533 | 218 | 0 | 1 | 4 | 0 | -0.491291 | -0.491291 | 0 |
2,110,533 | 218 | 0 | 2 | 3 | 0 | -0.002658 | -0.002658 | 0 |
2,110,533 | 218 | 0 | 2 | 5 | 0 | -0.001329 | -0.001329 | 0 |
2,110,533 | 218 | 0 | 3 | 26 | -0.012447 | -0.001649 | -0.014097 | 1.1325 |
2,110,533 | 218 | 0 | 3 | 5 | 0 | -0.003597 | -0.003597 | 0 |
2,110,533 | 218 | 0 | 3 | 6 | -0.021084 | -0.001947 | -0.023031 | 1.092359 |
2,110,533 | 218 | 0 | 4 | 6 | 0 | -0.380275 | -0.380275 | 0 |
2,110,533 | 218 | 0 | 5 | 6 | -0.034525 | -0.12719 | -0.161714 | 4.684022 |
2,110,533 | 218 | 0 | 15 | 16 | 0 | -0.085678 | -0.085678 | 0 |
2,110,533 | 218 | 0 | 0 | 11 | 0 | -0.235479 | -0.235479 | 0 |
2,110,533 | 218 | 0 | 0 | 24 | 0 | -0.235479 | -0.235479 | 0 |
2,110,533 | 218 | 0 | 3 | 2 | 0 | -0.003047 | -0.003047 | 0 |
2,110,533 | 218 | 0 | 4 | 1 | 0 | -0.380275 | -0.380275 | 0 |
2,110,533 | 218 | 0 | 0 | 1 | 0 | -0.518917 | -0.518917 | 0 |
2,110,533 | 218 | 0 | 0 | 3 | 0 | -0.229845 | -0.229845 | 0 |
2,110,533 | 218 | 0 | 3 | 5 | -0 | 0.071234 | 0.071234 | -124,802,013.150688 |
2,110,533 | 218 | 0 | 30 | 31 | 0 | 0.07801 | 0.07801 | 0 |
2,110,533 | 218 | 0 | 30 | 32 | 0 | 0.07801 | 0.07801 | 0 |
2,110,533 | 218 | 0 | 30 | 33 | 0 | 0.07801 | 0.07801 | 0 |
2,110,533 | 218 | 0 | 32 | 33 | 0 | 0.075722 | 0.075722 | 0 |
2,110,533 | 218 | 0 | 0 | 2 | 0 | -0.085155 | -0.085155 | 0 |
2,110,533 | 218 | 0 | 4 | 5 | -0.389488 | 0.102367 | -0.287122 | 0.737176 |
2,110,533 | 218 | 0 | 0 | 1 | -0.15345 | -0.075486 | -0.228937 | 1.491927 |
2,110,533 | 218 | 0 | 0 | 2 | -0.162563 | -0.073461 | -0.236023 | 1.45189 |
2,110,533 | 218 | 0 | 1 | 2 | 0 | -0.098468 | -0.098468 | 0 |
2,110,533 | 218 | 0 | 1 | 4 | 0 | -0.049234 | -0.049234 | 0 |
2,110,533 | 218 | 0 | 2 | 4 | 0 | -0.073479 | -0.073479 | 0 |
2,110,533 | 218 | 0 | 2 | 5 | 0 | -0.073479 | -0.073479 | 0 |
2,110,533 | 218 | 0 | 4 | 5 | 0 | -0.045188 | -0.045188 | 0 |
2,110,533 | 218 | 0 | 2 | 1 | 0 | -0.073479 | -0.073479 | 0 |
2,110,533 | 218 | 0 | 2 | 14 | 0 | -0.073479 | -0.073479 | 0 |
2,110,533 | 218 | 0 | 4 | 15 | 0 | -0.03008 | -0.03008 | 0 |
2,110,533 | 218 | 0 | 4 | 16 | 0 | -0.03008 | -0.03008 | 0 |
2,110,533 | 218 | 0 | 0 | 1 | -0.384355 | -0.180643 | -0.564998 | 1.469989 |
2,110,533 | 218 | 0 | 0 | 2 | -0.373415 | -0.183824 | -0.557239 | 1.492277 |
2,110,533 | 218 | 0 | 0 | 3 | -0.495714 | -0.166044 | -0.661757 | 1.334959 |
2,110,533 | 218 | 0 | 0 | 4 | 0 | -0.214089 | -0.214089 | 0 |
2,110,533 | 218 | 0 | 0 | 5 | -0.397359 | -0.214089 | -0.611447 | 1.538779 |
2,110,533 | 218 | 0 | 3 | 4 | -0 | -0.004268 | -0.004268 | 5,464,485,638,894.853 |
2,110,533 | 218 | 0 | 3 | 5 | 0 | -0.004268 | -0.004268 | 0 |
2,110,533 | 218 | 0 | 4 | 5 | -0.435438 | -0.018091 | -0.453529 | 1.041547 |
2,110,533 | 218 | 0 | 2 | 3 | -0 | -0.012809 | -0.012809 | 0 |
2,110,533 | 218 | 0 | 2 | 4 | -0 | -0.012809 | -0.012809 | 0 |
2,110,533 | 218 | 0 | 4 | 5 | -0 | -0.014093 | -0.014093 | 0 |
2,110,533 | 218 | 0 | 0 | 4 | 0 | -0.206419 | -0.206419 | 0 |
2,110,533 | 218 | 0 | 1 | 2 | -1.157312 | -0.330211 | -1.487523 | 1.285326 |
2,110,533 | 218 | 0 | 1 | 3 | -1.4217 | -0.268803 | -1.690503 | 1.189072 |
2,110,533 | 218 | 0 | 1 | 4 | 0 | -0.433909 | -0.433909 | 0 |
2,110,533 | 218 | 0 | 1 | 55 | 0 | -0.433909 | -0.433909 | 0 |
End of preview. Expand
in Data Studio
NeuraxonLife2.5-100K-DeepTimeSeries: Artificial Life Neuraxon Neural Network Simulation Deep Time Series Dataset (Active Binary Exploration)
The Dataset is a companion suporting material for the upcoming paper: "The Neutral Buffer State: Trinary Logic Advantage in Branching Ratio Stability for Continuous-Time Networks" by David Vivancos & Dr José Sanchez
Dataset Description
The NeuraxonLife 2.5 Deep Time Series Dataset is a massive, comprehensive collection of simulation data from the Neuraxon Game of Life environment. It tracks the evolution of over 100,000 autonomous agents ("NxErs") evolving biologically-plausible neural networks under survival pressures.
This dataset represents a significant expansion over previous versions, featuring "Deep Time Series" exploration with over 279 million plasticity events and high-resolution per-agent time series data.
Also validatie the original Neuraxon 1.0 paper: 'A New Neural Growth & Computation Blueprint' by David Vivancos https://www.vivancos.com/ & Dr. Jose Sanchez https://josesanchezgarcia.com/ for Qubic Science. https://qubic.org/
Paper Reference: Neuraxon: A New Neural Growth & Computation Blueprint
Dataset Summary
This dataset provides granular insights into emergent neural computation in an artificial life setting. It covers 2,791 distinct simulation games involving complex neural phenomena.
Citation
@dataset{NeuraxonLife2.5-TimeSeriesActiveBinary,
title={Neuraxon Game of Life 2.5 Research Dataset: Deep Time Series Exploration Active Binnary Exploration},
author={Vivancos, David and Sanchez, Jose},
year={2026},
publisher={Hugging Face},
version={2.5.0},
url={https://huggingface.co/datasets/DavidVivancos/NeuraxonLife2.5ActiveBinary}
}
Authors & Curators
- David Vivancos / Artificiology Research - Qubic Science
- Dr. Jose Sanchez / UNIR - Qubic Science Contact: For questions or issues, please open a GitHub issue at https://github.com/DavidVivancos/Neuraxon.
- Downloads last month
- 6