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Tags:
time-series
artificial-life
neural-networks
evolutionary-algorithms
reinforcement-learning
biology
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game_id int64 230k 1,000M | round_number int64 1 299 | tick int64 0 0 | pre_id int64 0 62 | post_id int64 0 63 | own_delta_w float64 -6.65 5.21 | neighbor_contribution float64 -2.56 3.11 | final_delta_w float64 -6.95 7.37 | amplification_factor float64 -1,168,566.44 354k |
|---|---|---|---|---|---|---|---|---|
3,362,944 | 195 | 0 | 18 | 22 | 0 | 0.029716 | 0.029716 | 0 |
3,362,944 | 195 | 0 | 10 | 5 | -0.68969 | 0.008065 | -0.681625 | 0.988307 |
3,362,944 | 195 | 0 | 10 | 18 | 0 | 0.008065 | 0.008065 | 0 |
3,362,944 | 195 | 0 | 17 | 35 | 0 | 0.015856 | 0.015856 | 0 |
3,362,944 | 195 | 0 | 23 | 27 | 0 | -0.035608 | -0.035608 | 0 |
3,362,944 | 195 | 0 | 48 | 49 | 0 | -0.02229 | -0.02229 | 0 |
3,362,944 | 195 | 0 | 49 | 50 | -0.109234 | -0.021186 | -0.13042 | 1.193953 |
3,362,944 | 195 | 0 | 49 | 26 | 0 | -0.044269 | -0.044269 | 0 |
3,362,944 | 195 | 0 | 0 | 1 | -0.130816 | -0.024751 | -0.155567 | 1.189208 |
3,362,944 | 195 | 0 | 0 | 2 | -0.218238 | -0.01782 | -0.236058 | 1.081655 |
3,362,944 | 195 | 0 | 0 | 3 | -0.187906 | -0.019023 | -0.206929 | 1.101235 |
3,362,944 | 195 | 0 | 4 | 5 | -0.160366 | 0.011299 | -0.149067 | 0.929544 |
3,362,944 | 195 | 0 | 6 | 3 | 0 | 0.008305 | 0.008305 | 0 |
3,362,944 | 195 | 0 | 9 | 11 | 0.127734 | 0.010006 | 0.13774 | 1.078334 |
3,362,944 | 195 | 0 | 9 | 12 | 0.077273 | 0.012006 | 0.089279 | 1.155375 |
3,362,944 | 195 | 0 | 11 | 12 | 0.099925 | 0.01496 | 0.114885 | 1.149713 |
3,362,944 | 195 | 0 | 11 | 14 | 0.101663 | 0.013387 | 0.11505 | 1.131683 |
3,362,944 | 195 | 0 | 12 | 14 | 0.136447 | 0.017908 | 0.154355 | 1.131244 |
3,362,944 | 195 | 0 | 14 | 17 | 0.130637 | 0.016226 | 0.146864 | 1.124208 |
3,362,944 | 195 | 0 | 17 | 19 | -0.116311 | 0.023242 | -0.093069 | 0.800173 |
3,362,944 | 195 | 0 | 28 | 29 | -0.17198 | -0.013056 | -0.185037 | 1.075918 |
3,362,944 | 195 | 0 | 28 | 30 | -0.176701 | -0.012682 | -0.189383 | 1.071771 |
3,362,944 | 195 | 0 | 28 | 31 | 0.097671 | -0.023559 | 0.074112 | 0.758796 |
3,362,944 | 195 | 0 | 31 | 33 | 0.104867 | -0.007585 | 0.097282 | 0.927668 |
3,362,944 | 195 | 0 | 37 | 38 | 0.127385 | -0.004917 | 0.122468 | 0.961397 |
3,362,944 | 195 | 0 | 37 | 39 | -0.182032 | 0.019614 | -0.162418 | 0.89225 |
3,362,944 | 195 | 0 | 38 | 39 | -0.190556 | -0.009442 | -0.199998 | 1.049548 |
3,362,944 | 195 | 0 | 38 | 40 | -0.211723 | -0.007763 | -0.219486 | 1.036668 |
3,362,944 | 195 | 0 | 38 | 41 | 0.118107 | -0.020838 | 0.097268 | 0.823563 |
3,362,944 | 195 | 0 | 17 | 0 | 0.120486 | 0.012994 | 0.13348 | 1.107844 |
3,362,944 | 195 | 0 | 23 | 17 | 0.088526 | 0.016972 | 0.105498 | 1.191712 |
3,362,944 | 195 | 0 | 28 | 1 | -0.110447 | -0.018059 | -0.128506 | 1.163504 |
3,362,944 | 195 | 0 | 31 | 38 | 0.103966 | -0.007458 | 0.096508 | 0.928265 |
3,362,944 | 195 | 0 | 38 | 14 | 0.100744 | -0.02038 | 0.080365 | 0.79771 |
3,362,944 | 195 | 0 | 4 | 2 | -0.590026 | -0.04093 | -0.630957 | 1.069371 |
3,362,944 | 195 | 0 | 4 | 14 | -0.574085 | -0.042067 | -0.616152 | 1.073277 |
3,362,944 | 195 | 0 | 10 | 12 | -0.439469 | -0.051157 | -0.490626 | 1.116406 |
3,362,944 | 195 | 0 | 10 | 1 | -0.509908 | -0.048646 | -0.558553 | 1.095401 |
3,362,944 | 195 | 0 | 4 | 5 | 0 | 0.082595 | 0.082595 | 0 |
3,362,944 | 195 | 0 | 5 | 2 | -0.499767 | -0.094987 | -0.594754 | 1.190062 |
3,362,944 | 195 | 0 | 10 | 14 | 0.760328 | -0.005569 | 0.75476 | 0.992676 |
3,362,944 | 195 | 0 | 14 | 35 | -0.402164 | -0.196698 | -0.598862 | 1.489098 |
3,362,944 | 195 | 0 | 14 | 17 | -0.522413 | -0.135103 | -0.657517 | 1.258614 |
3,362,944 | 195 | 0 | 5 | 34 | 0.568286 | -0.094987 | 0.473299 | 0.832854 |
3,362,944 | 195 | 0 | 10 | 17 | -0.331629 | -0.005569 | -0.337197 | 1.016792 |
3,362,944 | 195 | 0 | 14 | 5 | 0.697212 | -0.208418 | 0.488794 | 0.701069 |
3,362,944 | 195 | 0 | 6 | 7 | -2.122603 | 0.410176 | -1.712426 | 0.806758 |
3,362,944 | 195 | 0 | 6 | 10 | 1.033562 | -0.194882 | 0.838679 | 0.811446 |
3,362,944 | 195 | 0 | 7 | 10 | 0 | 0.085409 | 0.085409 | 0 |
3,362,944 | 195 | 0 | 7 | 11 | 0 | 0.085409 | 0.085409 | 0 |
3,362,944 | 195 | 0 | 10 | 11 | 0.911393 | -0.409052 | 0.502341 | 0.55118 |
3,362,944 | 195 | 0 | 10 | 12 | -1.491029 | 0.017459 | -1.473571 | 0.988291 |
3,362,944 | 195 | 0 | 10 | 14 | -1.389958 | -0.011944 | -1.401902 | 1.008593 |
3,362,944 | 195 | 0 | 11 | 12 | -1.213216 | -0.324505 | -1.537721 | 1.267475 |
3,362,944 | 195 | 0 | 11 | 15 | -0.987097 | -0.365453 | -1.352551 | 1.370231 |
3,362,944 | 195 | 0 | 12 | 14 | 0 | -0.020582 | -0.020582 | 0 |
3,362,944 | 195 | 0 | 12 | 15 | 0 | -0.020582 | -0.020582 | 0 |
3,362,944 | 195 | 0 | 22 | 23 | -1.380578 | -0.24916 | -1.629737 | 1.180475 |
3,362,944 | 195 | 0 | 22 | 24 | -1.775788 | -0.178997 | -1.954784 | 1.100798 |
3,362,944 | 195 | 0 | 22 | 25 | 1.309403 | -0.452859 | 0.856544 | 0.654148 |
3,362,944 | 195 | 0 | 22 | 1 | -0.847098 | -0.325242 | -1.17234 | 1.383948 |
3,362,944 | 195 | 0 | 23 | 24 | 0 | -0.023471 | -0.023471 | 0 |
3,362,944 | 195 | 0 | 23 | 32 | 0 | -0.023471 | -0.023471 | 0 |
3,362,944 | 195 | 0 | 23 | 26 | 0 | -0.023471 | -0.023471 | 0 |
3,362,944 | 195 | 0 | 25 | 28 | -1.509435 | -0.154825 | -1.66426 | 1.102571 |
3,362,944 | 195 | 0 | 25 | 29 | -1.32062 | -0.165998 | -1.486618 | 1.125697 |
3,362,944 | 195 | 0 | 28 | 31 | 0 | -0.019356 | -0.019356 | 0 |
3,362,944 | 195 | 0 | 6 | 3 | -1.239938 | -0.194882 | -1.43482 | 1.157171 |
3,362,944 | 195 | 0 | 6 | 20 | -0.961918 | -0.194882 | -1.156801 | 1.202598 |
3,362,944 | 195 | 0 | 7 | 0 | 0 | 0.085409 | 0.085409 | 0 |
3,362,944 | 195 | 0 | 7 | 25 | 0 | 0.085409 | 0.085409 | 0 |
3,362,944 | 195 | 0 | 10 | 24 | -0.922303 | -0.011944 | -0.934247 | 1.01295 |
3,362,944 | 195 | 0 | 12 | 24 | 0 | -0.020582 | -0.020582 | 0 |
3,362,944 | 195 | 0 | 15 | 31 | 0 | -0.029099 | -0.029099 | 0 |
3,362,944 | 195 | 0 | 1 | 2 | 0 | -0.103804 | -0.103804 | 0 |
3,362,944 | 195 | 0 | 1 | 3 | -0.392909 | -0.037997 | -0.430906 | 1.096706 |
3,362,944 | 195 | 0 | 1 | 4 | -0.266152 | -0.048612 | -0.314764 | 1.182647 |
3,362,944 | 195 | 0 | 1 | 5 | -0.281356 | -0.047763 | -0.329119 | 1.16976 |
3,362,944 | 195 | 0 | 2 | 6 | 0 | -0.038053 | -0.038053 | 0 |
3,362,944 | 195 | 0 | 2 | 4 | -0.16969 | -0.009632 | -0.179322 | 1.056761 |
3,362,944 | 195 | 0 | 2 | 5 | -0.115014 | -0.014211 | -0.129225 | 1.123555 |
3,362,944 | 195 | 0 | 6 | 0 | -0.237222 | -0.05811 | -0.295332 | 1.24496 |
3,362,944 | 195 | 0 | 6 | 8 | -0.199224 | -0.064474 | -0.263698 | 1.323628 |
3,362,944 | 195 | 0 | 6 | 9 | 0 | -0.081158 | -0.081158 | 0 |
3,362,944 | 195 | 0 | 6 | 4 | -0.443176 | -0.056416 | -0.499592 | 1.127299 |
3,362,944 | 195 | 0 | 10 | 31 | 0 | -0.046206 | -0.046206 | 0 |
3,362,944 | 195 | 0 | 10 | 12 | 0 | -0.046206 | -0.046206 | 0 |
3,362,944 | 195 | 0 | 10 | 13 | 0 | -0.046206 | -0.046206 | 0 |
3,362,944 | 195 | 0 | 11 | 9 | 0 | -0.018874 | -0.018874 | 0 |
3,362,944 | 195 | 0 | 11 | 13 | 0 | -0.018874 | -0.018874 | 0 |
3,362,944 | 195 | 0 | 11 | 15 | 0 | -0.012582 | -0.012582 | 0 |
3,362,944 | 195 | 0 | 12 | 13 | 0 | -0.104495 | -0.104495 | 0 |
3,362,944 | 195 | 0 | 12 | 15 | 0 | -0.052248 | -0.052248 | 0 |
3,362,944 | 195 | 0 | 12 | 16 | 0 | -0.052248 | -0.052248 | 0 |
3,362,944 | 195 | 0 | 13 | 14 | -0.629932 | -0.0308 | -0.660731 | 1.048894 |
3,362,944 | 195 | 0 | 13 | 19 | 0 | -0.136306 | -0.136306 | 0 |
3,362,944 | 195 | 0 | 13 | 16 | 0 | -0.136306 | -0.136306 | 0 |
3,362,944 | 195 | 0 | 13 | 17 | -0.551674 | -0.105506 | -0.657181 | 1.191248 |
3,362,944 | 195 | 0 | 15 | 16 | 0 | -0.066648 | -0.066648 | 0 |
3,362,944 | 195 | 0 | 15 | 40 | 0 | -0.033324 | -0.033324 | 0 |
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NeuraxonLife2.5-100K-DeepTimeSeries: Artificial Life Neuraxon Neural Network Simulation Deep Time Series Dataset (Passive 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-TimeSeriesPassiveBinary,
title={Neuraxon Game of Life 2.5 Research Dataset: Deep Time Series Exploration Passive Binnary Exploration},
author={Vivancos, David and Sanchez, Jose},
year={2026},
publisher={Hugging Face},
version={2.5.0},
url={https://huggingface.co/datasets/DavidVivancos/NeuraxonLife2.5PassiveBinary}
}
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.
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