Spontaneous dynamical differentiation in an experimental network of single-transistor chaotic oscillators modeling a biological neuronal culture

These are experimental time series for a network of electronic chaotic oscillators which are coupled according to a structural connectivity derived from a biological neural culture. Recordings from two different physical circuit realizations, as well as the SPICE netlist, as provided. Additional recordings are available upon request (not uploaded due to size restrictions). The attached data acquisition script serves to explain the contents of the records. Board design materials are also given, but it should be kept in mind that they are only representative, and the actual component values used in the experiment are those that are reported in the publication referenced below; some patches may also need to be applied. These materials are made available to support the replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of neural dynamics, nonlinear electronic circuits, and related aspects, in compliance with the specified license terms and all applicable legal clauses. For any questions regarding these data, the corresponding author of the publication referenced below should be contacted. The following reference must be cited when using these data: Minati L, Sparacino L, Ngamsa Tegnitsap JV, Zhao M, Fang F, Mijatovic G, Antonacci Y, Valdes-Sosa PA, Ito H, Frasca M, Faes L, Spontaneous dynamical differentiation in an experimental network of single-transistor chaotic oscillators modeling a biological neuronal culture, Chaos, Solitons and Fractals 200 (2025) 117111, https://doi.org/10.1016/j.chaos.2025.117111. L.M. gratefully acknowledges the support of the ‘‘Hundred Talents’’ program of the University of Electronic Science and Technology of China, of the ‘‘Outstanding Young Talents Program (Overseas)’’ program of the National Natural Science Foundation of China, and of the talent programs of the Sichuan province and Chengdu municipality. All experimental activities were fully self-funded and conducted by L.M. during the period 2020–2022 using own independent assets located in Grigno TN, Italy; the manuscript was later finalized after joining the University of Electronic Science and Technology of China, to which no equipment was transferred. L.S. and L.F. are supported by the project ‘‘HONEST - High-Order Dynamical Networks in Computational Neuroscience and Physiology: an Information-Theoretic Framework’’, Italian Ministry of University and Research (MUR), PRIN 2022, code 2022YMHNPY, CUP: B53D23003020006, wheres Y.A. and L.F. are supported by the SiciliAn MicronanOTecH Research And Innovation CEnter ‘‘SAMOTHRACE’’ (MUR, PNRR-M4C2, ECS-00000022). M.F. acknowledges partial support of the University of Catania, Italy under the framework of the PIA.CE.RI. project entitled ‘‘CoCoS: Control of Complex Systems’’. The authors are grateful to Stefano Aldrigo of Tecno77 Srl (Brendola VI, Italy) for board layout design, to Christophe Letellier for insightful discussions on the choice of a Poincare’ section, complexity measurement, and general feedback on an earlier draft, and to Karan K.H. Manjunatha to assistance during graph illustration.本数据集为基于生物神经培养物（biological neural culture）衍生的结构连接性进行耦合的电子混沌振荡器（electronic chaotic oscillators）网络实验时序序列。现有数据包含两种不同物理电路实现的记录结果，以及SPICE网表（SPICE netlist）。受文件大小限制，未上传额外的实验记录，此类记录可根据申请提供。附带的数据采集脚本用于说明记录文件的具体内容。此外还提供了电路板设计材料，但需注意：此类材料仅为示例，实验中实际使用的元件参数以如下引用文献中报道的内容为准，部分补丁可能也需要额外适配。本数据集及配套材料的发布旨在支持对相关已发表研究结果的复现，以及神经动力学、非线性电子电路及相关领域的各类公有域学术研究，使用时需遵守指定的许可条款及所有适用法律条款。若对本数据集存在任何疑问，请联系如下引用文献的通讯作者。 使用本数据集时必须引用以下参考文献：Minati L, Sparacino L, Ngamsa Tegnitsap JV, Zhao M, Fang F, Mijatovic G, Antonacci Y, Valdes-Sosa PA, Ito H, Frasca M, Faes L. 模拟生物神经培养物的单晶体管混沌振荡器实验网络中的自发动力学分化[J]. Chaos, Solitons and Fractals, 2025, 200: 117111. https://doi.org/10.1016/j.chaos.2025.117111. L.M. 衷心感谢电子科技大学“百人计划”、国家自然科学基金委员会“优秀青年科学基金（海外）”项目，以及四川省及成都市人才计划的支持。所有实验工作均由L.M.于2020-2022年间自筹经费，使用位于意大利特雷维索省格里尼奥镇的自有设备完成；论文手稿则是在加入电子科技大学后定稿，期间未向该校转移任何实验设备。L.S.与L.F.得到意大利大学与研究部（MUR）PRIN 2022项目“HONEST——计算神经科学与生理学中的高阶动力学网络：信息论框架”（项目编号：2022YMHNPY，CUP：B53D23003020006）的资助；而Y.A.与L.F.则得到西西里微米纳米技术研究与创新中心（SAMOTHRACE，MUR，PNRR-M4C2，ECS-00000022）的支持。M.F.感谢意大利卡塔尼亚大学在“CoCoS：复杂系统控制”PIA.CE.RI.计划框架下提供的部分资助。作者们感谢Tecno77 Srl公司的Stefano Aldrigo（意大利维琴察省布伦多拉）提供电路板版图设计，感谢Christophe Letellier就庞加莱截面（Poincaré section）的选择、复杂度测量方法以及早期稿件的整体反馈提供的富有启发性的讨论，同时感谢Karan K.H. Manjunatha在图表绘制过程中提供的协助。