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.