Dataset of "Neuron-like self-sustained oscillatory devices for unconventional computing applications"
[Data processing methods:] Raw data and analytical results for the FitzHugh–Nagumo model were generated using Python 3.x and Wolfram Mathematica. Simulation files for the Wilson–Bower oscillator were created and analyzed with LTspice XVII. Phase-space simulations for the mixed oscillator system were...
| Autores: | , , |
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| Formato: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/408050 |
| Acesso em linha: | http://hdl.handle.net/10261/408050 |
| Access Level: | acceso abierto |
| Palavra-chave: | Self-sustained oscillator Spiking neuron Neuromorphic computing Memristor Mixed ionic–electronic conductor Impedance spectroscopy |
| Resumo: | [Data processing methods:] Raw data and analytical results for the FitzHugh–Nagumo model were generated using Python 3.x and Wolfram Mathematica. Simulation files for the Wilson–Bower oscillator were created and analyzed with LTspice XVII. Phase-space simulations for the mixed oscillator system were performed using MATLAB. All figures were processed and formatted using OriginLab and Inkscape for graphical consistency. |
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