| _version_ | 1866902003908608000 |
|---|---|
| author | Betteti, Simone |
| author_facet | Betteti, Simone |
| contents | <p>Repository containing the code to reproduce the experiments in the paper "Input-Driven Dynamics for Robust Memory<br>Retrieval in Hopfield Networks". The code is commented to help the reader comprehend how the experiments unravels.</p> <p> The files are</p> <ul> <li>MainHNN_SHB.py Initializes the network, creates the input and calls the necessary classes to integrate the trajectories of the system</li> <li>HNN_Gen.py Class for the generation of the IDP Hopfield model (or the classic Hopfield model)</li> <li>Eul_May.py Class containing all the necessary function to implement the Euler-Maruyama method for the integration of SDE</li> <li>HNPlot.py Contains the functions necessary for the plotting of the trajectories</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14873595 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | IDP Hopfield model - Input-Driven dynamics for robust memory retrieval Betteti, Simone Computational neuroscience Dynamical systems <p>Repository containing the code to reproduce the experiments in the paper "Input-Driven Dynamics for Robust Memory<br>Retrieval in Hopfield Networks". The code is commented to help the reader comprehend how the experiments unravels.</p> <p> The files are</p> <ul> <li>MainHNN_SHB.py Initializes the network, creates the input and calls the necessary classes to integrate the trajectories of the system</li> <li>HNN_Gen.py Class for the generation of the IDP Hopfield model (or the classic Hopfield model)</li> <li>Eul_May.py Class containing all the necessary function to implement the Euler-Maruyama method for the integration of SDE</li> <li>HNPlot.py Contains the functions necessary for the plotting of the trajectories</li> </ul> |
| title | IDP Hopfield model - Input-Driven dynamics for robust memory retrieval |
| topic | Computational neuroscience Dynamical systems |
| url | https://doi.org/10.5281/zenodo.14873595 |