Neurons are sophisticated IO machines which allow extraditing their relevant dynamics through various approaches. Since the adoption of now wide-spread machine learning methods, there has been a great interest in drwaing parallels between biological and artificial units of computation. Here we consider 4 different single neuron models (Moris-Lecar 2D, FitzHugh-Nagumo 2D, FitzHugh-Nagumo-Rinzel 3D, Wang 4D) and utilize 3 different neural architectures (Wavenet, Transformer, CNN) in portraying these models dynamics.
The bulk of the training and testing in this work is carried out on a NVIDIA 4070 GPU using pytorch library.
ODE dynamics of neuronal models are investigated using xpp-auto.
If you find this work useful, please consider citing it.
@article{forgione2023from,
author={Forgione, Marco and Pura, Filippo and Piga, Dario},
journal={IEEE Control Systems Letters},
title={From System Models to Class Models:
An In-Context Learning Paradigm},
year={2023},
volume={7},
number={},
pages={3513-3518},
doi={10.1109/LCSYS.2023.3335036}
}
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