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Estimating Synaptic Conductances By Learning Neuron Dynamics

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.

System Requirements

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.

Neural Data and Mathematical Models

Training

Benchmarking

Citing

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}
}

License

This repository is released under the MIT license. See LICENSE for additional details.