Neural Fields – Old Idea, New Glory¶

About¶

In 1977, Shun-ichi Amari introduced neural fields, a class of potential-based recurrent neural networks [1]. This architecture was developed as a simplistic model of the activity of neurons in a (human) brain. It's main characteristic is the lateral in-/exhibition of neurons though their accumulated potential. Due to its simplicity and expressiveness, Amari’s work was highly influential and led to several follow-up papers such as [2, 3, 4, 5] to only name a few.

Citing¶

If you use code or ideas from this repository for your projects or research, please cite and star it. It does not cost you anything, and would support me for putting in the effort of providing a clean state-of-the-art implementation to you.

@misc{Muratore_neuralfields,
  author = {Fabio Muratore},
  title = {neuralfields - A type of potential-based recurrent neural networks implemented with PyTorch},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/famura/neuralfields}}
}

Features¶

• There are two variants of the neural fields implemented in this repository: one called NeuralField that matches the model of Amari closely using 1D convolutions, as well as another one called SimpleNeuralField that replaces the convolutions and introduces custom potential dynamics function.
• Both implementations have by modern standards very few, i.e. typically less than 1000, parameters. I suggest that you start with the NeuralField class since it is more expressive. However, the SimpleNeuralField has the benefit of operating with typically less than 20 parameters, which allows you to use optimizers that otherwise might not scale.
• Both model classes are subclasses of torch.nn.Module, hence able to process batched data and run on GPUs.
• This repository is a spin-off from SimuRLacra where the neural fields have been used as the backbone for control policies. In SimuRLacra, the focus is on reinforcement learning for sim-to-real transfer. However, the goal of this repository is to make the implementation as general as possible, such that it could for example be used as generative model.

Getting Started¶

To install this package, simply run

pip install neuralfields

For further information, please have a look at the getting started guide. In the documentation, you can also find the complete reference of the source code.

[1] Shun-ichi Amari. Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics, 27(2):77–87, 1977.
[2] K. Kishimoto and Shun-ichi Amari. Existence and stability of local excitations in homogeneous neural fields. Journal of Mathematical Biology, 7(4):303–318, 1979.
[3] Wolfram Erlhagen and Gregor Schöner. Dynamic field theory of movement preparation. Psychological Review, 109(3):545–572, 2002.
[4] Shun-ichi Amari, Hyeyoung Park, and Tomoko Ozeki. Singularities affect dynamics of learning in neuromanifolds. Neural Computation, 18(5):1007–1065, 2006.
[5] T. Luksch, M. Gineger, M. Mühlig, T. Yoshiike, Adaptive Movement Sequences and Predictive Decisions based on Hierarchical Dynamical Systems. International Conference on Intelligent Robots and Systems, 2012.