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Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

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Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

DOI

This is the repository for our preprint titled Reservoir-computing based associative memory and itinerancy for complex dynamical attractors. We use two different approaches - index-based reservoir computer (RC) and index-free RC - to have multifunctional recurrent neural networks with complex dynamical long-term memory states.

Training Data and RC Weights

The training data (time series of target memory states) and trained weights of reservoir computers (RC) are shared in this OSF repository) with DOI 10.17605/OSF.IO/YXM2V. You may generate the data yourself using the codes we shared here in this GitHub repository).

Codes for Generating the Training Data

They are shared under the Data folder.

Codes for Training and Testing the Reservoir Computers (RCs)

They are shared under the Code-and-Results folder.

Contact

If you have any questions or would like to discuss this work further, please do not hesitate to contact me!

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Reservoir-computing based associative memory and itinerancy for complex dynamical attractors

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