OSJL-py/PRCpy
Simple modular python package for physical reservoir computing. Use your own experimental data.
This tool helps researchers in neuromorphic computing analyze experimental data from physical reservoir computing systems. You feed it your raw experimental data, along with parameters for preprocessing and defining your target output (like a square wave or Mackey-Glass series). It then processes this data through a custom pipeline, trains a model, runs the reservoir computing simulation, and provides results, including reservoir metrics like nonlinearity and memory capacity. It's designed for physicists and materials scientists working on novel computing hardware.
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Use this if you are conducting experiments on physical reservoir computing systems and need a streamlined way to preprocess your data, define targets, run simulations, and extract key performance metrics.
Not ideal if you are looking for a software-based reservoir computing simulator for purely theoretical or abstract dataset analysis, rather than experimental data from a physical system.
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Language
Python
License
MIT
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Last pushed
Oct 25, 2024
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