General Information
Basic features of RectiPy
- Frontend:
implement models via the PyRates frontend
choose between YAML templates or Python classes to define your RNN
choose between various pre-implemented neuron models or implement your custom neuron model
add synaptic dynamics and/or delayed coupling
implement spiking or rate-based neuron models
full control over the RNN parameters that can be trained: Choose between synaptic weights, membrane time constants of neurons, …
run pre-implemented training and testing workflows via a single function call, OR use any
rectipy.Network
as a single unit/layer within your own, custom torch codetrack any state variable of your model with any temporal resolution during training/testing/simulation procedures
- Backend:
make full use of the PyTorch backend
full support of autograd for your parameter optimization
use any loss function and optimization algorithm available in torch
deploy your model on different hardware
Reference
If you use RectiPY, please cite the most recent release:
Contact
If you have questions, problems or suggestions regarding RectiPy, please contact Richard Gast.
Contribute
RectiPy is an open-source project that everyone is welcome to contribute to. Check out our GitHub repository for all the source code, open issues etc. and send us a pull request, if you would like to contribute something to our software.
Useful links
RectiPy makes use of two essential Python tools:
Each of these two Python tools comes with an extensive documentation that is complementary to the content covered on this documentation website.