Recursively generate large-scale Nengo models using NumPy semantics.
pip install git+https://github.com/nengo-labs/nengo-gyrusAn example of computing the square of a two-dimensional vector with Gyrus:
import gyrus
import matplotlib.pyplot as plt
import numpy as np
u = gyrus.stimuli([np.cos, np.sin])
x = (u ** 2).filter(0.01)
y = np.asarray(x.run(np.pi)) # shape: (fold, time, size_out)
plt.figure()
plt.plot(y.squeeze(axis=-1).T)
plt.xlabel("Time-step")
plt.show()
This code is automagically converted to Nengo and implemented via two spiking LIF ensembles and a lowpass synapse.
Gyrus supports many common NumPy 'ufuncs', array functions, and numeric Python operators. Thus, code can be written in a functional style using N-D arrays and then realized as a Nengo neural network. This enables algorithms to be written in NumPy and then compiled onto Nengo's supported backends (e.g., GPUs, microcontrollers, neuromorphic hardware, and other neural network accelerators).
Check out and render the Jupyter notebooks located in docs/examples.
The gyrus_overview notebook is currently the best starting point to learn the Gyrus API and see a variety of examples.
Tested against nengo>=3.0.0 and requires numpy>=1.17.
This project is currently pre-alpha. Pull requests are welcome, as are breaking (i.e., reverse-incompatible) changes.
If something doesn't work quite as you thought it should, or if you have ideas for improvements, please feel free to open up a GitHub issue or post on the Nengo Forum.