neural networks as a general-purpose computational framework
This project uses networks of neuron-like computational units to build a framework of computation. Specifically, it implements characteristics traditionally found in neural networks including synaptic diversity, temporal delays, and voltage spikes. It builds on the ideas proposed in the paper STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation.
To run a sample network, you can run the module.
python -m neuralkernel
The networks currently implemented are:
- Inverting Memory
- Non-Inverting Memory
- Full Subtractor
For more information on each of these networks, please check out the
Running the tests
To run the unit tests, you can run the following.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size neuralkernel-0.0.8-py3-none-any.whl (8.8 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
Hashes for neuralkernel-0.0.8-py3-none-any.whl