Spiking MoYF

This is a Spiking Neural Network library based on a below paper. However, this project does NOT completely reflect the paper (detail: Issue: #2).

Training Deep Spiking Neural Networks Using Backpropagation

Examples

$ python examples/digits.py
MNIST Loading...OK

...

In 5938
answer:
[[ 1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.]]
infer:
[[ 1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.]]
Complete!
Saving...OK

This model is saved in ./models/.

If you want to use already trained this model, use below.

$ python examples/digits.py --load sample_models/0-2.npz --no-save
MNIST Loading...OK
Loading...OK
No spike... in [False False False False False False False False False False False False
 False False False]
In 0
answer:
[[ 1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.]]
infer:
[[ 1.  1.  1.  1.  1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.]]
Complete!

You can also visualize these spikes. Use below.

$ python examples/digits.py --load sample_models/0-2.npz --no-save --draw

This visualizes this network distinguishes among 0, 1 and 2.

Requirements

• Python 3
• NumPy
• scikit-learn

License

MIT License