This repository is the official implementation of BPTC+NOSO: Backpropagation of errors based on temporal code with neurons that only spike once at the most. BPTC is backpropagation of errors based on temporal code, which is mathematically rigorous given that no approximations of any gradient evaluations are used. When combined with neurons that spike once at the most (NOSOs), BPTC+NOSO highlights the following advantages of learning efficiency: (i) computational complexity for learning is independent of the input encoding length, and (ii) only few NOSOs are active during learning and inference periods, leading to large reduction in computational complexity.
To install requirements:
pip install -r requirements.txt
N-MNIST dataset loader was adapted from PySNN and modified.
To train a folded SNN (FCN) using BPTC on MNIST or N-MNIST, run this command:
cd SNN_folded
python main_folded.py --task <mnist or nmist> --mode train
To train unfolded SNNs (FCN and CNN) using BPTC on MNIST and N-MNIST, run this command:
cd SNN_unfolded
python main_unfolded.py --task <mnist or nmist> --network <fcn or cnn> --mode train
To evaluate a folded SNN (FCN) on MNIST or N-MNIST, run this command:
cd SNN_folded
python main_folded.py --task <mnist or nmist> --mode eval
To evaluate unfolded SNNs (FCN and CNN) on MNIST or N-MNIST, run this command:
cd SNN_unfolded
python main_unfolded.py --task <mnist or nmist> --network <fcn or cnn> --mode eval
Our model achieves the following performance on: MNIST dataset
| Method | Network | Accuracy (%) | Average # spikes per neuron and sample (learning) |
|---|---|---|---|
| BPTC+NOSO | 784-400-10 | 98.09% | 0.340 |
| BPTC+NOSO | 28x28-12C5-2MP-64C5-2MP-10 | 99.01% | 0.209 |
N-MNIST dataset
| Method | Network | Accuracy (%) | Average # spikes per neuron and sample (learning) |
|---|---|---|---|
| BPTC+NOSO | 34x34x2-800-10 | 97.22% | 0.234 |
| BPTC+NOSO | 34x34x2-12C5-2MP-64C5-2MP-10 | 98.67% | 0.214 |