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snn-iir

PyTorch implementation of IJCAI 2020 paper Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network [arXiv] [IJCAI 2020]

Prerequisites

Install all the required Python packages:

pip install -r requirements.txt

Train Model

Run Python script to train the corresponding model:

python *.py --train

Prepare Trained Weights for Testing

To Use Your Own Trained Weights

Move them to the appropriate path.

To Use ZIP Weights

  • Download the ZIP weights by initializing and updating the snn-iir-checkpoints git submodule; 
    git submodule init
git submodule update
  • Unzip ZIP files to get trained weights;
  • Move to appropriate path;

Test Model

  • Modify test_checkpoint_path in .yaml config file;
  • Run Python script to test the corresponding model with assigned weights: python *.py --test

Models

Details of the models for the following 3 tasks.

Associative Memory

experiment network states filter dataset encoding length
associative_memory MLP zero dual exp iir Pattern Dataset original 300

Vision Tasks

experiment network states filter dataset encoding length
snn_mlp_1 MLP zero dual exp iir MNIST copy along time dimension 25
snn_mlp_1_non_zero MLP preserved dual exp iir MNIST copy along time dimension 25
snn_mlp_1_poisson_input MLP zero dual exp iir MNIST rate-based poisson 25
snn_mlp_2 MLP zero first order low pass MNIST copy along time dimension 25
snn_mlp_2_poisson_input MLP zero first order low pass MNIST rate-based poisson 25
snn_conv_1_mnist CNN zero dual exp iir MNIST copy along time dimension 25
snn_conv_1_mnist_poisson_input CNN zero dual exp iir MNIST rate-based poisson 25
snn_conv_1_nmnist CNN zero dual exp iir N-MNIST accumulate within time window(OR) 30
snn_conv_1_gesture CNN zero dual exp iir DVS128 Gesture Dataset accumulate within time window(OR) 50
snn_conv_1_gesture_30 CNN zero dual exp iir DVS128 Gesture Dataset accumulate within time window(OR) 30
snn_conv_1_gesture_max CNN zero dual exp iir DVS128 Gesture Dataset accumulate within time window(SUM)/frame(MAX) 30

Times Series Classification

Not implemented.

Results

The results of the following 3 tasks.

Associative Memory

experiment train dev test best epoch paper
associative_memory 0.0031(93) 0.00369(92) 0.0042(92) 92 -

Vision Tasks

experiment train dev test best epoch paper
snn_mlp_1 99.252(72) 98.58(72) 98.94(72) 72 -
snn_mlp_1_non_zero 99.116(93) 98.488(93) 98.858(93) 93 -
snn_mlp_1_poisson_input 99.208(98) 98.628(98) 98.928(98) 98 -
snn_mlp_2 99.3(72) 98.66(72) 98.96(72) 72 -
snn_mlp_2_poisson_input 99.284(96) 98.748(96) 98.978(96) 96 -
snn_conv_1_mnist 99.84(99) 99.47(99) 99.59(99) 99 -
snn_conv_1_mnist_poisson_input 99.822(93) 99.479(93) 99.519(93) 93 99.46
snn_conv_1_nmnist 99.998(51) 98.708(89) 98.558(89) 89 99.39
snn_conv_1_gesture 95.474(46) 85.156(46) 66.319(46) 46 96.09
snn_conv_1_gesture_30 96.094(59) 85.938(59) 68.75(59) 59 96.09
snn_conv_1_gesture_max 97.845(68) 75.781(68) 70.486(68) 68 96.09

Times Series Classification

Not implemented.

Author

Zhongyu Chen