"Spiking GLOM: Bio-inspired Architecture for Next-generation Object Recognition", Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt. In review.

From the main directory run:

pipenv install

pip install spikingjelly

to install the required dependencies.

To run contrastive pre-training on CIFAR-10, execute:

python spiking_glom_new/main.py --flagfile spiking_glom_config_new/spiking_glom_contrast.cfg

After pre-training, to run supervised training on CIFAR-10, execute:

python spiking_glom_new/main.py --flagfile spiking_glom_config_new/spiking_glom_supervise.cfg

See spiking_glom_new/flags_Agglomerator_slom.py to check all the flag meanings.

To run contrastive pre-training on CIFAR-10, execute:

python hybrid_spiking_glom_new/main.py --flagfile hybrid_spiking_glom_config_new/spiking_glom_contrast.cfg

After pre-training, to run supervised training on CIFAR-10, execute:

python hybrid_spiking_glom_new/main.py --flagfile hybrid_spiking_glom_config_new/spiking_glom_supervise.cfg

For Potential-assisted Spiking GLOM:

python spiking_glom_new/main_energy.py --flagfile spiking_glom_config_new/spiking_glom_energy.cfg

For Hybrid Spiking GLOM:

python hybrid_spiking_glom_new/main.py --flagfile hybrid_spiking_glom_config_new/spiking_glom_energy.cfg

python spiking_glom_new/main.py --flagfile spiking_glom_config_new/spiking_glom_plot.cfg

We provide pre-trained models Potential-assisted Spiking GLOM and Hybrid Spiking GLOM for calculate the firing rates and specific 5-level Potential-assisted Spiking GLOM for freeze to plot.

• Agglomerator by Garau et al.
• SpikingJelly