I am currently using the snntoolbox to test SNN conversion accuracy under various configurations.
I was able to get useful information and really appreciate for providing this nice tool.
However, at some point, I found it is quite difficult to achieve error rates reported in the paper (Conversion of analog to spiking neural networks using sparse temporal coding).
For TTFS, I have tried out various configurations (duartions, dt, etc.) on Lenet-5, but I was unable to achieve the error rate of 2% (as reported in the paper).
The error rate is far worse than the error rate reported in the paper.
So, I was wondering if I'm making a mistake at some point (maybe the configuration was wrong).
If you don't mind, can I get the well-fit configuration files for temporal_mean_rate and TTFS coding scheme?
Hello,
I am currently using the snntoolbox to test SNN conversion accuracy under various configurations. I was able to get useful information and really appreciate for providing this nice tool.
However, at some point, I found it is quite difficult to achieve error rates reported in the paper (Conversion of analog to spiking neural networks using sparse temporal coding). For TTFS, I have tried out various configurations (duartions, dt, etc.) on Lenet-5, but I was unable to achieve the error rate of 2% (as reported in the paper). The error rate is far worse than the error rate reported in the paper. So, I was wondering if I'm making a mistake at some point (maybe the configuration was wrong). If you don't mind, can I get the well-fit configuration files for temporal_mean_rate and TTFS coding scheme?
Thank you!