Dear Liangwei,

Have a look at:

https://hal.archives-ouvertes.fr/tel-02429539/document

https://www.mdpi.com/1424-8220/21/9/3240/htm

https://arxiv.org/ftp/arxiv/papers/1709/1709.00583.pdf

Regards,

IH

Dear Liangwei Yang

When the samples in the training dataset add up to zero, neural networks frequently learn quicker. This is accomplished by removing the mean value from each input variable, a process known as centering. Convergence occurs more quickly when the average of each input variable across the training set is close to zero.

When the membrane potential hits the threshold, the neuron fires and sends a signal to neighboring neurons, which raise or decrease their potentials in response to the signal. A spiking neuron model is a neuron model that fires at the moment of threshold crossing.

I also recommend that you read the following articles:

1. Preprint Training Deep Spiking Neural Networks

2. Preprint Training Spiking Neural Networks Using Lessons From Deep Learning

3. https://www.sciencedirect.com/science/article/abs/pii/S0893608019301595

4. https://www.nature.com/articles/s41467-020-17236-y

5. https://www.frontiersin.org/articles/10.3389/fnins.2016.00508/full

Kind Regards

Qamar Ul Islam

I found these links useful, please have a look at them:

https://www.frontiersin.org/articles/10.3389/fnins.2018.00774/full

https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.432.9700&rep=rep1&type=pdf