Dear Sonja,
nest is a spiking neural network simulator and not a machine learning library.
My current knowledge on SNN is that there is no established learning algorithm as there are best-practices for ANNs.
Here is a brief overview over some methods:
There ist STDP for correlation learning, reward based STDP is a reinforcement learning algorithm still being researched. Another option is to train a ANN and then convert it to a SNN.
SNN don’t have derivative of the activation function, therefore backprop is not transferable easily to SNN. There are methods like BPTT and e-prop to make backprop work. There might be more methods in the area of backprop adaptions. I am not an expert on this.
SNNs can also be used for reservoir computing which is yet another thing (https://gitlab.com/aiCTX/rockpool).
I am not sure which learning algorithm norse uses, they mention Policy gradient.
Kind regards,
Benedikt S. Vogler
> Am 15.06.2020 um 14:24 schrieb s.kraemer96@gmx.net:
>
> Dear all,
> I´m writing a master thesis on spiking neural networks and how transparent they are. For that I need to implement a SNN network and train it. So I started with Brian but that is much to complex and I don´t need something special. So I decided to use PyNest. I did all the tutorials but I´m missing a tutorial how to train the network. I don´t know how to put in a dataset to train the model. I haven´t found anything to this topic. So my questions are:
> 1. Can PyNest train set up a SNN and train it trough data and if not is there another simulator who can do this?
> 2. How do I do it? Is there anything I missed to read or can someone send me an example? This would be very helpful.
> Thanks for your help.
>
> Best,
> Sonja
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