[NEST Users] Re: Training SNNs with PyNEST

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 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: is there another simulator who can do this? send me an example? This would be very helpful. _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org