15 Jun
2020
15 Jun
'20
5:18 p.m.
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(a)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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