2024-07-10 16:09:16 +02:00
|
|
|
Refinement of the approach for deep NNMF networks shown in:
|
|
|
|
|
|
|
|
```
|
|
|
|
Competitive performance and superior noise robustness of a non-negative deep convolutional spiking network
|
|
|
|
David Rotermund, Alberto Garcia-Ortiz, Kaus R. Pawelzik
|
|
|
|
https://www.biorxiv.org/content/10.1101/2023.04.22.537923v1
|
|
|
|
```
|
|
|
|
|
|
|
|
Now a normal ADAM optimiser will work.
|
|
|
|
|
|
|
|
The BP learning rule is taken from here (it was derived for a spike-based SbS system, but it works exactly the same for NNMF):
|
|
|
|
|
|
|
|
```
|
|
|
|
Back-Propagation Learning in Deep Spike-By-Spike Networks
|
|
|
|
David Rotermund and Klaus R. Pawelzik
|
|
|
|
https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00055/full
|
|
|
|
```
|
2024-07-10 15:43:31 +02:00
|
|
|
|