diff --git a/README.md b/README.md
index 5b9ffb0..96efa40 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,7 @@
+Code excerpt from David Rotermund, Mahbod Nouri, Alberto Garcia-Ortiz and Kaus R. Pawelzik trying to understand deep NNMF networks. 
+
+# Origin of the algorithm
+
 Refinement of the approach for deep NNMF networks shown in:
 
 ```
@@ -16,3 +20,68 @@ David Rotermund and Klaus R. Pawelzik
 https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2019.00055/full
 ```
 
+# Network structure
+
+Note: A block like 
+
+```
+  (1): Unfold(kernel_size=(5, 5), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
+  (2): Fold(output_size=torch.Size([24, 24]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (3): L1NormLayer()
+  (4): NNMF2d(75, 32, pfunctype=0, local_learning=False)
+```
+represents one(!) Con2d NNMF Layer. We just see more of the innards that for a normal Conv2d.
+
+
+```
+Sequential(
+  (0): ReLU()
+  (1): Unfold(kernel_size=(5, 5), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
+  (2): Fold(output_size=torch.Size([24, 24]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (3): L1NormLayer()
+  (4): NNMF2d(75, 32, pfunctype=0, local_learning=False)
+  (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (6): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
+  (7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (8): ReLU()
+  (9): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
+  (10): Fold(output_size=torch.Size([12, 12]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (11): L1NormLayer()
+  (12): NNMF2d(128, 32, pfunctype=0, local_learning=False)
+  (13): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (14): ReLU()
+  (15): Unfold(kernel_size=(5, 5), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
+  (16): Fold(output_size=torch.Size([8, 8]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (17): L1NormLayer()
+  (18): NNMF2d(800, 64, pfunctype=0, local_learning=False)
+  (19): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (20): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+  (21): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (22): ReLU()
+  (23): Unfold(kernel_size=(2, 2), dilation=(1, 1), padding=(0, 0), stride=(2, 2))
+  (24): Fold(output_size=torch.Size([4, 4]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (25): L1NormLayer()
+  (26): NNMF2d(256, 64, pfunctype=0, local_learning=False)
+  (27): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
+  (28): ReLU()
+  (29): Unfold(kernel_size=(4, 4), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
+  (30): Fold(output_size=torch.Size([1, 1]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (31): L1NormLayer()
+  (32): NNMF2d(1024, 96, pfunctype=0, local_learning=False)
+  (33): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1))
+  (34): ReLU()
+  (35): Unfold(kernel_size=(1, 1), dilation=(1, 1), padding=(0, 0), stride=(1, 1))
+  (36): Fold(output_size=torch.Size([1, 1]), kernel_size=(1, 1), dilation=1, padding=0, stride=1)
+  (37): L1NormLayer()
+  (38): NNMF2d(96, 10, pfunctype=0, local_learning=False)
+  (39): Conv2d(10, 10, kernel_size=(1, 1), stride=(1, 1))
+  (40): Softmax(dim=1)
+  (41): Flatten(start_dim=1, end_dim=-1)
+)
+
+Information about used parameters:
+cnn_top: 14638
+nnmf: 173344
+batchnorm2d: 576
+total number of parameter: 188558
+```