diff --git a/docs/about/release_notes.md b/docs/about/release_notes.md
index 1f5a15691..556181ce3 100644
--- a/docs/about/release_notes.md
+++ b/docs/about/release_notes.md
@@ -1,5 +1,11 @@
 # Release notes
 
+## v3.1.3 (04/02/2026)
+
+* Add tests for mapping non-sequential networks
+* Fix networks used for tests, they didnt meet the hardware constraints
+* Update documentation with the non-sequential networks types that have a test
+
 ## v3.1.2 (12/12/2025)
 
 * Update use of visualizer in Sinabs tutorials, previous tutorials were using a deprecated visualizer version.
diff --git a/docs/getting_started/iaf_neuron_model.ipynb b/docs/getting_started/iaf_neuron_model.ipynb
index e920d55ad..992a31205 100644
--- a/docs/getting_started/iaf_neuron_model.ipynb
+++ b/docs/getting_started/iaf_neuron_model.ipynb
@@ -163,7 +163,6 @@
     "import torch\n",
     "import sinabs.layers as sl\n",
     "\n",
-    "\n",
     "# Define a neuron in 'SINABS'\n",
     "neuron = sl.IAF()\n",
     "\n",
diff --git a/docs/speck/faqs/available_network_arch.md b/docs/speck/faqs/available_network_arch.md
index f25cb78ac..7ae38a3b3 100644
--- a/docs/speck/faqs/available_network_arch.md
+++ b/docs/speck/faqs/available_network_arch.md
@@ -39,28 +39,31 @@ dynapcnn.to(devcie="your device", chip_layers_ordering=[2, 5, 7, 1])
 
 ## What network structure can I define?
 
-`Sinabs` can parse a `torch.nn.Sequential` like architecture, so it is recommended to
-use a `Sequential` like network.
+`Sinabs` can parse a `torch.nn.Sequential` like architecture, so it is recommended to use a `Sequential` like network.
 
 As of `v3.1.0`, we released a network graph extraction feature that helps users deploy their networks with more complex architectures into the devkit.
 Our `Speck` chip, in fact, supports branched architectures. With the graph extraction feature, we support a range of network structures, as shown below:
 
+A network with a merge and a split:
 
-Two independent networks:
-
-![Two independent networks](imgs/two-independent-networks.png)
+![A network with a merge and a split](imgs/network-with-merge-and-split.png)
 
 Two networks with merging outputs:
 
 ![Two networks with merging outputs](imgs/two-networks-merging-output.png)
 
-A network with a merge and a split:
+A network with residual connections:
 
-![A network with a merge and a split](imgs/network-with-merge-and-split.png)
+![A network with residual connections](imgs/network-with-residual-connection.png)
+
+A more complex network:
 
+![A more complex network](imgs/complex-network.png)
 
 Note: with the graph extracture feature it is possible to implement recurrent neural networks. However, this is not recommended or supported as it can result in deadlock on the chip.
 
+Note2: the use of two parallel network although supported by our chip was not fully considered in our sinabs implementation.
+
 ## How to make use of the graph extraction feature?
 
 For general architectures, users need to define their classes, by defining at least the `__init__` method with all the layers, as well as an appropriate `forward` method.
@@ -68,64 +71,70 @@ For general architectures, users need to define their classes, by defining at le
 Here is an example to define a network with a merge and a split:
 
 ```python
+import torch
 import torch.nn as nn
 
 from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
 from sinabs.layers import IAFSqueeze, Merge, SumPool2d
 
+
 class SNN(nn.Module):
     def __init__(self, batch_size) -> None:
         super().__init__()
-
-        self.conv1 = nn.Conv2d(2, 1, 2, 1, bias=False)
-        self.iaf1 = IAFSqueeze(
+        # -- graph node A --
+        self.conv_A = nn.Conv2d(2, 4, 2, 1, bias=False)
+        self.iaf_A = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-
-        self.conv2 = nn.Conv2d(1, 1, 2, 1, bias=False)
-        self.iaf2 = IAFSqueeze(
+        # -- graph node B --
+        self.conv_B = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf2_B = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-        self.pool2 = SumPool2d(2, 2)
-
-        self.conv3 = nn.Conv2d(1, 1, 2, 1, bias=False)
-        self.iaf3 = IAFSqueeze(
+        self.pool_B = SumPool2d(2, 2)
+        # -- graph node C --
+        self.conv_C = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_C = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-        self.pool3 = SumPool2d(2, 2)
-        self.pool3a = SumPool2d(5, 5)
-
-        self.conv4 = nn.Conv2d(1, 1, 2, 1, bias=False)
-        self.iaf4 = IAFSqueeze(
+        self.pool_C = SumPool2d(2, 2)
+        # -- graph node D --
+        self.conv_D = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_D = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-        self.pool4 = SumPool2d(3, 3)
-
-        self.flat1 = nn.Flatten()
-        self.flat2 = nn.Flatten()
-
-        self.conv5 = nn.Conv2d(1, 1, 2, 1, bias=False)
-        self.iaf5 = IAFSqueeze(
+        # -- graph node E --
+        self.conv_E = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf3_E = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-
-        self.fc2 = nn.Linear(25, 10, bias=False)
-        self.iaf2_fc = IAFSqueeze(
+        self.pool_E = SumPool2d(2, 2)
+        # -- graph node F --
+        self.conv_F = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_F = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+        # -- graph node G --
+        self.fc3 = nn.Linear(144, 10, bias=False)
+        self.iaf3_fc = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
@@ -134,43 +143,47 @@ class SNN(nn.Module):
 
         # -- merges --
         self.merge1 = Merge()
-        self.merge2 = Merge()
+
+        # -- falts --
+        self.flat_D = nn.Flatten()
+        self.flat_F = nn.Flatten()
 
     def forward(self, x):
         # conv 1 - A/0
-        con1_out = self.conv1(x)
-        iaf1_out = self.iaf1(con1_out)
+        convA_out = self.conv_A(x)
+        iaf_A_out = self.iaf_A(convA_out)
 
         # conv 2 - B/1
-        conv2_out = self.conv2(iaf1_out)
-        iaf2_out = self.iaf2(conv2_out)
-        pool2_out = self.pool2(iaf2_out)
+        conv_B_out = self.conv_B(iaf_A_out)
+        iaf_B_out = self.iaf2_B(conv_B_out)
+        pool_B_out = self.pool_B(iaf_B_out)
 
         # conv 3 - C/2
-        conv3_out = self.conv3(iaf1_out)
-        iaf3_out = self.iaf3(conv3_out)
-        pool3_out = self.pool3(iaf3_out)
-        pool3a_out = self.pool3a(iaf3_out)
-
-        # conv 4 - D/3
-        merge1_out = self.merge1(pool2_out, pool3_out)
-        conv4_out = self.conv4(merge1_out)
-        iaf4_out = self.iaf4(conv4_out)
-        pool4_out = self.pool4(iaf4_out)
-        flat1_out = self.flat1(pool4_out)
-
-        # conv 5 - E/4
-        conv5_out = self.conv5(pool3a_out)
-        iaf5_out = self.iaf5(conv5_out)
-        flat2_out = self.flat2(iaf5_out)
-
-        # fc 2 - F/5
-        merge2_out = self.merge2(flat2_out, flat1_out)
-
-        fc2_out = self.fc2(merge2_out)
-        iaf2_fc_out = self.iaf2_fc(fc2_out)
-
-        return iaf2_fc_out
+        conv_C_out = self.conv_C(pool_B_out)
+        iaf_C_out = self.iaf_C(conv_C_out)
+        pool_C_out = self.pool_C(iaf_C_out)
+
+        # conv 4 - D/4
+        conv_D_out = self.conv_D(pool_C_out)
+        iaf_D_out = self.iaf_D(conv_D_out)
+        # fc 1 - E/3
+        conv_E_out = self.conv_E(pool_B_out)
+        iaf3_E_out = self.iaf3_E(conv_E_out)
+        pool_E_out = self.pool_E(iaf3_E_out)
+
+        # fc 2 - F/6
+        conv_F_out = self.conv_F(pool_E_out)
+        iaf_F_out = self.iaf_F(conv_F_out)
+
+        # fc 2 - G/5
+        flat_D_out = self.flat_D(iaf_D_out)
+        flat_F_out = self.flat_F(iaf_F_out)
+
+        merge1_out = self.merge1(flat_D_out, flat_F_out)
+        fc3_out = self.fc3(merge1_out)
+        iaf3_fc_out = self.iaf3_fc(fc3_out)
+
+        return iaf3_fc_out
 ```
 
 ## Can I achieve a "Residual Connection" like ResNet does?
@@ -180,9 +193,9 @@ change the `samna.speck2f.configuration.CNNLayerDestination.layer` to achieve th
 familiar with the `samna-configuration`.
 You can also make use of our network graph extraction feature, to implement residual networks.
 
-## How to use "Residual Connection" manually?
+## How can I define "Residual Connection" manually?
 
-Alright! Here I will give an example of achieving the "Residual Connection" by manually modify the `samna-configuration`.
+You can also achieve "Residual Connection" by manually modify the `samna-configuration`.
 
 Let's say you want an architecture like below:
 
@@ -221,7 +234,7 @@ class ResidualBlock(nn.Module):
 
 ```
 
-Since currently Sinabs can only parse Sequential like network, we need to do some tedious work like below:
+You can write it like:
 
 ```python
 # define a Sequential first
@@ -264,8 +277,8 @@ devkit.get_model().apply_configuration(samna_cfg)
 
 ```
 
-I have to say it is not an elegant solution though, it should help you to achieve an initial Residual Block. We will
-improve this part after Sinabs has the ability for extracting model's graph.
+It is a lot of manual work but it will let you have your Residual Block.
+We recommend to use our network graph extraction feature for residual connections.
 
 ## What execution order should I be aware of when I am implementing a sequential structure?
 You should be aware with the internal layer order.
diff --git a/docs/speck/faqs/imgs/complex-network.png b/docs/speck/faqs/imgs/complex-network.png
new file mode 100644
index 000000000..18c55b619
Binary files /dev/null and b/docs/speck/faqs/imgs/complex-network.png differ
diff --git a/docs/speck/faqs/imgs/network-with-residual-connection.png b/docs/speck/faqs/imgs/network-with-residual-connection.png
new file mode 100644
index 000000000..d1456353b
Binary files /dev/null and b/docs/speck/faqs/imgs/network-with-residual-connection.png differ
diff --git a/docs/speck/faqs/imgs/two-independent-networks.png b/docs/speck/faqs/imgs/two-independent-networks.png
deleted file mode 100644
index 66483143b..000000000
Binary files a/docs/speck/faqs/imgs/two-independent-networks.png and /dev/null differ
diff --git a/docs/speck/notebooks/nmnist_quick_start.ipynb b/docs/speck/notebooks/nmnist_quick_start.ipynb
index bcf975e64..5eb9edf64 100644
--- a/docs/speck/notebooks/nmnist_quick_start.ipynb
+++ b/docs/speck/notebooks/nmnist_quick_start.ipynb
@@ -97,7 +97,6 @@
    "source": [
     "from torch import nn\n",
     "\n",
-    "\n",
     "# define a CNN model\n",
     "cnn = nn.Sequential(\n",
     "    # [2, 34, 34] -> [8, 17, 17]\n",
@@ -1181,7 +1180,6 @@
    "source": [
     "from sinabs.backend.dynapcnn.dynapcnn_visualizer import DynapcnnVisualizer\n",
     "\n",
-    "\n",
     "visualizer = DynapcnnVisualizer(\n",
     "    window_scale=(4, 8),\n",
     "    dvs_shape=(34, 34),\n",
diff --git a/docs/speck/notebooks/play_with_speck_dvs.ipynb b/docs/speck/notebooks/play_with_speck_dvs.ipynb
index aaf055559..0aa3ac613 100644
--- a/docs/speck/notebooks/play_with_speck_dvs.ipynb
+++ b/docs/speck/notebooks/play_with_speck_dvs.ipynb
@@ -103,7 +103,6 @@
     "from sinabs.backend.dynapcnn.dynapcnn_visualizer import DynapcnnVisualizer\n",
     "from sinabs.backend.dynapcnn import DynapcnnNetwork\n",
     "\n",
-    "\n",
     "# create a dummy snn for DynapcnnNetwork initialization\n",
     "snn = nn.Sequential(\n",
     "    nn.Conv2d(1, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),\n",
diff --git a/docs/tutorials/nmnist.ipynb b/docs/tutorials/nmnist.ipynb
index 555f92a5c..31d8b876a 100644
--- a/docs/tutorials/nmnist.ipynb
+++ b/docs/tutorials/nmnist.ipynb
@@ -292,7 +292,6 @@
    "source": [
     "from tqdm.notebook import tqdm\n",
     "\n",
-    "\n",
     "n_epochs = 1\n",
     "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
     "crit = nn.functional.cross_entropy\n",
diff --git a/sinabs/backend/dynapcnn/chips/dynapcnn.py b/sinabs/backend/dynapcnn/chips/dynapcnn.py
index e1da34af2..77b5987f5 100644
--- a/sinabs/backend/dynapcnn/chips/dynapcnn.py
+++ b/sinabs/backend/dynapcnn/chips/dynapcnn.py
@@ -101,7 +101,7 @@ def get_dynapcnn_layer_config_dict(
         dimensions["input_shape"]["feature_count"] = channel_count
 
         # dimensions["output_feature_count"] already done in conv2d_to_dict
-        (f, h, w) = layer.get_neuron_shape()
+        f, h, w = layer.get_neuron_shape()
         dimensions["output_shape"]["size"] = {}
         dimensions["output_shape"]["feature_count"] = f
         dimensions["output_shape"]["size"]["x"] = w
@@ -121,7 +121,7 @@ def get_dynapcnn_layer_config_dict(
         config_dict["dimensions"] = dimensions
         # Update parameters from convolution
         if layer.conv_layer.bias is not None:
-            (weights, biases) = layer.conv_layer.parameters()
+            weights, biases = layer.conv_layer.parameters()
         else:
             (weights,) = layer.conv_layer.parameters()
             biases = torch.zeros(layer.conv_layer.out_channels)
diff --git a/sinabs/backend/dynapcnn/dynapcnn_visualizer.py b/sinabs/backend/dynapcnn/dynapcnn_visualizer.py
index f19210bc9..cb6abe220 100644
--- a/sinabs/backend/dynapcnn/dynapcnn_visualizer.py
+++ b/sinabs/backend/dynapcnn/dynapcnn_visualizer.py
@@ -455,7 +455,7 @@ def connect(
 
         # Streamer graph
         # Dvs node
-        (_, dvs_member_filter, _, streamer_node) = self.streamer_graph.sequential(
+        _, dvs_member_filter, _, streamer_node = self.streamer_graph.sequential(
             [
                 dynapcnn_network.samna_device.get_model_source_node(),
                 samna.graph.JitMemberSelect(),
@@ -526,7 +526,7 @@ def connect(
         ## Readout node
         if "r" in self.gui_type:
             if self.readout_node == "JitMajorityReadout":
-                (_, majority_readout_node, _) = self.streamer_graph.sequential(
+                _, majority_readout_node, _ = self.streamer_graph.sequential(
                     [
                         spike_collection_node,
                         samna.graph.JitMajorityReadout(samna.ui.Event),
@@ -544,7 +544,7 @@ def connect(
                     self.readout_default_threshold_high
                 )
             else:
-                (_, majority_readout_node, _) = self.streamer_graph.sequential(
+                _, majority_readout_node, _ = self.streamer_graph.sequential(
                     [
                         spike_collection_node,
                         self.readout_node,
diff --git a/sinabs/backend/dynapcnn/nir_graph_extractor.py b/sinabs/backend/dynapcnn/nir_graph_extractor.py
index 30f5ef5bf..c14c41b81 100644
--- a/sinabs/backend/dynapcnn/nir_graph_extractor.py
+++ b/sinabs/backend/dynapcnn/nir_graph_extractor.py
@@ -504,7 +504,7 @@ def _add_dvs_node(self, dvs_input_shape: Tuple[int, int, int]) -> DVSLayer:
             A handler to the newly added `DVSLayer` instance.
         """
 
-        (features, height, width) = dvs_input_shape
+        features, height, width = dvs_input_shape
         if features > 2:
             raise ValueError(
                 f"A DVSLayer istance can have a max feature dimension of 2 but {features} was given."
@@ -578,7 +578,7 @@ def _validate_dvs_setup(self, dvs_input_shape: Tuple[int, int, int]) -> None:
                 f"A DVSLayer node exists and there are {nb_entries} entry nodes in the graph: the DVSLayer should be the only entry node."
             )
 
-        (features, _, _) = dvs_input_shape
+        features, _, _ = dvs_input_shape
 
         if features > 2:
             raise ValueError(
@@ -752,7 +752,7 @@ def _get_nodes_io_shapes(
                 # different input sources to a core to have the same output shapes.
                 if any(inp.shape != inputs[0].shape for inp in inputs):
                     raise ValueError(
-                        f"Layer `sinabs.layers.merge.Merge` (node {node}) requires input tensors with the same shape"
+                        f"Layer `sinabs.layers.merge.Merge` (node {node}) requires input tensors with the same shape."
                     )
 
                 # forward input through the node.
@@ -828,6 +828,7 @@ def _find_source_of_input_to(self, node: int) -> int:
         if len(sources) == 0:
             return -1
         if len(sources) > 1:
+            # return -1 #TODO: why throw a runtime error when the documentation explicitly say -1 in case of more than one input node?
             raise RuntimeError(f"Node {node} has more than 1 input")
         return sources.pop()
 
diff --git a/sinabs/layers/pool2d.py b/sinabs/layers/pool2d.py
index c9aeb37e1..3958086e0 100644
--- a/sinabs/layers/pool2d.py
+++ b/sinabs/layers/pool2d.py
@@ -80,7 +80,7 @@ def get_output_shape(self, input_shape: Tuple) -> Tuple:
         Returns:
             (channelsOut, height_out, width_out)
         """
-        (channels, height, width) = input_shape
+        channels, height, width = input_shape
 
         height_out = conv_output_size(
             height + sum(self.padding[2:]), self.pool_size[0], self.strides[0]
diff --git a/tests/test_dynapcnn/hw_utils.py b/tests/hw_utils.py
similarity index 100%
rename from tests/test_dynapcnn/hw_utils.py
rename to tests/hw_utils.py
diff --git a/tests/test_dynapcnn/__init__.py b/tests/test_dynapcnn/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/test_dynapcnn/test_device_movement.py b/tests/test_dynapcnn/test_device_movement.py
index 34d84c5ec..f8864c24b 100644
--- a/tests/test_dynapcnn/test_device_movement.py
+++ b/tests/test_dynapcnn/test_device_movement.py
@@ -3,7 +3,7 @@
 
 from sinabs.backend.dynapcnn import DynapcnnNetwork
 from sinabs.from_torch import from_model
-from hw_utils import find_open_devices
+from tests.hw_utils import find_open_devices
 
 ann = nn.Sequential(
     nn.Conv2d(1, 20, 5, 1, bias=False),
diff --git a/tests/test_dynapcnn/test_discover_device.py b/tests/test_dynapcnn/test_discover_device.py
index caae4eef1..cecd55b3a 100644
--- a/tests/test_dynapcnn/test_discover_device.py
+++ b/tests/test_dynapcnn/test_discover_device.py
@@ -2,7 +2,7 @@
 import samna
 
 from sinabs.backend.dynapcnn import io
-from hw_utils import find_open_devices
+from tests.hw_utils import find_open_devices
 
 
 def test_is_device_type():
diff --git a/tests/test_dynapcnn/test_large_net.py b/tests/test_dynapcnn/test_large_net.py
index 4c7c9432a..27937b354 100644
--- a/tests/test_dynapcnn/test_large_net.py
+++ b/tests/test_dynapcnn/test_large_net.py
@@ -14,7 +14,7 @@
 from sinabs.backend.dynapcnn.dynapcnn_network import DynapcnnNetwork
 from sinabs.from_torch import from_model
 from sinabs.layers import NeuromorphicReLU
-from hw_utils import find_open_devices
+from tests.hw_utils import find_open_devices
 
 
 class DynapCnnNetA(nn.Module):
diff --git a/tests/test_dynapcnn/test_neuron_leak.py b/tests/test_dynapcnn/test_neuron_leak.py
index 7bc0c63b5..769e366c5 100644
--- a/tests/test_dynapcnn/test_neuron_leak.py
+++ b/tests/test_dynapcnn/test_neuron_leak.py
@@ -3,12 +3,7 @@
 import pytest
 import samna
 import torch
-from hw_utils import (
-    find_open_devices,
-    get_ones_network,
-    is_any_samna_device_connected,
-    is_device_connected,
-)
+from tests.hw_utils import find_open_devices, is_any_samna_device_connected
 from torch import nn
 
 from sinabs.backend.dynapcnn import DynapcnnNetwork
diff --git a/tests/test_dynapcnn/test_single_neuron_hardware.py b/tests/test_dynapcnn/test_single_neuron_hardware.py
index d7d730222..728c21ac4 100644
--- a/tests/test_dynapcnn/test_single_neuron_hardware.py
+++ b/tests/test_dynapcnn/test_single_neuron_hardware.py
@@ -1,9 +1,8 @@
 import pytest
-from hw_utils import (
+from tests.hw_utils import (
     find_open_devices,
     get_ones_network,
     is_any_samna_device_connected,
-    is_device_connected,
     reset_all_connected_boards,
 )
 
diff --git a/tests/test_dynapcnn/test_visualizer.py b/tests/test_dynapcnn/test_visualizer.py
index bcc1c6219..dbe1fe397 100644
--- a/tests/test_dynapcnn/test_visualizer.py
+++ b/tests/test_dynapcnn/test_visualizer.py
@@ -3,9 +3,8 @@
 
 import pytest
 import samna
-from custom_jit_filters import majority_readout_filter as custom_filter
-from hw_utils import find_open_devices, is_any_samna_device_connected
-
+from .custom_jit_filters import majority_readout_filter as custom_filter
+from tests.hw_utils import find_open_devices
 from sinabs.backend.dynapcnn.dynapcnn_visualizer import DynapcnnVisualizer
 
 
@@ -89,7 +88,7 @@ def test_jit_compilation():
             streamer_graph = samna.graph.EventFilterGraph()
             # Streamer graph
             # Dvs node
-            (_, dvs_member_filter, _, streamer_node) = streamer_graph.sequential(
+            _, dvs_member_filter, _, streamer_node = streamer_graph.sequential(
                 [
                     # samna.graph.JitSource(samna.speck2e.event.OutputEvent),
                     dynapcnn_network.samna_device.get_model_source_node(),
diff --git a/tests/test_dynapcnnnetwork/conftest_dynapcnnnetwork.py b/tests/test_dynapcnnnetwork/conftest_dynapcnnnetwork.py
index df12d90e9..f5005e968 100644
--- a/tests/test_dynapcnnnetwork/conftest_dynapcnnnetwork.py
+++ b/tests/test_dynapcnnnetwork/conftest_dynapcnnnetwork.py
@@ -14,6 +14,11 @@
 from .model_dummy_4 import expected_output as expected_output_4
 from .model_dummy_4 import input_shape as input_shape_4
 from .model_dummy_4 import snn as snn_4
+from .model_dummy_5 import batch_size as batch_size_5
+from .model_dummy_5 import expected_output as expected_output_5
+from .model_dummy_5 import input_shape as input_shape_5
+from .model_dummy_5 import snn as snn_5
+
 from .model_dummy_seq import (
     expected_seq_1,
     expected_seq_2,
@@ -30,3 +35,13 @@
     (seq_1, input_shape_seq, 1, expected_seq_1),
     (seq_2, input_shape_seq, 1, expected_seq_2),
 ]
+
+args_DynapcnnNetworkMappingTest = [
+    (snn_1, input_shape_1, batch_size_1),
+    (snn_2, input_shape_2, batch_size_2),
+    (snn_3, input_shape_3, batch_size_3),
+    (snn_4, input_shape_4, batch_size_4),
+    (seq_1, input_shape_seq, 1),
+    (seq_2, input_shape_seq, 1),
+    # (snn_5, input_shape_5, batch_size_5), #TODO: forward method needs implementation
+]
diff --git a/tests/test_dynapcnnnetwork/model_dummy_1.py b/tests/test_dynapcnnnetwork/model_dummy_1.py
index c24aabe15..6b7ef3edc 100644
--- a/tests/test_dynapcnnnetwork/model_dummy_1.py
+++ b/tests/test_dynapcnnnetwork/model_dummy_1.py
@@ -13,49 +13,49 @@ class SNN(nn.Module):
     def __init__(self, batch_size) -> None:
         super().__init__()
 
-        self.conv1 = nn.Conv2d(2, 10, 2, 1, bias=False)  # node 0
+        self.conv1 = nn.Conv2d(2, 10, 2, 1, bias=False)
         self.iaf1 = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
-        )  # node 1
-        self.pool1 = nn.AvgPool2d(3, 3)  # node 2
-        self.pool1a = nn.AvgPool2d(4, 4)  # node 3
+        )
+        self.pool1 = nn.AvgPool2d(2, 2)
+        self.pool1a = nn.AvgPool2d(2, 2)
 
-        self.conv2 = nn.Conv2d(10, 10, 4, 1, bias=False)  # node 4
+        self.conv2 = nn.Conv2d(10, 10, 1, 1, bias=False)
         self.iaf2 = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
-        )  # node 6
+        )
 
-        self.conv3 = nn.Conv2d(10, 1, 2, 1, bias=False)  # node 8
+        self.conv3 = nn.Conv2d(10, 1, 2, 1, bias=False)
         self.iaf3 = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
-        )  # node 9
+        )
 
         self.flat = nn.Flatten()
 
-        self.fc1 = nn.Linear(49, 500, bias=False)  # node 10
+        self.fc1 = nn.Linear(225, 200, bias=False)
         self.iaf4 = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
-        )  # node 11
+        )
 
-        self.fc2 = nn.Linear(500, 10, bias=False)  # node 12
+        self.fc2 = nn.Linear(200, 10, bias=False)
         self.iaf5 = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
-        )  # node 13
+        )
 
         self.adder = Merge()
 
@@ -67,14 +67,13 @@ def forward(self, x):
 
         conv2_out = self.conv2(pool1_out)
         iaf2_out = self.iaf2(conv2_out)
-
         conv3_out = self.conv3(self.adder(pool1a_out, iaf2_out))
         iaf3_out = self.iaf3(conv3_out)
-
         flat_out = self.flat(iaf3_out)
 
         fc1_out = self.fc1(flat_out)
         iaf4_out = self.iaf4(fc1_out)
+
         fc2_out = self.fc2(iaf4_out)
         iaf5_out = self.iaf5(fc2_out)
 
diff --git a/tests/test_dynapcnnnetwork/model_dummy_2.py b/tests/test_dynapcnnnetwork/model_dummy_2.py
index cff57e8d4..2fc3af5ce 100644
--- a/tests/test_dynapcnnnetwork/model_dummy_2.py
+++ b/tests/test_dynapcnnnetwork/model_dummy_2.py
@@ -81,39 +81,38 @@ def __init__(self, batch_size) -> None:
 
     def forward(self, x):
         # conv 1 - A/0
-        convA_out = self.conv_A(x)  # node 0
-        iaf_A_out = self.iaf_A(convA_out)  # node 1
+        convA_out = self.conv_A(x)
+        iaf_A_out = self.iaf_A(convA_out)
 
         # conv 2 - B/1
-        conv_B_out = self.conv_B(iaf_A_out)  # node 2
-        iaf_B_out = self.iaf2_B(conv_B_out)  # node 3
-        pool_B_out = self.pool_B(iaf_B_out)  # node 4
+        conv_B_out = self.conv_B(iaf_A_out)
+        iaf_B_out = self.iaf2_B(conv_B_out)
+        pool_B_out = self.pool_B(iaf_B_out)
 
         # conv 3 - C/2
-        conv_C_out = self.conv_C(pool_B_out)  # node 5
-        iaf_C_out = self.iaf_C(conv_C_out)  # node 7
-        pool_C_out = self.pool_C(iaf_C_out)  # node 8
+        conv_C_out = self.conv_C(pool_B_out)
+        iaf_C_out = self.iaf_C(conv_C_out)
+        pool_C_out = self.pool_C(iaf_C_out)
 
         # conv 4 - D/4
-        conv_D_out = self.conv_D(pool_C_out)  # node 9
-        iaf_D_out = self.iaf_D(conv_D_out)  # node 10
-
+        conv_D_out = self.conv_D(pool_C_out)
+        iaf_D_out = self.iaf_D(conv_D_out)
         # fc 1 - E/3
-        conv_E_out = self.conv_E(pool_B_out)  # node 6
-        iaf3_E_out = self.iaf3_E(conv_E_out)  # node 12
-        pool_E_out = self.pool_E(iaf3_E_out)  # node 13
+        conv_E_out = self.conv_E(pool_B_out)
+        iaf3_E_out = self.iaf3_E(conv_E_out)
+        pool_E_out = self.pool_E(iaf3_E_out)
 
         # fc 2 - F/6
-        conv_F_out = self.conv_F(pool_E_out)  # node 14
-        iaf_F_out = self.iaf_F(conv_F_out)  # node 15
+        conv_F_out = self.conv_F(pool_E_out)
+        iaf_F_out = self.iaf_F(conv_F_out)
 
         # fc 2 - G/5
-        flat_D_out = self.flat_D(iaf_D_out)  # node 11
-        flat_F_out = self.flat_F(iaf_F_out)  # node 16
+        flat_D_out = self.flat_D(iaf_D_out)
+        flat_F_out = self.flat_F(iaf_F_out)
 
-        merge1_out = self.merge1(flat_D_out, flat_F_out)  # node 19
-        fc3_out = self.fc3(merge1_out)  # node 17
-        iaf3_fc_out = self.iaf3_fc(fc3_out)  # node 18
+        merge1_out = self.merge1(flat_D_out, flat_F_out)
+        fc3_out = self.fc3(merge1_out)
+        iaf3_fc_out = self.iaf3_fc(fc3_out)
 
         return iaf3_fc_out
 
diff --git a/tests/test_dynapcnnnetwork/model_dummy_4.py b/tests/test_dynapcnnnetwork/model_dummy_4.py
index 92fd4417f..9092cf7d0 100644
--- a/tests/test_dynapcnnnetwork/model_dummy_4.py
+++ b/tests/test_dynapcnnnetwork/model_dummy_4.py
@@ -38,7 +38,7 @@ def __init__(self, batch_size) -> None:
             surrogate_grad_fn=PeriodicExponential(),
         )
         self.pool3 = SumPool2d(2, 2)
-        self.pool3a = SumPool2d(5, 5)
+        self.pool3a = SumPool2d(2, 2)
 
         self.conv4 = nn.Conv2d(1, 1, 2, 1, bias=False)
         self.iaf4 = IAFSqueeze(
@@ -47,7 +47,7 @@ def __init__(self, batch_size) -> None:
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
-        self.pool4 = SumPool2d(3, 3)
+        self.pool4 = SumPool2d(2, 2)
 
         self.flat1 = nn.Flatten()
         self.flat2 = nn.Flatten()
@@ -59,8 +59,9 @@ def __init__(self, batch_size) -> None:
             spike_threshold=1.0,
             surrogate_grad_fn=PeriodicExponential(),
         )
+        self.pool5 = SumPool2d(2, 2)
 
-        self.fc2 = nn.Linear(25, 10, bias=False)
+        self.fc2 = nn.Linear(49, 10, bias=False)
         self.iaf2_fc = IAFSqueeze(
             batch_size=batch_size,
             min_v_mem=-1.0,
@@ -98,7 +99,8 @@ def forward(self, x):
         # conv 5 - E/4
         conv5_out = self.conv5(pool3a_out)
         iaf5_out = self.iaf5(conv5_out)
-        flat2_out = self.flat2(iaf5_out)
+        pool5_out = self.pool5(iaf5_out)
+        flat2_out = self.flat2(pool5_out)
 
         # fc 2 - F/5
         merge2_out = self.merge2(flat2_out, flat1_out)
diff --git a/tests/test_dynapcnnnetwork/model_dummy_5.py b/tests/test_dynapcnnnetwork/model_dummy_5.py
new file mode 100644
index 000000000..82354d2b6
--- /dev/null
+++ b/tests/test_dynapcnnnetwork/model_dummy_5.py
@@ -0,0 +1,199 @@
+"""
+implementing "two independent networks" in https://github.com/synsense/sinabs/issues/181
+TODO: This is not working yet
+"""
+
+import torch
+import torch.nn as nn
+
+from sinabs.activation.surrogate_gradient_fn import PeriodicExponential
+from sinabs.layers import IAFSqueeze, Merge, SumPool2d
+
+
+class SNN(nn.Module):
+    def __init__(self, batch_size) -> None:
+        super().__init__()
+
+        self.conv_A = nn.Conv2d(2, 4, 2, 1, bias=False)
+
+        self.iaf_A = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+
+        self.conv_B = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_B = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+        self.pool_B = SumPool2d(2, 2)
+
+        self.conv_C = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_C = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+        self.pool_C = SumPool2d(2, 2)
+
+        self.conv_D = nn.Conv2d(2, 4, 2, 1, bias=False)
+        self.iaf_D = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+
+        self.conv_E = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_E = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+        self.pool_E = SumPool2d(2, 2)
+
+        self.conv_F = nn.Conv2d(4, 4, 2, 1, bias=False)
+        self.iaf_F = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+        self.pool_F = SumPool2d(2, 2)
+
+        self.flat_brach1 = nn.Flatten()
+        self.flat_brach2 = nn.Flatten()
+
+        self.fc1 = nn.Linear(196, 100, bias=False)
+        self.iaf1_fc = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+
+        self.fc2 = nn.Linear(100, 100, bias=False)
+        self.iaf2_fc = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+
+        self.fc3 = nn.Linear(100, 10, bias=False)
+        self.iaf3_fc = IAFSqueeze(
+            batch_size=batch_size,
+            min_v_mem=-1.0,
+            spike_threshold=1.0,
+            surrogate_grad_fn=PeriodicExponential(),
+        )
+
+    def forward(self, x):
+        # conv 1 - A
+        conv_A_out = self.conv_A(x)
+        iaf_A_out = self.iaf_A(conv_A_out)
+        # conv 2 - B
+        conv_B_out = self.conv_B(iaf_A_out)
+        iaf_B_out = self.iaf_B(conv_B_out)
+        pool_B_out = self.pool_B(iaf_B_out)
+        # conv 3 - C
+        conv_C_out = self.conv_C(pool_B_out)
+        iaf_C_out = self.iaf_C(conv_C_out)
+        pool_C_out = self.pool_C(iaf_C_out)
+
+        # ---
+
+        # conv 4 - D
+        conv_D_out = self.conv_D(x)
+        iaf_D_out = self.iaf_D(conv_D_out)
+        # conv 5 - E
+        conv_E_out = self.conv_E(iaf_D_out)
+        iaf_E_out = self.iaf_E(conv_E_out)
+        pool_E_out = self.pool_E(iaf_E_out)
+        # conv 6 - F
+        conv_F_out = self.conv_F(pool_E_out)
+        iaf_F_out = self.iaf_F(conv_F_out)
+        pool_F_out = self.pool_F(iaf_F_out)
+
+        # ---
+
+        flat_brach1_out = self.flat_brach1(pool_C_out)
+        flat_brach2_out = self.flat_brach2(pool_F_out)
+
+        # FC 7 - G
+        fc1_out = self.fc1(flat_brach1_out)
+        iaf1_fc_out = self.iaf1_fc(fc1_out)
+
+        # FC 8 - H
+        fc2_out1 = self.fc2(iaf1_fc_out)
+        iaf2_fc_out1 = self.iaf2_fc(fc2_out1)
+
+        fc3_out1 = self.fc3(iaf2_fc_out1)
+        iaf3_fc3_out1 = self.iaf3_fc(fc3_out1)
+
+        # FC 8 - H
+        fc1_out2 = self.fc1(flat_brach2_out)
+        iaf2_fc_out2 = self.iaf1_fc(fc1_out2)
+
+        fc2_out2 = self.fc2(iaf2_fc_out2)
+        iaf2_fc3_out2 = self.iaf2_fc(fc2_out2)
+
+        fc3_out_2 = self.fc3(iaf2_fc3_out2)
+        iaf3_fc3_out2 = self.iaf3_fc(fc3_out_2)
+
+        return iaf3_fc3_out1, iaf3_fc3_out2
+
+
+channels = 2
+height = 34
+width = 34
+batch_size = 2
+input_shape = (channels, height, width)
+
+snn = SNN(batch_size)
+
+expected_output = {
+    "dcnnl_edges": {
+        (0, 2),
+        (2, 4),
+        (4, 6),
+        (6, 7),
+        (1, 3),
+        (3, 5),
+        (5, 6),
+        (7, 8),
+        ("input", 0),
+        ("input", 1),
+    },
+    "node_source_map": {
+        0: {"input"},
+        2: {0},
+        4: {2},
+        6: {4, 5},
+        1: {"input"},
+        3: {1},
+        5: {3},
+        7: {6},
+        8: {7},
+    },
+    "destination_map": {
+        0: {2},
+        2: {4},
+        4: {6},
+        6: {7},
+        1: {3},
+        3: {5},
+        5: {6},
+        7: {8},
+        8: {-1},
+    },
+    "sorted_nodes": [0, 1, 2, 3, 4, 5, 6, 7, 8],
+    "output_shape": torch.Size([2, 10, 1, 1]),
+    "entry_points": {0, 1},
+}
diff --git a/tests/test_dynapcnnnetwork/test_dynapcnnnetwork.py b/tests/test_dynapcnnnetwork/test_dynapcnnnetwork.py
index de694ace6..3706a98f4 100644
--- a/tests/test_dynapcnnnetwork/test_dynapcnnnetwork.py
+++ b/tests/test_dynapcnnnetwork/test_dynapcnnnetwork.py
@@ -2,8 +2,12 @@
 import torch
 
 from sinabs.backend.dynapcnn.dynapcnn_network import DynapcnnNetwork
+from tests.hw_utils import find_open_devices, supported_device_types_for_testing
 
-from .conftest_dynapcnnnetwork import args_DynapcnnNetworkTest
+from .conftest_dynapcnnnetwork import (
+    args_DynapcnnNetworkTest,
+    args_DynapcnnNetworkMappingTest,
+)
 
 
 @pytest.mark.parametrize(
@@ -58,3 +62,22 @@ def test_DynapcnnNetwork(snn, input_shape, batch_size, expected_output):
     assert (
         expected_output["output_shape"] == output.shape
     ), "wrong model output tensor shape."
+
+
+@pytest.mark.parametrize(
+    "snn, input_shape, batch_size", args_DynapcnnNetworkMappingTest
+)
+def test_DynapcnnNetwork_movement(snn, input_shape, batch_size):
+    """Tests if the network can be mapped to the hardware"""
+
+    dcnnnet = DynapcnnNetwork(snn, input_shape, batch_size)
+
+    devices = find_open_devices()
+
+    if len(devices) == 0:
+        pytest.skip("A connected Speck device is required to run this test")
+
+    # deploy to devkit
+    for device in devices:
+        if device in supported_device_types_for_testing:
+            dcnnnet.to(device=device, monitor_layers=["dvs", -1])