@@ -16,7 +16,7 @@ Create a model consisting of a population of Izhikevich neurons with heterogeneo
1616 comp_neuro_101/1_neurons.ipynb
1717
1818.. image :: https://colab.research.google.com/assets/colab-badge.svg
19- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/comp_neuro_101/1_neurons.ipynb
19+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/comp_neuro_101/1_neurons.ipynb
2020
2121Synapses
2222--------
@@ -28,7 +28,7 @@ Create a simple balanced random network with two, sparsely connected populations
2828 comp_neuro_101/2_synapses.ipynb
2929
3030.. image :: https://colab.research.google.com/assets/colab-badge.svg
31- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/comp_neuro_101/2_synapses.ipynb
31+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/comp_neuro_101/2_synapses.ipynb
3232
3333MNIST inference
3434===============
@@ -44,7 +44,7 @@ Create a simple three layer network of integrate-and-fire neurons, densely conne
4444 mnist_inference/tutorial_1.ipynb
4545
4646.. image :: https://colab.research.google.com/assets/colab-badge.svg
47- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mnist_inference/tutorial_1.ipynb
47+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mnist_inference/tutorial_1.ipynb
4848
4949Classifying entire test set
5050---------------------------
@@ -56,7 +56,7 @@ Present entire MNIST test set to previous model and calculate accuracy.
5656 mnist_inference/tutorial_2.ipynb
5757
5858.. image :: https://colab.research.google.com/assets/colab-badge.svg
59- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mnist_inference/tutorial_2.ipynb
59+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mnist_inference/tutorial_2.ipynb
6060
6161
6262Improve classification performance
@@ -69,7 +69,7 @@ Use parallel batching and custom updates to improve inference performance by ove
6969 mnist_inference/tutorial_3.ipynb
7070
7171.. image :: https://colab.research.google.com/assets/colab-badge.svg
72- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mnist_inference/tutorial_3.ipynb
72+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mnist_inference/tutorial_3.ipynb
7373
7474Insect-inspired MNIST classification
7575====================================
@@ -85,7 +85,7 @@ Create the first layer of *Projection Neurons* which convert input images into a
8585 mushroom_body/1_first_layer.ipynb
8686
8787.. image :: https://colab.research.google.com/assets/colab-badge.svg
88- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mushroom_body/1_first_layer.ipynb
88+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mushroom_body/1_first_layer.ipynb
8989
9090Kenyon Cells
9191------------
@@ -97,7 +97,7 @@ Add a second, randomly-connected layer of *Kenyon Cells* to the model.
9797 mushroom_body/2_second_layer.ipynb
9898
9999.. image :: https://colab.research.google.com/assets/colab-badge.svg
100- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mushroom_body/2_second_layer.ipynb
100+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mushroom_body/2_second_layer.ipynb
101101
102102Kenyon Cell gain control
103103------------------------
@@ -109,7 +109,7 @@ Add recurrent inhibition circuit, inspired by <i>Giant GABAergic Neuron</i> in l
109109 mushroom_body/3_second_layer_gain_control.ipynb
110110
111111.. image :: https://colab.research.google.com/assets/colab-badge.svg
112- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mushroom_body/3_second_layer_gain_control.ipynb
112+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mushroom_body/3_second_layer_gain_control.ipynb
113113
114114Mushroom Body Output Neurons
115115----------------------------
@@ -121,7 +121,7 @@ Add *Mushroom Body Output Neurons* with STDP learning and train model on MNIST t
121121 mushroom_body/4_third_layer.ipynb
122122
123123.. image :: https://colab.research.google.com/assets/colab-badge.svg
124- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mushroom_body/4_third_layer.ipynb
124+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mushroom_body/4_third_layer.ipynb
125125
126126Testing
127127-------
@@ -133,4 +133,4 @@ Create a simplified copy of the model without learning, load in the trained weig
133133 mushroom_body/5_testing.ipynb
134134
135135.. image :: https://colab.research.google.com/assets/colab-badge.svg
136- :target: https://colab.research.google.com/github/genn-team/genn/blob/genn_5 /docs/tutorials/mushroom_body/5_testing.ipynb
136+ :target: https://colab.research.google.com/github/genn-team/genn/blob/master /docs/tutorials/mushroom_body/5_testing.ipynb
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