diff --git a/README.md b/README.md index 141aece..0169e08 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ We have been studying brain tissues of humans, mice, and fruit flies. We implemented the obtained results into artificial neural networks to design architectures that outperform conventional AIs. ## Mouse-mimetic layer -Mouse-mimetic layer is based on our study on nanometer-scale 3D structures of mouse brain tissues and also on those of human, such as [this](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287646). We implemented the mouse-mimetic convolutional layers in generative AIs and found that the resultant mouse AI excels at generating cat face and cheese photos. Python scripts used in our study are available from here.
+Mouse-mimetic layer is based on our study on nanometer-scale 3D structures of mouse brain tissues and also on those of human, such as [this](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0287646). We implemented the mouse-mimetic convolutional layers in generative AIs and found that the resultant mouse AI excels at generating cat face and cheese photos, but underperforms for human faces and birds. Python scripts used in our study are available from here.
## How to implement the mouse-mimetic layer in your network Our code runs on Tensorflow 2.16 / Keras 3.3. Mouse-mimetic versions of the fully connected layer and the 2D convolutional layers are available. Their usage is the same with the Keras layers, except for specifying the %usage of weights and its reduction method. The reduction method for the mouse layer is `2d` and its recommended window width is 0.4-0.6, which corresponds to the parameter %usage of 35-60%.