diff --git a/FrontNeurorobot/README.md b/FrontNeurorobot/README.md index 1be9680..e8dcd9c 100644 --- a/FrontNeurorobot/README.md +++ b/FrontNeurorobot/README.md @@ -1,7 +1,7 @@ # Schizophrenia-mimicking layer The original schizophrenia-mimicking layer is based on our [study on nanometer-scale 3D structure of neuronal network in schizophrenia cases](https://www.nature.com/articles/s41398-019-0427-4). We translated the findings into newly designed layers that mimic connection constraints in schizophrenia.
-Test calculations using the connection-constraint layer indicated that 80% of weights can be eliminated without any changes in training procedures or network configuration. Very interestingly the connection-constraint layer completely suppresses overfitting and outperforms fully connected layer. Here is a typical example obtained using [this python code](https://github.com/mizutanilab/biomimetic-nn/FrontNeurorobot/CIFAR10_CNNSchizo200910.py) with slight changes of num_epoch=200, idlist=\[0.5, 0.0\] and num_repeat=10. Over 60% of kernel weights of convolution layers can be zeroed with the same method without any accuracy loss. This study was published in Front Neurorobot.

+Test calculations using the connection-constraint layer indicated that 80% of weights can be eliminated without any changes in training procedures or network configuration. Very interestingly the connection-constraint layer completely suppresses overfitting and outperforms fully connected layer. Here is a typical example obtained using [this python code](https://github.com/mizutanilab/biomimetic-nn/blob/master/FrontNeurorobot/CIFAR10_CNNSchizo200910.py) with slight changes of num_epoch=200, idlist=\[0.5, 0.0\] and num_repeat=10. Over 60% of kernel weights of convolution layers can be zeroed with the same method without any accuracy loss. This study was published in Front Neurorobot.

![training example](CIFAR_CNN_ConcurrTraj200913.png) ## How to implement the schizophrenia-mimicking layer in your network