Improving the Transparency of Deep Neural Networks using Artificial Epigenetic Molecules

George Lacey, Annika Schoene, Nina Dethlefs, Alexander Turner


Artificial gene regulatory networks (AGRNs) are connectionist architectures inspired by biological gene regulation capable of solving tasks within complex dynamical systems. The implementation of an operational layer inspired by epigenetic mechanisms has been shown to improve the performance of AGRNs, and improve their transparency by providing a degree of explainability. In this paper, we apply artificial epigenetic layers (AELs) to two trained deep neural networks (DNNs) in order to gain an understanding of their internal workings, by determining which parts of the network are required at a particular point in time, and which nodes are not used at all. The AEL consists of artificial epigenetic molecules (AEMs) that dynamically interact with nodes within the DNNs to allow for the selective deactivation of parts of the network.


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