Interpreting RNN behaviour via excitable network attractors

Machine learning has become a basic tool in scientific research and for the development of technologies with significant impact on society. In fact, such methods allow to discover regularities in data and make predictions without explicit knowledge of the rules governing the system under analysis. However, a price must be paid for exploiting such a modeling flexibility: machine learning methods are usually black-box, meaning that it is difficult to fully understand what the machine is doing and how. This poses constraints on the applicability of such methods, neglecting the possibility to gather novel scientific insights from experimental data. Our research aims to open the black-box of recurrent neural networks, an important family of neural networks suitable to process sequential data. Here, we propose a novel methodology that allows to provide a mechanistic interpretation of their behaviour when used to solve computational tasks. The methodology is based on mathematical constructs called excitable network attractors, which are models represented as networks in phase space composed by stable attractors and excitable connections between them. As the behaviour of recurrent neural networks depends on training and inputs driving the autonomous system, we introduce an algorithm to extract network attractors directly from a trajectory generated by the neural network while solving tasks. Simulations conducted on a controlled benchmark highlight the relevance of the proposed methodology for interpreting the behaviour of recurrent neural networks on tasks that involve learning a finite number of stable states.