RNN-based Model for Self-adaptive Systems - The Emergence of Epilepsy in the Human Brain

The human brain is the self-adaptive system par excellence. We claim that a hierarchical model for self-adaptive system can be built on two levels, the upper structural level S and the lower behavioral level B. The higher order structure naturally emerges from interactions of the system with its environment and it acts as coordinator of local interactions among simple reactive elements. The lower level regards the topology of the network whose elements self-organize to perform the behavior of the system. The adaptivity feature follows the self-organizing principle that supports the entanglement of lower level elements and the higher order structure. The challenging idea in this position paper is to represent the two-level model as a second order Long Short-Term Memory Recurrent Neural Network, a bio-inspired class of artificial neural networks, very powerful for dealing with the dynamics of complex systems and for studying the emergence of brain activities. It is our aim to experiment the model over real Electrocorticographical data (EcoG) for detecting the emergence of long-term neurological disorders such as epileptic seizures.