Integrative Probabilistic Evolving Spiking Neural Networks Utilising Quantum Inspired Evolutionary Algorithm: A Computational Framework

Integrative evolving connectionist systems (iECOS) integrate principles from different levels of information processing in the brain, including cognitive-, neuronal-, genetic- and quantum, in their dynamic interaction over time. The paper introduces a new framework of iECOS called integrative probabilistic evolving spiking neural networks (ipSNN) that incorporate probability learning parameters. ipSNN utilize a quantum inspired evolutionary optimization algorithm to optimize the probability parameters as these algorithms belong to the class of estimation of distribution algorithms (EDA). Both spikes and input features in ipESNN are represented as quantum bits being in a superposition of two states (1 and 0) defined by a probability density function. This representation allows for the state of an entire ipESNN at any time to be represented probabilistically in a quantum bit register and probabilistically optimised until convergence using quantum gate operators and a fitness function. The proposed ipESNN is a promising framework for both engineering applications and brain data modeling as it offers faster and more efficient feature selection and model optimization in a large dimensional space in addition to revealing new knowledge that is not possible to obtain using other models. Further development of ipESNN are the neuro-genetic models – ipESNG, that are introduced too, along with open research questions.