Neural Logic Circuits: An evolutionary neural architecture that can learn and generalize

Abstract

We introduce Neural Logic Circuits (NLC), an evolutionary, weightless, and learnable neural architecture loosely inspired by the neuroplasticity of the brain. This new paradigm achieves learning by evolution of its architecture through reorganization of augmenting synaptic connections and generation of artificial neurons functioning as logic gates. These neural units mimic biological nerve cells stimulated by binary input signals and emit excitatory or inhibitory pulses, thus executing the “all-or-none” character of their natural counterparts. Unlike Artificial Neural Networks (ANN), our model achieves generalization ability without intensive weight training and dedicates computational resources solely to building network architecture with optimal connectivity. We evaluated our model on well-known binary classification datasets using advanced performance metrics and compared results with modern and competitive machine learning algorithms. Extensive experimental data reveal remarkable superiority of our initial model, called NLCv1, on all test instances, achieving outstanding results for implementation of this new paradigm.