Initial State-Dependent Implementation of Logic Gates with Memristive Neurons

This study introduces a simple Memristor Cellular Neural Network (M-CellNN) structure, a minimalist configuration with only two cells, designed to concurrently address two logic problems. The unique attribute of this system lies in its adaptability, where the nature of the implemented logic gate, be it AND, OR, and XOR, is determined exclusively by the initial states of the memristors. The memristors' state, alterable through current flow, allows for dynamic manipulation, enabling the setting of initial conditions and consequently, a change in the circuit's functionality. To optimize the parameters of this dynamic system, we employ contemporary machine learning techniques, specifically gradient descent optimization. Through a case study, we exemplify the potential of leveraging intricate circuit dynamics to expand the spectrum of problems solvable with a defined number of neurons. This work not only underscores the significance of adaptability in logical circuits but also demonstrates the efficacy of memristive elements in enhancing problem-solving capabilities.