Our papers are the official record of our discoveries. They allow others to build on and apply our work. Each paper is the result of many months of research, so we make a special effort to make them clear, beautiful and inspirational, and publish them in leading journals.
Balancing memory from linear components with nonlinearities from memristors optimises the computational capacity of electronic reservoirs.
A simple solvable model of memristive networks suggests a correspondence between the asymptotic states of memristors and the Ising model.
Exact solutions for the dynamics of interacting memristors predict whether they relax to higher or lower resistance states given random initialisations.
Memristive networks preserve memory and have the ability to learn according to analysis of the network’s internal memory dynamics.
Exact equations of motion provide an analytical description of the evolution and relaxation properties of complex memristive circuits.
A local model of preferential attachment with short-term memory generates scale-free networks, which can be readily computed by memristors.