Simulating signal flow in simple ENP-polymer networks

The Autonomous Computing Materials (ACM) project is a collaborative effort aimed at investigating brain-like materials for their applications in computing and data storage. Networks of nanoparticles at the nodes connected either by polymers or single-stranded DNA offer the possibility of such function. We will report on our progress in demonstrating that such engineered-nanoparticle polymer (ENP) networks can retain states and thereby preserve programmable memory. To approach the mathematical equations behind using ENP networks for information storage and processing, we used the Competitive Threshold Linear Network (CTLN) model. We show the relationships between different network parameters, such as connectivity, connection strengths, and initial conditions on the persistence of a sequential attractor in a 4-node, 1-sink network, as well as 2 connected networks model. We consider different models of heating (input) and their impact on the signal flow patterns and limit cycle persistence in the networks.