From ‘follow the leader’ to autonomous swarming: physical reservoir computing in two dimensions

Abstract

Percolating networks of nanoparticles (PNNs) are self-assembled nanoscale systems that possess brain-like characteristics that are useful for information processing, particularly within a reservoir computing (RC) framework. Previous work has successfully demonstrated one-dimensional RC tasks, such as chaotic time-series prediction and nonlinear transformation. We focus here on the challenge of two-dimensional (2D) tasks and introduce novel `follow the leader' and 'swarming' tasks. In the first task a `follower' is required to accurately track a `leader' in two dimensions. The task is performed successfully for a range of trajectories and parameters, for both position-based tracking and velocity-based tracking incorporating inertia. In both cases, the task is successful even for trajectories unseen in training. We then successfully demonstrate a 2D implementation of swarming behavior. Each agent is represented by a PNN which is trained to react to the behavior of the other members of the swarm, such that the future trajectory of all agents is generated autonomously. 
As well as demonstrating that the computational capabilities of PNNs can be extended into two dimensions, this work presents a first step in the emulation of complex emergent biological behaviors such as swarming, and opens a new route to the solution of complex optimization problems.