Collective Motion and Infromation Dynamics in Systems of Vibrated Active Granular Particles

Biology‟s growing interest in the mechanical and statistical properties of living matter has led physicists to develop flocking models and fabricate artificial analogues of self-propulsion in order to gain better understanding of the complex collective behaviors of living matter. This dissertation research investigates the dynamics of self-propulsion, collective motion, and information propagation in vibrated active systems comprising active granular particles (AGP) with a head-tail structure reminiscent of biological swimmers such as flagellated bacteria and spermatozoa. A novel information-theoretic framework based on connected mutual information (CMI) is introduced to quantify the propagation of speed and polarization fluctuation information in AGP systems comprising 10 to 100 particles. Empirical results show that, when subjected to sinusoidal vibration, AGPs can effectively mimic certain complex collective behaviors of biological systems including spontaneous swarm formation and flocking, intermittent regrouping events, and coherent changes in travel direction. Rigorous nonlinear CMI analyses also reveal that: (i) the speed and polarization fluctuations are independent of each other; (ii) the polarization fluctuation CMI is more effective in discriminating between random and nonlinear dynamical behavior, i.e., compared to the speed fluctuation CMI; and (iii) for every type of active system, there exists a critical number of members that could maximize the sharing of information within the group. The information-theoretic framework introduced in this study can have potential applications to other studies involving various collective phenomena, both in the biological and the artificial realms