Real-time representations of whole brain dynamics: towards computational models for “hybrid” systems Bradly Alicea Department of Telecommunication, Information Studies, and Media and Cognitive Science Program; Michigan State University Keywords: Attention, Cognitive Dynamics, Brain Networks, Complexity Theory ABSTRACT In this paper, models from complexity theory is used to characterize the dynamics of attentional networks during continuous behavior and brain activity. The argument is made that brain networks controlling attentional resources are robust to inherent levels of distraction. This is governed by a dichotomous model related to capacity. The maintenance of stable automatic activity over time is due to a sliding threshold model of activity based on metastable dynamics at each node. According to the model presented here, disruptions called phase transitions with characteristic topological and statistical signatures affect the entire network at stochastically-determined intervals. These intervals occur relatively infrequently, but have a large-scale impact on the system. These networks can be conceptualized in two ways. The first is through considering functional connectivity due to synaptic activity. The second is through considering effective connectivity, which is due to anatomical connections between structures. This model is considered in terms of both dynamic function and information about attentional disruption can be used to model potential strategies for augmenting cognition in operational settings and even predicting onsets of attentional blindness. INTRODUCTION Attentional networks serve as a bridge between the brain, cognition, and complexity. One reason is that attention serves as a gateway for examining real-time processing of informational aggregations encountered by the senses and the brain. Virtually all visual 1