Kevin Passino

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  Mathematics, Technology, and Mental Illness
  Kevin Passino
  Mental illnesses such as depression and bipolar disorder are highly prevalent and disabling.  A mathematical/computational model for mood disorders (e.g., depression and bipolar), and one therapy method, are briefly overviewed.  Then, key components of mood dynamics are analyzed: (i) “mood traps” (equilibria and stability), (ii) "mood-congruent attention" (stability and boundedness), and (iii) "mindfulness practice" as therapeutic attention re-balancing (stability, distraction rejection).  Finally, an overview of how these can be used for the development of technology for treatment, and psychoeducation, via bio-signal-driven (e.g., via EEG) adaptive music and virtual reality (i.e. feedback control).
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  Cohesive Swarm Behavior With Information Flow Constraints
  Kevin Passino
  Bacteria, bees, and birds often work together in groups to find food. A group of mobile wheeled robots can be designed to coordinate their activities to achieve a goal. Networked cooperative autonomous air vehicles are being developed for commercial and military applications. In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli. In this talk, we characterize cohesiveness of discrete-time multiagent systems as a boundedness or stability property of the agents' position trajectories and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive group behaviors even in the presence of (i) an interagent "sensing topology'' that constrains information flow, where by "information flow,'' we mean the sensing of positions and velocities of agents, (ii) a random but bounded delay and "noise'' in sensing other agents' positions and velocities, and (iii) noise in sensing a resource profile that represents an environmental stimulus and quantifies the goal of the multiagent system. Simulations are used to illustrate the ideas for multiagent systems and to make connections to synchronization of coupled oscillators.
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  Swarm Cognition in Honey Bees
  Kevin Passino

  We synthesize findings from neuroscience, psychology, and behavioral biology to show that some key features of cognition in the neuron-based brains of vertebrates are also present in the insect-based swarm of honey bees. We present our ideas in the context of the cognitive task of nest-site selection by honey bee swarms. After reviewing the mechanisms of distributed evidence gathering and processing that are the basis of decision-making in bee swarms, we point out numerous similarities in the functional organization of vertebrate brains and honey bee swarms. These include the existence of interconnected subunits, parallel processing of information, a spatially distributed memory, layered processing of information, lateral inhibition, and mechanisms of focusing attention on critical stimuli. We also review the performance of simulated swarms in standard psychological tests of decision making: tests of discrimination ability and assessments of distractor effects.