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Dynamics of collective behaviour in large scale self-organizing systems

Most animals usually do not live as solitary creatures. Instead they organize into social groups of sometimes large numbers, then often called a herd or a swarm. In the process of doing so each individual (“EGO”) will experience all others (“ALTER”) as being part of its own environment. A general principle for improved survival is predictive learning, which is learning to predict the near-future development of EGO’s environment. Animals/agents that have such a learning mechanism available will necessarily also try to learn predicting the behaviour of all ALTERs, creating a system where the agents will not only mutually influence each others behaviour but also each others learning.

Computational Neuroscience

The goal of this project group is three-fold:
  • Developing swarms of adaptive agents by evolutionary development of basic neuronal controllers including neuronal mechanisms for predictive learning.
  •  Investigation of the collective behaviour and learning of (large) swarms of such agents (and doing this towards the limit case of a “learning gas”).
  •  Investigation of the dynamics of complex neuronal controllers, which can produce state transitions leading into chaotic domains. Addressing the issue of how to learn controlling the chaos, by ways of synaptic plasticity.

Computational Neuroscience

swarmsClusteringPic15robotsLearningOA_ico  swarmingGasMovieSnap
 Self-organized clustering of 150 robots . Each individual can sense solely the direction of its closest neighbor. (divx codec required)
 Obstacle avoidance behavior acquisition with the ICO learning rule by 15 interacting robots.
Clustering in a "learning" gas with 4096 particels. 

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