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  • Emergent behavior and neural representations in spatial learning
Emergent behavior and neural representations in spatial learning
Collaborator: Edison Pignaton de Freitas, Paulo Rogerio de Almeida Ribeiro
Funding: SFB 1280

Animals must forage for food and water, find mates, avoid predators and return to their resting location in order to survive. Spatial navigation and learning are thus vital to the success of many species, and a variety of navigation behaviors and strategies have been observed. Place cells, grid cells, and other diverse cell types discovered in the hippocampus and adjoining areas have been thought to be neural representations that support spatial navigation and learning. However, the mechanisms that lead to the emergence of the multitude of cell types involved in navigation as well as the wide variety of observed navigation strategies are still unclear. We study spatial navigation using deep reinforcement learning to understand how experimentally observed behaviors may emerge in an artificial agent in a virtual environment. To this end, simple standard navigation tasks, such as the Morris Water Maze, as well as more complex paradigms such as extinction learning are used. Once the spatial behavior is learned, we can study the spatial representations that emerged in the network and that allow the artificial agent to navigate. These representations can then be compared to neural codes for space in the hippocampus.


Publications

    2018

  • A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems
    Menezes, M. C., de Freitas, E. P., Cheng, S., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Autonomous Exploration Guided by Optimisation Metaheuristic
    Santos, R. G., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., & de Oliveira, A. C. M.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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