• RUB
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  • Dynamics of extinction learning in behavior and neural activity
Dynamics of extinction learning in behavior and neural activity
Collaborator: Julian Packheiser, Roland Pusch, Harald Lachnit, Metin Üngör, Onur Güntürkün
Funding:

SFB 1280



The ability
to change a previously acquired behaviour as a result of altered reinforcement contingencies is essential for a successful adaptation to a changing environment. This process is called “extinction learning” and unfolds dynamically over time - just as any other form of learning. Nevertheless, learning is typically quantified by comparing post- to pre-learning blocks, thus missing the learning process itself. In addition, the data of several individuals are often lumped together to compute an average, thus occluding any inter-subject variability. By contrast, we study how learning develops in a trial-by-trial manner in individual subjects. To this end we analyze both behavioral and neural data from rodent, avian and human subjects, which we obtain from collaborating labs. In a second line of research, we use computational approaches, such as associative models, reinforcement learning, and artificial neural networks, to understand the mechanisms underlying learning and to account for the variability of the learning dynamics, both across time and across subjects. Recently, reinforcement learning has been combined with deep neural networks to achieve superior performance. However, the representations learned by these networks are seldomly investigated. We study whether the representations and their dynamical changes during learning correlate with that those of recorded neurons.


Publications

    2022

  • The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study
    Batsikadze, G., Diekmann, N., Ernst, T. M., Klein, M., Maderwald, S., Deuschl, C., et al.
    NeuroImage, 253, 119080
  • 2021

  • Emergence of complex dynamics of choice due to repeated exposures to extinction learning
    Donoso, J. R., Packheiser, J., Pusch, R., Lederer, Z., Walther, T., Uengoer, M., et al.
    Animal Cognition, 24(6), 1279–1297
  • Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal
    Packheiser, J., Donoso, J. R., Cheng, S., Güntürkün, O., & Pusch, R.
    Progress in Neurobiology, 197, 101901
  • 2007

  • Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics
    Cheng, S., & Sabes, P. N.
    Journal of Neurophysiology, 97(4), 3057–3069
  • 2006

  • Modeling Sensorimotor Learning with Linear Dynamical Systems
    Cheng, S., & Sabes, P. N.
    Neural Computation, 18(4), 760–793

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.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210