Dr. David Kappel

Group(s): Neural Control and Robotics ,
Neural Computation
Email:
david.kappel@phys.uni-goettingen.de
Phone: +49 551/ 39 10763
Room: E.01.104

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    Year
    Title
    Journal / Proceedings / Book
    Kappel, D. and Legenstein, R. and Habenschuss, S. and Hsieh, M. and Maass, W. (2018).
    A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning. eNeuro, ENEURO--0301, 5, 2. DOI: 10.1523/ENEURO.0301-17.2018.
    BibTeX:
    @article{kappellegensteinhabenschuss2018,
      author = {Kappel, D. and Legenstein, R. and Habenschuss, S. and Hsieh, M. and Maass, W.},
      title = {A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning},
      pages = {ENEURO--0301},
      journal = {eNeuro},
      year = {2018},
      volume= {5},
      number = {2},
      publisher = {Society for Neuroscience},
      url = {http://www.eneuro.org/content/5/2/ENEURO.0301-17.2018},
      doi = {10.1523/ENEURO.0301-17.2018},
      abstract = {Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.}}
    Abstract: Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.
    Review:

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