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Dynamics of learning and computations in neuronal circuits

Third workshop on advanced methods in theoretical neuroscience, June 27-29, 2018, Göttingen (Germany)

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Jun 27, 2018 09:00 AM to
Jun 29, 2018 06:00 PM



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Understanding information processing in the brain requires powerful mathematical techniques capable of capturing the nonlinear and noisy dynamics observed across different temporal and spatial scales. At the level of local circuits, a mesoscopic computational theory for neuronal circuits typically entails two essential dynamical processes: (i) the dynamics of learning and synaptic organisation processes that follow the statistics of stimuli and tasks; and (ii) the dynamics of neuronal activity-patterns dynamics in response to a particular stimulus and internal states of the network. While these two critical processes typically operate on different timescales and have been studied separately, they are unarguably related. A common assertion is that simple and local plasticity rules may lead to complicated representations. Nonetheless, understanding the limits imposed by the computation phase, such as capacity or dynamic stability, can guide the study of relevant local rules in the learning phase.

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    •    Rainer J. Engelken (Columbia University, USA)
    •    Farzad Farkhooi (Technische Universität Berlin, Bernstein Center for Computational
         Neuroscience BCCN Berlin, Germany)
    •    Jonathan Kadmon (Stanford University, USA)
    •    Fred Wolf (Max Planck Institute for Dynamics and Self-Organization, Bernstein
         Center for Computational Neuroscience BCCN Göttingen, Germany)


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