title: The Use of Hebbian Cell Assemblies for Nonlinear Computation.
abstract: When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a network with multiple, simultaneously active, and computationally powerful cell assemblies is created. How such ordered structures are formed while preserving a rich diversity of neural dynamics needed for computation is still unknown. Here we show that the combination of synaptic plasticity with the slower process of synaptic scaling achieves (i) the formation of cell assemblies and (ii) enhances the diversity of neural dynamics facilitating the learning of complex calculations. Due to synaptic scaling the dynamics of different cell assemblies do not interfere with each other. As a consequence, this type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and - for execution - must cooperate with each other without interference. This mechanism, thus, permits the self-organization of computationally powerful sub-structures in dynamic networks for behavior control.
authors.affiliation: 1] Institute for Physics - Biophysics, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [2] Bernstein Center for Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [3].
authors.forename: Christian
authors.initials: C
authors.lastname: Tetzlaff
authors.affiliation: 1] Institute for Physics - Biophysics, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [2] Bernstein Center for Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [3].
authors.forename: Sakyasingha
authors.initials: S
authors.lastname: Dasgupta
authors.affiliation: 1] Institute for Physics - Biophysics, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [2] Bernstein Center for Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [3].
authors.forename: Tomas
authors.initials: T
authors.lastname: Kulvicius
authors.affiliation: 1] Institute for Physics - Biophysics, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany [2] Bernstein Center for Computational Neuroscience, Georg-August-University, Friedrich-Hund Platz 1, 37077, Göttingen, Germany.
authors.forename: Florentin
authors.initials: F
authors.lastname: Wörgötter
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*