The Use of Hebbian Cell Assemblies for Nonlinear Computation
Tetzlaff, Christian ; Dasgupta, Sakyasingha ; Kulvicius, Tomas ; Wörgötter, Florentin
Citable Link (URL):http://resolver.sub.uni-goettingen.de/purl?gs-1/12075
Journal Article (Published version)
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.
Sponsored by EU
