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Adaptation Controls Synchrony and Cluster States of Coupled Threshold-Model Neurons
Citation key Ladenbauer2013a
Author Ladenbauer, J. and Lehnert, J. and Rankoohi, H. and Dahms, T. and Schöll, E. and Obermayer, K.
Pages 042713
Year 2013
DOI 10.1103/PhysRevE.88.042713
Journal Physical Review E
Volume 88
Number 4
Abstract We analyze zero-lag and cluster synchrony of delay-coupled nonsmooth dynamical systems by extending the master stability approach, and apply this to networks of adaptive threshold-model neurons. For a homogeneous population of excitatory and inhibitory neurons we find (i) that subthreshold adaptation stabilizes or destabilizes synchrony depending on whether the recurrent synaptic excitatory or inhibitory couplings dominate, and (ii) that synchrony is always unstable for networks with balanced recurrent synaptic inputs. If couplings are not too strong, synchronization properties are similar for very different coupling topologies, i.e., random connections or spatial networks with localized connectivity. We generalize our approach for two subpopulations of neurons with nonidentical local dynamics, including bursting, for which activity-based adaptation controls the stability of cluster states, independent of a specific coupling topology.
Bibtex Type of Publication Selected:main selected:adaptation selected:publications
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