title: Analysis and application of neuronal network controllability and observability.
abstract: Controllability and observability analyses are important prerequisite for designing suitable neural control strategy, which can help lower the efforts required to control and observe the system dynamics. First, 3-neuron motifs including the excitatory motif, the inhibitory motif, and the mixed motif are constructed to investigate the effects of single neuron and synaptic dynamics on network controllability (observability). Simulation results demonstrate that for networks with the same topological structure, the controllability (observability) of the node always changes if the properties of neurons and synaptic coupling strengths vary. Besides, the inhibitory networks are more controllable (observable) than the excitatory networks when the coupling strengths are the same. Then, the numerically determined controllability results of 3-neuron excitatory motifs are generalized to the desynchronization control of the modular motif network. The control energy and neuronal synchrony measure indexes are used to quantify the controllability of each node in the modular network. The best driver node obtained in this way is the same as the deduced one from motif analysis.
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Su
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Bin
authors.identifier: 0000000280948656
authors.initials: B
authors.lastname: Deng
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Haitao
authors.initials: H
authors.lastname: Yu
authors.affiliation: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors.forename: Chen
authors.identifier: 0000000316353479
authors.initials: C
authors.lastname: Liu
mesh_terms: D003198:Computer Simulation; D004734:Energy Metabolism; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology; D013997:Time Factors