title: Detecting connectivity changes in neuronal networks.
abstract: We develop a method from semiparametric statistics (Cox, 1972) for the purpose of tracking links and connection strengths over time in a neuronal network from spike train data. We consider application of the method as implemented in Masud and Borisyuk (2011), and evaluate its use on data generated independently of the Cox model hypothesis, in particular from a spiking model of Izhikevich in four different dynamical regimes. Then, we show how the Cox method can be used to determine statistically significant changes in network connectivity over time. Our methodology is demonstrated using spike trains from multi-electrode array measurements of networks of cultured mammalian spinal cord cells.
authors: Department of Mathematical Sciences, George Mason University, Fairfax, VA 22030, USA.
authors: Tyrus
authors: T
authors: Berry
authors: Franz
authors: F
authors: Hamilton
authors: Nathalia
authors: N
authors: Peixoto
authors: Timothy
authors: T
authors: Sauer
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D002478:Cells, Cultured; D002540:Cerebral Cortex / Q000166:cytology; D004558:Electric Stimulation; D004622:Embryo, Mammalian; D051379:Mice; D008839:Microelectrodes; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000166:cytology*; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000166:cytology