Detecting connectivity changes in neuronal networks

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.

► The Cox method can be used to estimate connectivity in networks of neurons.
► We evaluate sensitivity and specificity of the method for general computational neural models.
► A variation of the method is developed to track significant changes in network connectivity.
► The method is demonstrated on a network of cultured mammalian spinal cord cells with MEA measurements.