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Nick Lesica

Ear Institute, UCL, UK


Wednesday 14 April 2010



Seminar Room B10 (Basement)

Alexandra House, 17 Queen Square, London, WC1N 3AR



New tools for the analysis and modeling of population spike trains


As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new tools for the analysis and of modeling of these data must also be developed. In this talk I will describe two new such tools. The first tool is a model for simulating the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model allows the simulation of population spike trains with PSTH, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the single cell properties and pairwise correlations in the spike trains to be manipulated independently. I will demonstrate the utility of the model in simulating and manipulating experimental responses from the mammalian auditory and visual systems. The second tool is a new method for characterizing the strength and dynamics of the functional connectivity between neurons: incremental mutual information (IMI). IMI measures how informative the activity of one neuron is about another a particular delay, after the past activity of both neurons has already been considered. IMI improves on the correlation-based measures that are typically used to study functional connectivity in two important ways: 1) IMI does not assume linearity and 2) IMI measures only the dependencies due to connectivity, ignoring those due to external stimuli or shared inputs, without the need for repeated trials. I will demonstrate the utility of IMI in characterizing the functional connectivity between cells in the mammalian visual pathway.