The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis ...Continue Reading
Clinical and electroencephalographic correlates of generalized spike and wave bursts occurring spontaneously in man
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Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology
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Fine Spike Timing in Hippocampal-Prefrontal Ensembles Predicts Poor Encoding and Underlies Behavioral Performance in Healthy and Malformed Brains.
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BRAIN Initiative Cell Census Network (BICCN)
The BRAIN Initiative Cell Census Network aims to identify and provide experimental access to the different brain cell types to determine their roles in health and disease. Discover the latest research from researchers in the BRAIN Initiative Cell Census Network here.