Clustering Synaptically-Coupled Neuronal Populations under Systematic Variations in Temporal Dependence
El Dawlatly, S.; Oweiss, K.G.
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Volume , Issue , 22-26 Aug. 2007 Page(s):1445 - 1448
Digital Object Identifier 10.1109/IEMBS.2007.4352572
Summary:Identifying clusters of neurons that exhibit functional interdependency in a recorded population has recently emerged as a direct result of the ability to simultaneously record multiple single unit activity with high- density microelectrode arrays. We demonstrated in a previous study that a graph theoretic approach can identify functional interdependency over multiple time scales between models of neuronal firing in response to a common input or synaptically- coupled in a multi-cluster population. In this paper, we investigate the performance of the technique in the case of neuronal interaction arising at various latencies and interval lengths. We report the capability of the approach to track these variable degrees of interactions. This feature can be very useful in decoding variable motor cortical response patterns during sensorimotor integration in Brain Machine Interface applications.
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