Clustering synaptically-coupled neuronal populations under systematic variations in temporal dependence.
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.
El Dawlatly
Seif
S
ECE Department at Michigan State University, East Lansing, MI 48824, USA. eldawlat@msu.edu
Oweiss
Karim G
KG
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D009435:Synaptic Transmission / Q000502:physiology*