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  • Poster presentation
  • Open Access

Network reconstruction in the presence of unmeasured neurons

BMC Neuroscience20078 (Suppl 2) :P20

https://doi.org/10.1186/1471-2202-8-S2-P20

  • Published:

Keywords

  • Animal Model
  • Network Analysis
  • Large Range
  • Large Class
  • Essential Feature

We present a method to determine whether a correlation in the spikes of two neurons is due to a causal connection between the neurons or due to common input originating from unmeasured neurons. The distinction is based on a point-process model of how a neuron's spiking probability can depend on both its own spiking history and a stimulus (or other external variables). Although the results depend on selecting a parametric model that captures essential features of the neural response, a large class of models can be used with the network analysis. Hence, the analysis could be applied to probe circuitry in a large range of neuronal systems.

Authors’ Affiliations

(1)
Department of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA

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