Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.

ABSTRACT: Inferring connectivity in neuronal networks remains a key challenge in statistical neuroscience. The "common input" problem presents a major roadblock: it is difficult to reliably distinguish causal connections between pairs of observed neurons versus correlations induced by common input from unobserved neurons. Available techniques allow us to simultaneously record, with sufficient temporal resolution, only a small fraction of the network. Consequently, naive connectivity estimators that neglect these common input effects are highly biased. This work proposes a "shotgun" experimental design, in which we observe multiple sub-networks briefly, in a serial manner. Thus, while the full network cannot be observed simultaneously at any given time, we may be able to observe much larger subsets o

SUBMITTER: Soudry D 

PROVIDER: S-EPMC4605541 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature