title: 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 of the network over the course of the entire experiment, thus ameliorating the common input problem. Using a generalized linear model for a spiking recurrent neural network, we develop a scalable approximate expected loglikelihood-based Bayesian method to perform network inference given this type of data, in which only a small fraction of the network is observed in each time bin. We demonstrate in simulation that the shotgun experimental design can eliminate the biases induced by common input effects. Networks with thousands of neurons, in which only a small fraction of the neurons is observed in each time bin, can be quickly and accurately estimated, achieving orders of magnitude speed up over previous approaches.
authors.affiliation: Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Soudry
authors.affiliation: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America.
authors.forename: Suraj
authors.initials: S
authors.lastname: Keshri
authors.affiliation: Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America.
authors.forename: Patrick
authors.initials: P
authors.lastname: Stinson
authors.affiliation: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America.
authors.forename: Min-Hwan
authors.initials: MH
authors.lastname: Oh
authors.affiliation: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York, United States of America.
authors.forename: Garud
authors.initials: G
authors.lastname: Iyengar
authors.affiliation: Department of Statistics, Department of Neuroscience, the Center for Theoretical Neuroscience, the Grossman Center for the Statistics of Mind, the Kavli Institute for Brain Science, and the NeuroTechnology Center, Columbia University, New York, New York, United States of America.
authors.forename: Liam
authors.initials: L
authors.lastname: Paninski
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D018401:Sample Size; D009435:Synaptic Transmission / Q000502:physiology*