ArticlePLoS computational biology2015
Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
- Deriving connectivity from spiking activity in detailed models of large-scale cortical microcircuits.iScience · 2026Article
- Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.ArXiv · 2025Article
- Inference of network connectivity from temporally binned spike trains.Journal of neuroscience methods · 2024Article
- Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains.PLoS computational biology · 2024Article
- Stimulation-mediated reverse engineering of silent neural networks.Journal of neurophysiology · 2023Article
- Aligning latent representations of neural activity.Nature biomedical engineering · 2023Article
- Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2022Article
- Inference of stochastic time series with missing data.Physical review. E · 2021Article
- Article
- Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.eLife · 2021Article
- Nonequilibrium Green's Functions for Functional Connectivity in the Brain.Physical review letters · 2021Article
- Systematic errors in connectivity inferred from activity in strongly recurrent networks.Nature neuroscience · 2020Article
- Dynamic Tracking Algorithm for Time-Varying Neuronal Network Connectivity using Wide-Field Optical Image Video Sequences.Scientific reports · 2020Article
- Review
- Predicting how and when hidden neurons skew measured synaptic interactions.PLoS computational biology · 2018Article
- Inferring circuit mechanisms from sparse neural recording and global perturbation in grid cells.eLife · 2018Article
- Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology.Journal of computational neuroscience · 2018Article
- Signatures of criticality arise from random subsampling in simple population models.PLoS computational biology · 2017Article
- Using computational theory to constrain statistical models of neural data.Current opinion in neurobiology · 2017Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.