ArticleNature communications2019
Reconstructing neuronal circuitry from parallel spike trains.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
- Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.Neuroinformatics · 2026Article
- A multimodal approach for visualizing and identifying electrophysiological cell types in vivo.Nature communications · 2026Article
- Comparing effective and functional connectivity.Scientific reports · 2026Article
- Prefrontal cortex interneurons and their contributions to attention, working memory, and adaptive behavior.Progress in neurobiology · 2026Review
- Sensory and palatability coding of taste stimuli in cortex involves dynamic and asymmetric cortico-amygdalar interactions.Journal of neurophysiology · 2026Article
- Deriving connectivity from spiking activity in detailed models of large-scale cortical microcircuits.iScience · 2026Article
- Modelling brain metabolism with interacting nonautonomous phase oscillators.Frontiers in network physiology · 2026Article
- Article
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
- A multimodal approach for visualization and identification of electrophysiological cell typesbioRxiv : the preprint server for biology · 2025Article
- Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.Scientific reports · 2025Article
- Sensory-spinal neuron co-culture platform enables analysis of sensory-driven spinal activation.Frontiers in neuroscience · 2025Article
- Stimulus type shapes the topology of cellular functional networks in mouse visual cortex.Nature communications · 2024Article
- Nondifferentiable activity in the brain.PNAS nexus · 2024Article
- 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
- Shared input and recurrency in neural networks for metabolically efficient information transmission.PLoS computational biology · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
State-of-the-art techniques allow researchers to record large numbers of spike trains in parallel for many hours. With enough such data, we should be able to infer the connectivity among neurons. Here we develop a method for reconstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlations. Our method estimates connections between neurons in units of postsynaptic potentials and the amount of spike recordings needed to verify connections. The performance of inference is optimized by counting the estimation errors using synthetic data. This method is superior to other established methods in correctly estimating connectivity. By applying our method to rat hippocampal data, we show that the types of estimated connections match the results inferred from other physiological cues. Thus our method provides the means to build a circuit diagram from recorded spike trains, thereby providing a basis for elucidating the differences in information processing in different brain regions.
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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.