ArticleNeuroinformatics2021
Causal Network Inference for Neural Ensemble Activity.
Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Jacobian Granger causality for count and binary data with applications to causal network inference.Scientific reports · 2025Article
- Advocating the Use of Bayesian Network in Analyzing the Modes of Occurrence of Elements in Coal.ACS omega · 2023Article
- Bayesian Coherence Analysis for Microcircuit Structure Learning.Neuroinformatics · 2023Article
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1 author.
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Abstract
Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters.
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Registered trials
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.