ArticleProceedings of the National Academy of Sciences of the United States of America2016
Analysis of complex neural circuits with nonlinear multidimensional hidden state models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 7 citations in OpenAlex.
- Spontaneous movements and their relationship to neural activity fluctuate with latent engagement states.Neuron · 2025Article
- Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.Cell · 2020Article
- Uncovering hidden brain state dynamics that regulate performance and decision-making during cognition.Nature communications · 2018Article
- Inferring neuronal network functional connectivity with directed information.Journal of neurophysiology · 2017Article
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.
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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.