ReviewThe Journal of neuroscience : the official journal of the Society for Neuroscience2023
Connecting Connectomes to Physiology.
Review in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Differential temporal filtering in the fly optic lobe.Journal of computational neuroscience · 2025Article
- Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research.Cellular and molecular neurobiology · 2024Review
- Connectivity Matrix Seriation via Relaxation.PLoS computational biology · 2024Article
- New Challenges for Anatomists in the Era of Omics.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
2 authors.
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Abstract
With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis and matrix reordering techniques. Such analytical treatment can allow us to make predictions about time constants of information processing and functional subunits in large networks.
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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.