ArticlePLoS computational biology2018
Predicting how and when hidden neurons skew measured synaptic interactions.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics.Nature communications · 2025Article
- Constraining inference of across-region interactions using neural activity perturbations.bioRxiv : the preprint server for biology · 2025Article
- Phase transitions inArXiv · 2025Article
- Article
- Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.Physical review research · 2024Article
- The quality and complexity of pairwise maximum entropy models for large cortical populations.PLoS computational biology · 2024Article
- Aligning latent representations of neural activity.Nature biomedical engineering · 2023Article
- Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.PLoS computational biology · 2022Article
- Article
- 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
- Inference of synaptic connectivity and external variability in neural microcircuits.Journal of computational neuroscience · 2020Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
- Causes and consequences of representational drift.Current opinion in neurobiology · 2019Review
- Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity.PLoS computational biology · 2019Article
- Second type of criticality in the brain uncovers rich multiple-neuron dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the "hidden" portion of the network. To properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuron r' to a post-synaptic neuron r can be decomposed into a sum of the true interaction from r' to r plus corrections from every directed path from r' to r through unobserved neurons. Importantly, the resulting formula reveals when the hidden units have-or do not have-major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with "strong" coupling-connection weights that scale with [Formula: see text], where N is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.
Indexed as
Identifiers
What OpenQuestion holds
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.