ArticlePLoS computational biology2014
Searching for collective behavior in a large network of sensory neurons.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 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
107 citing papers in PubMed.
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- Discovering interpretable low-dimensional dynamics using maximum entropy.Physical review. E · 2026Article
- Article
- Article
- From microstates to macroscales: A Critical Review of Maximum Entropy Modeling and Energy Landscape Analysis in functional MRI.Research square · 2025Article
- Minimax entropy: The statistical physics of optimal models.Physical review. E · 2025Article
- Intrinsic Neural Timescales Relate to Event-Related Activity-Key Role for Intracolumnar Connections.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Article
- Explosive neural networks via higher-order interactions in curved statistical manifolds.Nature communications · 2025Article
- Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Homeostatic synaptic normalization optimizes learning in network models of neural population codes.eLife · 2024Article
- Entropy of Neuronal Spike Patterns.Entropy (Basel, Switzerland) · 2024Review
- Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain.Physical review. X · 2024Article
- Spatiotemporal organization of ant foraging from a complex systems perspective.Scientific reports · 2024Article
- The quality and complexity of pairwise maximum entropy models for large cortical populations.PLoS computational biology · 2024Article
- Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling.Scientific reports · 2024Article
- Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks.Scientific reports · 2024Article
- Heavy-tailed neuronal connectivity arises from Hebbian self-organization.Nature physics · 2024Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.
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.