ArticlePLoS computational biology2017
Probabilistic models for neural populations that naturally capture global coupling and criticality.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Article
- Article
- Article
- Density estimation for ordinal biological sequences and its applications.Physical review. E · 2024Article
- Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling.Scientific reports · 2024Article
- Article
- Article
- Article
- Scale-free correlations and potential criticality in weakly ordered populations of brain cancer cells.Science advances · 2023Article
- Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Direct coupling analysis of epistasis in allosteric materials.PLoS computational biology · 2020Article
- Clustering of Neural Activity: A Design Principle for Population Codes.Frontiers in computational neuroscience · 2020Article
- Between Perfectly Critical and Fully Irregular: A Reverberating Model Captures and Predicts Cortical Spike Propagation.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Cross-Frequency Coupling in Descending Motor Pathways: Theory and Simulation.Frontiers in systems neuroscience · 2019Article
- Inferring collective dynamical states from widely unobserved systems.Nature communications · 2018Article
- Blindfold learning of an accurate neural metric.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Operating in a Reverberating Regime Enables Rapid Tuning of Network States to Task Requirements.Frontiers in systems neuroscience · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system's state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.
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.