ArticleFrontiers in physiology2016
Criticality Maximizes Complexity in Neural Tissue.
Article in Frontiers in physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- The goo that binds us: how field resonance solves neuroscience's binding and criticality problems.Frontiers in computational neuroscience · 2026Article
- Capturing the emergent dynamical structure in biophysical neural models.PLoS computational biology · 2025Article
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions.PLoS computational biology · 2024Article
- Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures.Journal of neurophysiology · 2024Article
- The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits.Network neuroscience (Cambridge, Mass.) · 2024Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.PloS one · 2023Article
- Article
- Article
- Avalanche criticality in individuals, fluid intelligence, and working memory.Human brain mapping · 2022Article
- Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features.Neuroscience of consciousness · 2022Article
- Addressing skepticism of the critical brain hypothesis.Frontiers in computational neuroscience · 2022Review
- Criticality enhances the multilevel reliability of stimulus responses in cortical neural networks.PLoS computational biology · 2022Article
- From the origins to the stream of consciousness and its neural correlates.Frontiers in integrative neuroscience · 2022Article
- Toward a Unified Analysis of the Brain Criticality Hypothesis: Reviewing Several Available Tools.Frontiers in neural circuits · 2022Review
- Neuronal avalanche dynamics and functional connectivity elucidate information propagationFrontiers in neural circuits · 2022Article
- Optimal Organization of Functional Connectivity Networks for Segregation and Integration With Large-Scale Critical Dynamics in Human Brains.Frontiers in computational neuroscience · 2021Article
- Not One, but Many Critical States: A Dynamical Systems Perspective.Frontiers in neural circuits · 2021Article
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
No grant is acknowledged in the PubMed record.
Abstract
The analysis of neural systems leverages tools from many different fields. Drawing on techniques from the study of critical phenomena in statistical mechanics, several studies have reported signatures of criticality in neural systems, including power-law distributions, shape collapses, and optimized quantities under tuning. Independently, neural complexity-an information theoretic measure-has been introduced in an effort to quantify the strength of correlations across multiple scales in a neural system. This measure represents an important tool in complex systems research because it allows for the quantification of the complexity of a neural system. In this analysis, we studied the relationships between neural complexity and criticality in neural culture data. We analyzed neural avalanches in 435 recordings from dissociated hippocampal cultures produced from rats, as well as neural avalanches from a cortical branching model. We utilized recently developed maximum likelihood estimation power-law fitting methods that account for doubly truncated power-laws, an automated shape collapse algorithm, and neural complexity and branching ratio calculation methods that account for sub-sampling, all of which are implemented in the freely available Neural Complexity and Criticality MATLAB toolbox. We found evidence that neural systems operate at or near a critical point and that neural complexity is optimized in these neural systems at or near the critical point. Surprisingly, we found evidence that complexity in neural systems is dependent upon avalanche profiles and neuron firing rate, but not precise spiking relationships between neurons. In order to facilitate future research, we made all of the culture data utilized in this analysis freely available online.
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.