ArticlePLoS computational biology2015
Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Information entropy dynamics, self-organization, and cybernetical neuroscience.Frontiers in network physiology · 2025Article
- Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.ArXiv · 2024Article
- Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.Advances in neural information processing systems · 2024Article
- Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation.Journal of neural engineering · 2022Article
- Article
- Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses.Frontiers in systems neuroscience · 2020Article
- Emergence of Mixed Mode Oscillations in Random Networks of Diverse Excitable Neurons: The Role of Neighbors and Electrical Coupling.Frontiers in computational neuroscience · 2020Article
- Article
- Maintaining extensivity in evolutionary multiplex networks.PloS one · 2017Article
- Chaotic, informational and synchronous behaviour of multiplex networks.Scientific reports · 2016Article
- Chimera-like States in Modular Neural Networks.Scientific reports · 2016Article
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
No grant is acknowledged in the PubMed record.
Abstract
We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.
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.