ArticleScientific reports2021
Optimal responsiveness and information flow in networks of heterogeneous neurons.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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
- Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses.Journal of computational neuroscience · 2025Article
- A computational approach to evaluate how molecular mechanisms impact large-scale brain activity.Nature computational science · 2025Article
- Model-agnostic neural mean field with a data-driven transfer function.Neuromorphic computing and engineering · 2024Article
- Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function.bioRxiv : the preprint server for biology · 2024Article
- Neural heterogeneity controls computations in spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Brain-inspired modular echo state network for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- Multiscale modeling of neuronal dynamics in hippocampus CA1.Frontiers in computational neuroscience · 2024Article
- Bursting gamma oscillations in neural mass models.Frontiers in computational neuroscience · 2024Article
- A multi-layer mean-field model of the cerebellum embedding microstructure and population-specific dynamics.PLoS computational biology · 2023Article
- Intrinsic neural diversity quenches the dynamic volatility of neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Noise Enhancement of Neural Information Processing.Entropy (Basel, Switzerland) · 2022Article
- Exact mean-field models for spiking neural networks with adaptation.Journal of computational neuroscience · 2022Article
- Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons.Frontiers in computational neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.
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.