ArticlePLoS computational biology2017
Intrinsically-generated fluctuating activity in excitatory-inhibitory networks.
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 29 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
29 citing papers in PubMed, 80 citations in OpenAlex.
- Strong and localized recurrence controls the dimensionality of neural activity across brain areas.Nature neuroscience · 2026Article
- Intrinsic chaos control in cortical circuits: A minimal E-I-M rate model for primary visual cortex.Journal of computational neuroscience · 2026Article
- A theory for self-sustained balanced states in absence of strong external currents.PLoS computational biology · 2026Article
- Structure of activity in multiregion recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Nondifferentiable activity in the brain.PNAS nexus · 2024Article
- Input correlations impede suppression of chaos and learning in balanced firing-rate networks.PLoS computational biology · 2022Article
- The Mean Field Approach for Populations of Spiking Neurons.Advances in experimental medicine and biology · 2022Article
- Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks With Intra-Correlative and Inter-Independent Connections.Frontiers in physiology · 2022Article
- Article
- Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.Neural computation · 2021Article
- Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model.Neuron · 2021Article
- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- Striatal network modeling in Huntington's Disease.PLoS computational biology · 2020Article
- Classification Methods Based on Complexity and Synchronization of Electroencephalography Signals in Alzheimer's Disease.Frontiers in psychiatry · 2020Article
- Cortical computations via metastable activity.Current opinion in neurobiology · 2019Review
- Temporal-specific complexity of spiking patterns in spontaneous activity induced by a dual complex network structure.Scientific reports · 2019Article
- Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity.PLoS computational biology · 2019Article
- How single neuron properties shape chaotic dynamics and signal transmission in random neural networks.PLoS computational biology · 2019Article
- Contrasting the effects of adaptation and synaptic filtering on the timescales of dynamics in recurrent networks.PLoS computational biology · 2019Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurrent networks of non-linear units display a variety of dynamical regimes depending on the structure of their synaptic connectivity. A particularly remarkable phenomenon is the appearance of strongly fluctuating, chaotic activity in networks of deterministic, but randomly connected rate units. How this type of intrinsically generated fluctuations appears in more realistic networks of spiking neurons has been a long standing question. To ease the comparison between rate and spiking networks, recent works investigated the dynamical regimes of randomly-connected rate networks with segregated excitatory and inhibitory populations, and firing rates constrained to be positive. These works derived general dynamical mean field (DMF) equations describing the fluctuating dynamics, but solved these equations only in the case of purely inhibitory networks. Using a simplified excitatory-inhibitory architecture in which DMF equations are more easily tractable, here we show that the presence of excitation qualitatively modifies the fluctuating activity compared to purely inhibitory networks. In presence of excitation, intrinsically generated fluctuations induce a strong increase in mean firing rates, a phenomenon that is much weaker in purely inhibitory networks. Excitation moreover induces two different fluctuating regimes: for moderate overall coupling, recurrent inhibition is sufficient to stabilize fluctuations; for strong coupling, firing rates are stabilized solely by the upper bound imposed on activity, even if inhibition is stronger than excitation. These results extend to more general network architectures, and to rate networks receiving noisy inputs mimicking spiking activity. Finally, we show that signatures of the second dynamical regime appear in networks of integrate-and-fire neurons.
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.