ArticlePLoS computational biology2015
Asynchronous Rate Chaos in Spiking Neuronal Circuits.
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 46 papers.
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46 citing papers in PubMed.
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
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- Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall.PLoS computational biology · 2025Article
- Dynamical mean-field theory for a highly heterogeneous neural population with graded persistent activity of the entorhinal cortex.PLoS computational biology · 2025Article
- Brain-wide organization of intrinsic timescales at single-neuron resolution.bioRxiv : the preprint server for biology · 2025Article
- Comparison of FORCE trained spiking and rate neural networks shows spiking networks learn slowly with noisy, cross-trial firing rates.PLoS computational biology · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- Theory of interaction between untuned modulatory inputs and tuned sensory inputs.bioRxiv : the preprint server for biology · 2025Article
- Hippocampal phase precession may be generated by chimera dynamics.Frontiers in neural circuits · 2025Article
- Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys.Neuron · 2023Article
- Geometry of population activity in spiking networks with low-rank structure.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
- Input correlations impede suppression of chaos and learning in balanced firing-rate networks.PLoS computational biology · 2022Article
- Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights.PLoS computational biology · 2022Article
- Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks With Intra-Correlative and Inter-Independent Connections.Frontiers in physiology · 2022Article
- The Mean Field Approach for Populations of Spiking Neurons.Advances in experimental medicine and biology · 2022Article
- A convolutional neural network for estimating synaptic connectivity from spike trains.Scientific reports · 2021Article
- Article
- Optimal learning with excitatory and inhibitory synapses.PLoS computational biology · 2020Article
- DNN-assisted statistical analysis of a model of local cortical circuits.Scientific reports · 2020Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.
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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.