What is the dynamical regime of cerebral cortex?
Not on the map. Why?
Abstract (arXiv)
Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size to the factors that cancel, rather than tight, meaning that the net input is very small relative to the cancelling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Aug 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2024How does the primate brain combine generative and discriminative computations in vision?cited by 4
- 2022Input correlations impede suppression of chaos and learning in balanced rate networkscited by 3
- 2021A brain basis of dynamical intelligence for AI and computational neurosciencecited by 1
- 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systemscited by 0
- 2025Dynamical stability for dense patterns in attractor neural networkscited by 0
- 2026TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamicscited by 0
- 2026TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamics19 May 2026
- 2025Dynamical stability for dense patterns in attractor neural networks14 Jul 2025
- 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems6 Dec 2024
- 2024How does the primate brain combine generative and discriminative computations in vision?11 Jan 2024
- 2022Input correlations impede suppression of chaos and learning in balanced rate networks24 Jan 2022
- 2021A brain basis of dynamical intelligence for AI and computational neuroscience15 May 2021