The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability

• Published in: Neuron (Cambridge, Mass.) Vol. 98; no. 4; pp. 846 - 860.e5
• Main Authors: Hennequin, Guillaume, Ahmadian, Yashar, Rubin, Daniel B., Lengyel, Máté, Miller, Kenneth D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 16.05.2018; Elsevier Limited; Cell Press
• Subjects: Animals; Charitable foundations; circuit dynamics; cortical variability; Data analysis; Data processing; Macaca; Neural Inhibition - physiology; Neural networks; Neural Networks, Computer; Neurons; Neurons - physiology; Noise; noise correlations; Nonlinear Dynamics; Occipital Lobe - cytology; Occipital Lobe - physiology; Somatosensory cortex; Spatial discrimination; Temporal lobe; theoretical neuroscience; variability quenching; Visual Cortex - cytology; Visual Cortex - physiology; variability quenching; cortical variability; MT; circuit dynamics; V1; noise correlations; theoretical neuroscience
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2018.04.017

Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception. •A simple network model explains stimulus-tuning of cortical variability suppression•Inhibition stabilizes recurrently interacting neurons with supralinear I/O functions•Stimuli strengthen inhibitory stabilization around a stable state, quenching variability•Single-trial V1 data are compatible with this model and rules out competing proposals Stimuli suppress cortical correlated variability. Hennequin et al. show that a cortical operating regime of inhibitory stabilization around a single stable state—the “stabilized supralinear network”—explains this suppression’s tuning and timing, while alternative proposed regimes do not.