We find that in both mouse and monkey V1, optogenetic stimulation of excitatory neurons strongly modulates the activity of single neurons, yet had weak or no effects on the distribution of firing rates across the population. We refer to this effect as reshuffling, and show that emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength is sufficient to elicit powerful inhibitory feedback.
Here we propose a parallel between optogenetic and behavioral modulations of activity and characterize their impact on cell-type-specific V1 processing under a common theoretical framework. We infer cell-type-specific circuitry from large-scale V1 recordings and demonstrate that, given strong recurrent excitation, the cell-type-specific responses imply key aspects of the known connectivity. In the inferred models, parvalbumin-expressing (PV), but not other, interneurons have responses to perturbations that we show theoretically imply that their activity stabilizes the circuit. We infer inputs that explain locomotion-induced changes in firing rates and find that, contrary to hypotheses of simple disinhibition, locomotory drive to VIP cells and to SOM cells largely cancel, with enhancement of excitatory-cell visual responses likely due to direct locomotory drive to them. We show that this SOM/VIP cancellation is a property emerging from V1 connectivity structure.
Here we show that models of cortical circuits near the onset of oscillatory synchrony selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity.
We investigate the dynamical properties of network models with respect to two known control parameters regulating collective oscillatory activity: delayed recurrent inhibition and an external periodic drive. To do so, we advanced the tractability of large spiking networks of exactly solvable neuronal models by developing a strategy that allows for exact characterization of the dynamics on the attractor of effectively delayed network models in a system with fixed and finite degrees of freedom. We find that, below the transition to collective oscillations, neuronal networks have a stereotypical dependence on the delay so far only described for scalar systems and low-dimensional maps. We demonstrate that the emergence of internally generated oscillations induces a complete dynamical reconfiguration, by increasing the dimensionality of the chaotic attractor, the speed at which nearby trajectories separate from one another, and the rate at which the network produces entropy. We find that periodic input drive leads to a dramatic increase of chaotic measures at a the resonance frequency of the recurrent network. However, transient oscillatory input only has a moderate role on the collective dynamics.