Obeid, D. | Search bioRxiv

bioRxiv2021

Stabilized Supralinear Network: Model of Layer 2/3 of the Primary Visual Cortex

Electrophysiological recording in the primary visual cortex (V1) of mammals have revealed a number of complex interactions between the center and surround. Understanding the underlying circuit mechanisms is crucial to understanding fundamental brain computations. In this paper we address the following phenomena that have been observed in V1 of animals with orientation maps: 1) surround suppression that is accompanied by a decrease in the excitatory and inhibitory currents that the cell receives as the stimulus size increases beyond the cells summation field; 2) surround tuning to the center orientation, in which the strongest suppression arises when the surround orientation matches that of the center stimulus; and 3) feature-specific suppression, in which a surround stimulus of a given orientation specifically suppresses that orientations component of the response to a center plaid stimulus. We show that a stabilized supralinear network that has biologically plausible connectivity and synaptic efficacies that depend on cortical distance and orientation difference between neurons can consistently reproduce all the above phenomena. We explain the mechanism behind each result, and argue that feature-specific suppression and surround tuning to the center orientation are independent phenomena. Specifically, if we remove some aspects of the connectivity from the model it will still produce feature-specific suppression but not surround tuning to the center orientation. We also show that in the model the activity decay time constant is similar to the cortical activity decay time constant reported in mouse V1. Our model indicates that if the surround activates neurons that fall within the reach of the horizontal projections in V1, the above mentioned phenomena can be generated by V1 alone without the need of cortico-cortical feedback. Finally, we show that these results hold both in networks with rate-based units and with conductance-based spiking units. This demonstrates that the stabilized supra-linear network mechanism can be achieved in the more biological context of spiking networks.

neuroscience↗

bioRxiv2020

Statistical structure of the trial-to-trial timing variability in synfire chains

Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here, we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit, the synfire chain. First, we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a chain of single integrate-and-fire neurons. We show that this circuits variability is well-described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next, we establish in simulations that these results carry over to a noisy homogeneous synfire chain. Finally, motivated by the fact that a synfire chain is thought to underlie the circuit that takes part in the control and timing of zebra finch song, we present simulations of a biologically realistic synfire chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings, and to agree with experimental observations of the songs temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.

neuroscience↗