Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression

Abstract

SUMMARY In what regime does the cortical circuit operate? Our intracellular studies of surround suppression in cat primary visual cortex (V1) provide strong evidence on this question. Although suppression has been thought to arise from an increase in lateral inhibition, we find that the inhibition that cells receive is reduced, not increased, by a surround stimulus. Instead, suppression is mediated by a withdrawal of excitation. Thalamic recordings and previous work show that these effects cannot be explained by a withdrawal of thalamic input. We find in theoretical work that this behavior can only arise if V1 operates as an inhibition-stabilized network (ISN), in which excitatory recurrence alone is strong enough to destabilize visual responses but feedback inhibition maintains stability. We confirm two strong tests of this scenario experimentally, and show through simulation that observed cell-to-cell variability in surround effects, from facilitation to suppression, can arise naturally from variability in the ISN.

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

“…In this article, we used neural fields to model the dispersion of axonal connections and describe the effect of surround suppression on gamma responses, see also [Pinotsis et al, 2013, 2014]. Finally, our source model comprises four populations whose connectivity is similar to recent theoretical and experimental work that focuses on the origins of visually induced gamma peak [Jadi and Sejnowski, 2014b; Ozeki et al, 2009]. …”

Neural Wave Interference in Inhibition-Stabilized Networks

“…1) [20]. We use the Wilson-Cowan model because even a single-node version of it (which consists of one excitatory cell and one inhibitory cell) has proven useful for understanding behavior of small cortical circuits [21], and its rich dynamical properties [22] have been helpful in studies of large-scale neural phenomena [23,24].…”

“…Variables r E (l) and r I (l) are the firing rates of the excitatory and inhibitory cells at node l, respectively, and τ E is the characteristic time of excitatory cells (while time is normalized on the characteristic time of the inhibitory cells). Coefficients w EE , w EI , w IE and w II describe the interactions of the excitatory and inhibitory cells within every node (as in [21]), andw EE ,w EI ,w IE ,w II represent the strength of coupling between the nearest nodes. Inputs i E (l) and i I (l) of the excitatory and inhibitory cells are generated by the stimulation, while input ratio i E (l)/i I (l) = α is the same for all the nodes (although it could be modeled as an additional parameter).…”

Renewal theory of coupled neuronal pools: Stable states and slow trajectories

“…Classically, pool dynamics are described by a set of coupled ordinary differential equations [8][9][10]. These approaches have been, and still are, very successfully applied to model neuronal processing in both cognitive and sensory cortical areas (e.g., [11,12]). Such a temporally local description, however, cannot account for intrinsic delays and the dependence of spike probability on the spiking history.…”