Common rules underlying optogenetic and behavioral modulation of responses in multi-cell-type V1 circuits

Abstract

Identifying the regime in which the cortical microcircuit operates is a prerequisite to determine the mechanisms that mediate its response to stimulus. Classic modeling work has started to characterize this regime through the study of perturbations, but an encompassing perspective that links the full ensemble of the network’s response to appropriate descriptors of the cortical operating regime is still lacking. Here we develop a class of mathematically tractable models that exactly describe the modulation of the distribution of cell-type-specific calcium-imaging activity with the contrast of a visual stimulus. The model’s fit recovers signatures of the connectivity structure found in mouse visual cortex. Analysis of this structure subsequently reveal parameter-independent relations between the responses of different cell types to perturbations and each interneuron’s role in circuit-stabilization. Leveraging recent theoretical approaches, we derive explicit expressions for the distribution of responses to partial perturbations which reveal a novel, counter-intuitive effect in the sign of response functions.

Interrogating theoretical models of neural computation with emergent property inference

“…First, we demonstrate the superior scalability of EPI compared to alternative techniques by inferring high-dimensional distributions of recurrent neural network connectivities that exhibit amplified, yet stable responses. Next, in a model of primary visual cortex [46,47], we show how EPI discovers parametric degeneracy, revealing how input variability across neuron types affects the excitatory population. Finally, in a model of superior colliculus [48], we used EPI to capture multiple parametric regimes of task switching, and queried the dimensions of parameter sensitivity to characterize each regime.…”

“…In particular three types of genetically identified inhibitory cell-types -parvalbumin (P), somatostatin (S), VIP (V) -compose 80% of GABAergic interneurons in V1 [61][62][63], and follow specific connectivity patterns (Fig. 3A) [64], which lead to cell-type specific computations [47,96].…”

“…As contrast c ∈ [0, 1] increases, input to the E-and P-populations increases relative to a baseline input h = h b + ch c . Connectivity (W fit ) and input (h b,fit and h c,fit ) parameters were fit using the deterministic V1 circuit model [47]…”

“…Stochasticity and inhibitory multiplicity introduce substantial complexity to the mathematical treatment of this problem (see Section 5.4.5) motivating the analysis of this model with EPI. Here, we consider fixed weights W and input h [47], and study the effect of input variability z = [σ E , σ P , σ S , σ V ] on excitatory variability.…”

“…Then, we show EPI's scalability to high dimensional parameter distributions by inferring connectivities of recurrent neural networks that exhibit stable, yet amplified responses -a hallmark of neural responses throughout the brain [43][44][45]. In a model of primary visual cortex [46,47], EPI reveals how the recurrent processing across different neuron-type populations shapes excitatory variability: a finding that we show is analytically intractable. Finally, we investigated the possible connectivities of a superior colliculus model that allow execution of different tasks on interleaved trials [48].…”