Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation.

Alexandre Mahrach,Nuo Li,Guang Chen,David Hansel,Carl Van Vreeswijk

doi:10.7554/elife.49967

Alexandre Mahrach, Nuo Li + Show 3 more

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https://doi.org/10.7554/elife.49967

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Abstract

GABAergic interneurons can be subdivided into three subclasses: parvalbumin positive (PV), somatostatin positive (SOM) and serotonin positive neurons. With principal cells (PCs) they form complex networks. We examine PCs and PV responses in mouse anterior lateral motor cortex (ALM) and barrel cortex (S1) upon PV photostimulation in vivo. In ALM layer five and S1, the PV response is paradoxical: photoexcitation reduces their activity. This is not the case in ALM layer 2/3. We combine analytical calculations and numerical simulations to investigate how these results constrain the architecture. Two-population models cannot explain the results. Four-population networks with V1-like architecture account for the data in ALM layer 2/3 and layer 5. Our data in S1 can be explained if SOM neurons receive inputs only from PCs and PV neurons. In both four-population models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.

Highlights

Local cortical circuits comprise several subclasses of GABAergic interneurons which together with the excitatory neurons form complex recurrent networks (Goldberg et al, 2004; Jiang et al, 2015; Karnani et al, 2016; Markram et al, 2004; Moore et al, 2010; Pfeffer et al, 2013; Tasic et al, 2018; Tremblay et al, 2016)
We studied the response of cortex to optogenetic stimulation of parvalbumin positive (PV) neurons and provided a mechanistic account for it
The suppression of the principal cells (PCs) and PV activity was the same relative to baseline

Summary

Introduction

Local cortical circuits comprise several subclasses of GABAergic interneurons which together with the excitatory neurons form complex recurrent networks (Goldberg et al, 2004; Jiang et al, 2015; Karnani et al, 2016; Markram et al, 2004; Moore et al, 2010; Pfeffer et al, 2013; Tasic et al, 2018; Tremblay et al, 2016). The architecture of these networks depends on the cortical area and layer (Beierlein et al, 2003; Jiang et al, 2013; Rudy et al, 2011; Xu et al, 2013; Xu and Callaway, 2009). Because of the complexity of these networks and of their nonlinear dynamics, qualitative intuition and simple reasoning (e.g. ‘boxandarrow’ diagrams) are of limited use to interpret the results of these manipulations

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