Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types

Molino, Luis Carlos García del; Yang, Guangyu Robert; Mejías, Jorge F.; Wang, Xiao Jing

doi:10.7554/elife.29742

Cited by 57 publications

(84 citation statements)

References 41 publications

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“…The interaction between these neuron types has been recently implicated, among other phenomena, in the top-down modulation of activity during locomotion [46; 47; 48; 49; 50]. In particular, the observed change of modulation with context (dark vs visual stimuli) has been explained with a rate model with supralinear f − µ curve, assuming a change in baseline activity (low vs high) [51]. As we have shown in the present work, other scenarios exist close to response onset in networks of spiking neurons, which might lead to alternative explanations of the observed phenomena.…”

Section: Discussionmentioning

confidence: 99%

Response nonlinearities in networks of spiking neurons

Sanzeni

Histed

Brunel

2019

Preprint

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Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities contradict the balancedstate model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the observed diversity of non-linearities observed in cortical networks. AUTHOR SUMMARYModels of cortical networks are often studied in the strong coupling limit, where the so-called balanced state emerges. In this strong coupling limit, networks exhibit without fine tuning, a number of ubiquitous properties of cortex, such as the irregular nature of neuronal firing. However, it fails to account for nonlinear summation of inputs, since the strong coupling limit leads to a linear network transfer function. We show that, in networks of spiking neurons, nonlinearities at response-onset and saturation emerge at finite coupling. Critically, for realistic parameter values, both types of nonlinearities are observed at experimentally observed rates. Thus, we propose that these models could explain experimentally observed nonlinearities.

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Section: Discussionmentioning

confidence: 99%

Response nonlinearities in networks of spiking neurons

Sanzeni

Histed

Brunel

2019

Preprint

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“…Activating these neurons may also regulate activity within the local network, where CC neurons make contacts onto other CC neurons, as well as CT and PT cells (Mercer A et al 2005;Brown SP and S Hestrin 2009). Moreover, by increasing the activity of VIP+ interneurons, D1-Rs can disinhibit the local network, which is known to play an important role in cortical function, including within the PFC (Wang XJ et al 2004;Garcia Del Molino LC et al 2017;Kamigaki T and Y Dan 2017). Thus, D1-Rs can activate both excitatory circuits in deep layers and disinhibitory circuits in superficial layers.…”

Section: Discussionmentioning

confidence: 99%

Cell-type specific D1 dopamine receptor modulation of projection neurons and interneurons in the prefrontal cortex

Anastasiades

Boada

Carter

2018

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Dopamine modulation in the prefrontal cortex (PFC) mediates diverse effects on neuronal physiology and function, but the expression of dopamine receptors at sub-populations of pyramidal neurons and interneurons remains unresolved. Here, we examine D1 receptor expression and modulation at specific cell types and layers in the mouse prelimbic PFC.We first show that D1 receptors are enriched in pyramidal neurons in both layers 5 and 6, and that these cells project intra-telencephalically, rather than to sub-cortical structures.We then find that D1 receptors are also present in interneurons, and enriched in VIP+ interneurons that co-expresses calretinin, but absent from PV+ and SOM+ interneurons.Finally, we determine that D1 receptors strongly and selectively enhance action potential firing in only a subset of these cortico-cortical neurons and VIP+ interneurons. Our findings define several novel sub-populations of D1+ neurons, highlighting how modulation via D1 receptors can influence both excitatory and disinhibitory micro-circuits in the PFC. neurons that project sub-cortically (Gabbott PL et al. 2005; Dembrow NC et al. 2010; Anastasiades PG et al. 2018). Recent studies indicate that dopamine receptors may differentially segregate between these two broad populations of layer 5 projection neurons in the PFC (Gee S et al. 2012; Seong HJ and AG Carter 2012; Clarkson RL et al. 2017).Interestingly, D1-Rs are also expressed in layer 6 (L6), where they may modulate corticothalamic (CT) projections (Gaspar P et al. 1995). However, there is currently no consensus on which projection neurons primarily express D1-Rs, with reports varying significantly (Vincent SL et al. 1993;Gaspar P et al. 1995). Given that the activity of defined projection 4 neurons can have distinct effects on behavior (Land BB et al. 2014;Jenni NL et al. 2017; Murugan M et al. 2017; Otis JM et al. 2017), this represents a significant gap in our understanding of how dopamine modulates PFC outputs.Like other cortices, the PFC also contains a diverse array of GABAergic interneurons (Markram H et al. 2004; Petilla Interneuron Nomenclature G et al. 2008;Anastasiades PG and SJ Butt 2011;Rudy B et al. 2011). Interneurons are segregated into distinct subtypes based on their intrinsic electrophysiology, morphology, and immunohistochemical markers (Kubota Y and Y Kawaguchi 1994;Kawaguchi Y and Y Kubota 1996;Butt SJ et al. 2005;Gonchar Y et al. 2007;Xu X et al. 2010;Anastasiades PG et al. 2016). Moreover, dopamine receptors are thought to be expressed in several populations of interneurons (Muly EC, 3rd et al. 1998;Glausier JR et al. 2009;Santana N et al. 2009), where they can mediate diverse effects (Gonzalez-Islas C and JJ Hablitz 2001;Kroner S et al. 2007;Towers SK and S Hestrin 2008;Karunakaran S et al. 2016). However, as with projection neurons, there are conflicting reports on which interneuron subtypes express D1-Rs in the PFC (Le Moine C and P Gaspar 1998;Muly EC, 3rd et al. 1998;Paspalas CD and PS Goldman-Rakic 2005;Santana N et al. 2009). While neuromo...

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“…They argued that depending on the degree of viral expression, the average response of the infected neurons can decrease or increase with the light intensity: it decreases only if a large proportion of the population is infected. (Del Molino et al 2017) showed that due to the nonlinearity in the neuronal transfer function, the response of the network to stimulation can be different for different https://docs.google.com/document/d/1UaHERm5J7ZX8fMIoyG42_mFfOzWPZq32QDjE_gxdSgk/edit?ts=5c9dfa46# 27/46 background rates. In particular, they showed that it can reverse the response of SOM neurons to VIP stimulation.…”

Section: Comparison With Previous Theoretical Workmentioning

confidence: 99%

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

Mahrach¹,

Chen

et al. 2019

Preprint

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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 layer 5, 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. Twopopulation models cannot account for the results. Networks with three inhibitory populations and V1like architecture account for the data in ALM layer 2/3. Our data in layer 5 can be accounted for if SOM neurons receive inputs only from PCs and PV neurons. In both fourpopulation models, the paradoxical effect implies not too strong recurrent excitation. It is not evidence for stabilization by inhibition.sufficiently strong, namely ifThe denominator in is the strength of the connection from the SOM population to J EE the VIP population. The numerator is the gain of the pathway which connects these two populations via the PCs. When the negative contribution of the disinhibitory J EE > J EE loop PCVIPSOMPC dominates in the expression of . It is the opposite when χ II . The stability of the balanced state provides other necessary conditions that J EE < J EE the interactions must satisfy (Materials and Methods). In particular, the determinant of the interaction matrix, , must be positive. J of the positive feedback loop, SOMXPCSOM, is sufficiently strong (Supplementary Materials, SMD). Obviously, this condition simplifies and reads J J J EX XS > J XX ES (4) Remarkably, this inequality does not depend on . This is in contrast to what J EE happens in Model 1 where the paradoxical effect occurs only if is small enough J EE (see Eq. (2)). https://docs.google.com/document/d/1UaHERm5J7ZX8fMIoyG42_mFfOzWPZq32QDjE_gxdSgk/edit?ts=5c9dfa46# 24/46 was already substantial for small light intensities, where the PCs were still significantly active. The twopopulation model cannot account for this feature. In Model 1, whether the network exhibits a paradoxical effect depends on the value of the ratio where . Here, , is theThe requirement that at baseline the network state is fully balanced and stable implies that J I0 Therefore, the PC population activity increases upon PC stimulation if J J J J IX XS > IS XX (SM61)

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Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types

Cited by 57 publications

References 41 publications

Response nonlinearities in networks of spiking neurons

Response nonlinearities in networks of spiking neurons

Cell-type specific D1 dopamine receptor modulation of projection neurons and interneurons in the prefrontal cortex

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

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