Single Circuit in V1 Capable of Switching Contexts during Movement Using an Inhibitory Population as a Switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatiotemporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units.

Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers

The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.

Total number and ratio of GABAergic neuron types in the mouse lateral and basal amygdala

GABAergic neurons are key circuit elements in cortical networks. In spite of growing evidence showing that inhibitory cells play a critical role in the lateral (LA) and basal (BA) amygdala functions, neither the number of GABAergic neurons nor the ratio of their distinct types have been determined in these amygdalar nuclei. Using unbiased stereology, we found that the ratio of GABAergic neurons in the BA (22 %) is significantly higher than in the LA (16 %) in both male and female mice. No difference was observed between the right and left hemispheres in either sexes. In addition, we assessed the ratio of the major inhibitory cell types in both amygdalar nuclei. Using transgenic mice and a viral strategy for visualizing inhibitory cells combined with immunocytochemistry, we estimated that the following cell types together compose the vast majority of GABAergic cells in the LA and BA: axo-axonic cells (5.5-6 %), basket cells expressing parvalbumin (17-20 %) or cholecystokinin (7-9 %), dendrite-targeting inhibitory cells expressing somatostatin (10-16 %), NPY-containing neurogliaform cells (14-15 %), VIP and/or calretinin-expressing interneuron-selective interneurons (29-38 %) and GABAergic projection neurons expressing somatostatin and neuronal nitric oxide synthase (nNOS, 5.5-8 %). Our results show that these amygdalar nuclei contain all major GABAergic neuron types as found in other cortical regions. Furthermore, our data offer an essential reference for future studies aiming to reveal changes in GABAergic cell number and in inhibitory cell types typically observed under different pathological conditions, and to model functioning amygdalar networks in health and disease.

Host interneurons mediate plasticity reactivated by embryonic inhibitory cell transplantation in mouse visual cortex

The adult brain lacks sensitivity to changes in the sensory environment found in the juvenile brain. The transplantation of embryonic interneurons has been shown to restore juvenile plasticity to the adult host visual cortex. It is unclear whether transplanted interneurons directly mediate the renewed cortical plasticity or whether these cells act indirectly by modifying the host interneuron circuitry. Here we find that the transplant-induced reorganization of mouse host circuits is specifically mediated by Neuregulin (NRG1)/ErbB4 signaling in host parvalbumin (PV) interneurons. Brief visual deprivation reduces the visual activity of host PV interneurons but has negligible effects on the responses of transplanted PV interneurons. Exogenous NRG1 both prevents the deprivation-induced reduction in the visual responses of host PV interneurons and blocks the transplant-induced reorganization of the host circuit. While deletion of ErbB4 receptors from host PV interneurons blocks cortical plasticity in the transplant recipients, deletion of the receptors from the donor PV interneurons does not. Altogether, our results indicate that transplanted embryonic interneurons reactivate cortical plasticity by rejuvenating the function of host PV interneurons.

Generalized paradoxical effects in excitatory/inhibitory networks

An inhibition-stabilized network (ISN) is a network of excitatory and inhibitory cells at a stable fixed point of firing rates for a given input, for which the excitatory subnetwork would be unstable if inhibitory rates were frozen at their fixed point values. It has been shown that in a low-dimensional model (one unit per neuronal subtype) of an ISN with a single excitatory and single inhibitory cell type, the inhibitory unit shows a “paradoxical” response, lowering (raising) its steady-state firing rate in response to addition to it of excitatory (inhibitory) input. This has been generalized to an ISN with multiple inhibitory cell types: if input is given only to inhibitory cells, the steady-state inhibition received by excitatory cells changes paradoxically, that is, it decreases (increases) if the steady-state excitatory firing rates decrease (increase).We generalize these analyses of paradoxical effects to low-dimensional networks with multiple cell types of both excitatory and inhibitory neurons. The analysis depends on the connectivity matrix of the network linearized about a given fixed point, and its eigenvectors or “modes”. We show the following: (1) A given cell type shows a paradoxical change in steady-state rate in response to input it receives, if and only if the network with that cell type omitted has an odd number of unstable modes. Excitatory neurons can show paradoxical responses when there are two or more inhibitory subtypes. (2) More generally, if the cell types are divided into two nonoverlapping subsets A and B, then subset B has an odd (even) number of modes that show paradoxical response if and only if subset A has an odd (even) number of unstable modes. (3) The net steady-state inhibition received by any unstable mode of the excitatory subnetwork will change paradoxically, i.e. in the same direction as the change in amplitude of that mode. In particular, this means that a sufficient condition to determine that a network is an ISN is if, in response to an input only to inhibitory cells, the firing rates of and inhibition received by all excitatory cell types all change in the same direction. This in turn will be true if all E cells and all inhibitory cell types that connect to E cells change their firing rates in the same direction.

Deciphering how specialized interneuron-specific cell types contribute to circuit function

The wide diversity of inhibitory cells across the brain makes them fit to contribute to network dynamics in specialized fashions. However, the contributions of a particular inhibitory cell type in a behaving animal is challenging to decipher as one needs to both record cellular activities and identify the cell type being recorded. Thus, using computational modeling to explore cell-specific contributions so as to predict and hypothesize functional contributions is desirable. Here we examine potential contributions of interneuron-specific 3 (I-S3) cells - a type of inhibitory interneuron found in CA1 hippocampus that only targets other inhibitory interneurons - during simulated theta rhythms. We use previously developed multi-compartment models of oriens lacunosum-moleculare (OLM) cells, the main target of I-S3 cells, and explore how I-S3 cell inputs during in vitro and in vivo scenarios contribute to theta. We find that I-S3 cells suppress OLM cell spiking, rather than engender its spiking via post-inhibitory rebound mechanisms. To elicit recruitment similar to experiment, the inclusion of disinhibited pyramidal cell inputs is necessary, suggesting that I-S3 cell firing can broaden the window for disinhibiting pyramidal cells. Using in vivo virtual networks, we show that I-S3 cells can contribute to a sharpening of OLM cell recruitment at theta frequencies. Further, a shifting of the timing of I-S3 cell spiking due to external modulation can shift the timing of the OLM cell firing and thus disinhibitory windows. We thus propose a specialized contribution of I-S3 cells to create temporally precise coordination of modulation pathways.Significance StatementHow information is processed across different brain structures is an important question that relates to the different functions that the brain performs. In this work we use computational models that focus on a particular inhibitory cell type that only inhibits other inhibitory cell types – the I-S3 cell in the hippocampus. We show that this cell type is able to broaden the window for disinhibition of excitatory cells. We further illustrate that this broadening presents itself as a mechanism for input pathway switching and modulation over the timing of inhibitory cell spiking. Overall, this work contributes to our knowledge of how coordination between sensory and memory consolidation information is attained in a brain area that is involved in memory formation.

Fast and reversible neural inactivation in macaque cortex by optogenetic stimulation of GABAergic neurons

Optogenetic techniques for neural inactivation are valuable for linking neural activity to behavior but they have serious limitations in macaques. To achieve powerful and temporally precise neural inactivation, we used an adeno-associated viral (AAV) vector carrying the channelrhodopsin-2 gene under the control of a Dlx5/6 enhancer, which restricts expression to GABAergic neurons. We tested this approach in the primary visual cortex, an area where neural inactivation leads to interpretable behavioral deficits. Optical stimulation modulated spiking activity and reduced visual sensitivity profoundly in the region of space represented by the stimulated neurons. Rebound firing, which can have unwanted effects on neural circuits following inactivation, was not observed, and the efficacy of the optogenetic manipulation on behavior was maintained across >1000 trials. We conclude that this inhibitory cell-type-specific optogenetic approach is a powerful and spatiotemporally precise neural inactivation tool with broad utility for probing the functional contributions of cortical activity in macaques.

Experience-dependent inhibitory plasticity is mediated by CCK+ basket cells in the developing dentate gyrus

Early postnatal experience shapes both inhibitory and excitatory networks in the hippocampus. However, the underlying circuit plasticity is unclear. Using an enriched environment (EE) paradigm, we assessed the circuit plasticity of inhibitory cell-types in the hippocampus. We found that cholecystokinin (CCK)-expressing basket cells strongly increased somatic inhibition on the excitatory granular cells (GC) following EE while another pivotal inhibitory cell-type, parvalbumin (PV)-expressing cells did not show changes. By inhibiting activity of the entorhinal cortex (EC) using a chemogenetic approach, we demonstrate that the projections from the EC is responsible for the developmental plasticity of CCK+ basket cells. Our measurement of the input decorrelation by DG circuit suggests that EE has little effect on pattern separation despite of the altered CCK+ basket cell circuit. Altogether, our study places the activity-dependent remodeling of CCK+ basket cell innervation as a central process to adjust inhibition in the DG, while maintaining the computation in the circuit.

Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types

Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined.HighlightsGeneration and evaluation of more than 130 million single-cell models with active conductances along the reconstructed morphology faithfully recapitulate the electrophysiology of 230 in vitro experiments.Optimized ion channel conductances along the cellular morphology (‘all-active’) are characteristic of model complexity and offer enhanced biophysical realism.Ion channel conductance vectors of all-active models classify transcriptomically defined cell-types.Cell type differences in ion channel conductances predicted by the models correlate with experimentally measured single-cell gene expression differences in inhibitory (Pvalb, Sst, Htr3a) and excitatory (Nr5a1, Rbp4) classes.A set of ion channel conductances identified by comparing between cell type model populations explain electrophysiology differences between these types in simulations and brain slice experiments.All-active models recapitulate multimodal properties of excitatory and inhibitory cell types offering a systematic and causal way of linking differences between them.