Broadening of inhibitory tuning underlies contrast-dependent sharpening of orientation selectivity in mouse visual cortex

Functional specificity of recurrent inhibition in visual cortex

In the neocortex, neural activity is shaped by the interaction of excitatory and inhibitory neurons, defined by the organization of their synaptic connections. Although connections among excitatory pyramidal neurons are sparse and functionally tuned, inhibitory connectivity is thought to be dense and largely unstructured. By measuring in vivo visual responses and synaptic connectivity of parvalbumin-expressing (PV+) inhibitory cells in mouse primary visual cortex, we show that the synaptic weights of their connections to nearby pyramidal neurons are specifically tuned according to the similarity of the cells' responses. Individual PV+ cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This structured organization of inhibitory synaptic weights provides a circuit mechanism for tuned inhibition onto pyramidal cells despite dense connectivity, stabilizing activity within feature-specific excitatory ensembles while supporting competition between them.

Unsupervised approach to decomposing neural tuning variability

Neural representation is often described by the tuning curves of individual neurons with respect to certain stimulus variables. Despite this tradition, it has become increasingly clear that neural tuning can vary substantially in accordance with a collection of internal and external factors. A challenge we are facing is the lack of appropriate methods to accurately capture the moment-to-moment tuning variability directly from the noisy neural responses. Here we introduce an unsupervised statistical approach, Poisson functional principal component analysis (Pf-PCA), which identifies different sources of systematic tuning fluctuations, moreover encompassing several current models (e.g.,multiplicative gain models) as special cases. Applying this method to neural data recorded from macaque primary visual cortex- a paradigmatic case for which the tuning curve approach has been scientifically essential- we discovered a simple relationship governing the variability of orientation tuning, which unifies different types of gain changes proposed previously. By decomposing the neural tuning variability into interpretable components, our method enables discovery of unexpected structure of the neural code, capturing the influence of the external stimulus drive and internal states simultaneously.

Gamma oscillations point to the role of primary visual cortex in atypical motion processing in autism

Neurophysiological studies suggest that abnormal neural inhibition may explain a range of sensory processing differences in autism spectrum disorders (ASD). In particular, the impaired ability of people with ASD to visually discriminate the motion direction of small-size objects and their reduced perceptual suppression of background-like visual motion may stem from deficient surround inhibition within the primary visual cortex (V1) and/or its atypical top-down modulation by higher-tier cortical areas. In this study, we estimate the contribution of abnormal surround inhibition to the motion-processing deficit in ASD. For this purpose, we used a putative correlate of surround inhibition-suppression of the magnetoencephalographic (MEG) gamma response (GR) caused by an increase in the drift rate of a large annular high-contrast grating. The motion direction discrimination thresholds for the gratings of different angular sizes (1° and 12°) were assessed in a separate psychophysical paradigm. The MEG data were collected in 42 boys with ASD and 37 typically developing (TD) boys aged 7-15 years. Psychophysical data were available in 33 and 34 of these participants, respectively. The results showed that the GR suppression in V1 was reduced in boys with ASD, while their ability to detect the direction of motion was compromised only in the case of small stimuli. In TD boys, the GR suppression directly correlated with perceptual suppression caused by increasing stimulus size, thus suggesting the role of the top-down modulations of V1 in surround inhibition. In ASD, weaker GR suppression was associated with the poor directional sensitivity to small stimuli, but not with perceptual suppression. These results strongly suggest that a local inhibitory deficit in V1 plays an important role in the reduction of directional sensitivity in ASD and that this perceptual deficit cannot be explained exclusively by atypical top-down modulation of V1 by higher-tier cortical areas.

The dynamic shaping of local cortical circuitry by sex and age, and its relation to Pattern Comparison Processing Speed

Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown that the strength of local neural interactions decreases with distance. Here we extend that line of research to evaluate effects of sex and age on local cortical circuitry in 6 cortical areas (superior frontal, precentral, postcentral, superior parietal, inferior parietal, lateral occipital) using data acquired from 1,054 healthy young adults who participated in the Human Connectome Project. We confirmed previous findings that the strength of zero-lag correlations between prewhitened, resting-state, blood level oxygenation-dependent (BOLD) fMRI time series decreased with distance locally, and documented that the rate of decrease with distance ("spatial steepness") (a) was progressively lower from anterior to posterior areas, (b) was greater in women, especially in anterior areas, (c) increased with age, particularly for women, (d) was significantly correlated with percent inhibition, and (e) was positively and highly significantly correlated with pattern comparison processing speed (PCPS). A hierarchical tree clustering analysis of this dependence of PCPS on spatial steepness revealed a differential organization in processing that information between the two hemispheres, namely a more independent vs. a more integrative processing in the left and right hemispheres, respectively. These findings document sex and age differences in dynamic local cortical interactions, and provide evidence that spatial sharpening of these interactions may underlie cognitive processing speed differently organized in the two hemispheres.

Parvalbumin-Positive Interneurons Regulate Cortical Sensory Plasticity in Adulthood and Development Through Shared Mechanisms

Parvalbumin-positive neurons are the largest class of GABAergic, inhibitory neurons in the central nervous system. In the cortex, these fast-spiking cells provide feedforward and feedback synaptic inhibition onto a diverse set of cell types, including pyramidal cells, other inhibitory interneurons, and themselves. Cortical inhibitory networks broadly, and cortical parvalbumin-expressing interneurons (cPVins) specifically, are crucial for regulating sensory plasticity during both development and adulthood. Here we review the functional properties of cPVins that enable plasticity in the cortex of adult mammals and the influence of cPVins on sensory activity at four spatiotemporal scales. First, cPVins regulate developmental critical periods and adult plasticity through molecular and structural interactions with the extracellular matrix. Second, they activate in precise sequence following feedforward excitation to enforce strict temporal limits in response to the presentation of sensory stimuli. Third, they implement gain control to normalize sensory inputs and compress the dynamic range of output. Fourth, they synchronize broad network activity patterns in response to behavioral events and state changes. Much of the evidence for the contribution of cPVins to plasticity comes from classic models that rely on sensory deprivation methods to probe experience-dependent changes in the brain. We support investigating naturally occurring, adaptive cortical plasticity to study cPVin circuits in an ethologically relevant framework, and discuss recent insights from our work on maternal experience-induced auditory cortical plasticity.

Emergence of probabilistic representation in the neural network of primary visual cortex

During the early development of the mammalian visual system, the distribution of neuronal preferred orientations in the primary visual cortex (V1) gradually shifts to match major orientation features of the environment, achieving its optimal representation. By combining computational modeling and electrophysiological recording, we provide a circuit plasticity mechanism that underlies the developmental emergence of such matched representation in the visual cortical network. Specifically, in a canonical circuit of densely-interconnected pyramidal cells and inhibitory parvalbumin-expressing (PV+) fast-spiking interneurons in V1 layer 2/3, our model successfully simulates the experimental observations and further reveals that the nonuniform inhibition plays a key role in shaping the network representation through spike timing-dependent plasticity. The experimental results suggest that PV + interneurons in V1 are capable of providing nonuniform inhibition shortly after vision onset. Our study elucidates a circuit mechanism for acquisition of prior knowledge of environment for optimal inference in sensory neural systems

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Computational and experimental methods are combined to representation in mice V1

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Nonuniform inhibition plays a key role in shaping the network representation

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PV + interneurons provide nonuniform inhibition shortly after vision onset

A novel theoretical framework for simultaneous measurement of excitatory and inhibitory conductances

The firing of neurons throughout the brain is determined by the precise relations between excitatory and inhibitory inputs, and disruption of their balance underlies many psychiatric diseases. Whether or not these inputs covary over time or between repeated stimuli remains unclear due to the lack of experimental methods for measuring both inputs simultaneously. We developed a new analytical framework for instantaneous and simultaneous measurements of both the excitatory and inhibitory neuronal inputs during a single trial under current clamp recording. This can be achieved by injecting a current composed of two high frequency sinusoidal components followed by analytical extraction of the conductances. We demonstrate the ability of this method to measure both inputs in a single trial under realistic recording constraints and from morphologically realistic CA1 pyramidal model cells. Future experimental implementation of our new method will facilitate the understanding of fundamental questions about the health and disease of the nervous system.

Most neurons in the brain receive synaptic inputs from both excitatory and inhibitory neurons. Together, these inputs determine neuronal activity: excitatory synapses shift the electrical potential across the membrane towards the threshold for generation of action potentials, whereas inhibitory synapses lower this potential away from the threshold. Action potentials are the rapid electrochemical signals that transmit information to other neurons and they are critical for the information processing abilities of the brain.

Although there are many ways to measure either excitatory or inhibitory inputs, these methods have been unable to measure both at the same time. Measuring both inputs together is essential towards understanding how neurons integrate information. We developed a new analytical method to measure excitatory and inhibitory inputs at the same time from the voltage response to injection of an alternating current into a neuron. We describe the foundation of this new method and find that it works in biologically realistic simulations of neurons. By using this technique in real neurons, scientists could investigate basic principles of information processing in the healthy and diseased brain.

The contribution of low contrast-preferring neurons to information representation in the primary visual cortex after learning

Animals exhibit improved perception of lower-contrast visual objects after training. We explored this neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination. We found that the firing rates of a subset of neurons increased by reducing luminance contrast, being at least above basal activities at low contrast. These low contrast–preferring neurons were rare during passive viewing without training or anesthesia after training. They fired more frequently in correct-choice than incorrect-choice trials. At single-neuron and population levels, they efficiently represented low-contrast orientations. Following training, in addition to generally enhanced excitation, the phase synchronization of spikes to beta oscillations at high contrast was stronger in putative inhibitory than excitatory neurons. The change in excitation-inhibition balance might contribute to low-contrast preference. Thus, low-contrast preference in V1 activity is strengthened in an experience-dependent manner, which may contribute to low-contrast visual discrimination.

Chronic bisphenol A exposure triggers visual perception dysfunction through impoverished neuronal coding ability in the primary visual cortex

Contrast perception is a fundamental visual ability that allows us to distinguish objects from the background. However, whether it is perturbed by chronic exposure to environmental xenoestrogen, bisphenol A (BPA), is still elusive. Here, we used adult cats to explore BPA-induced changes in contrast sensitivity (CS) and its underlying neuronal coding mechanism. Behavioral results showed that 14 days of BPA exposure (0.4 mg/kg/day) was sufficient to induce CS declines at the tested spatial frequencies (0.05-2 cycles/deg) in all four cats. Furthermore, based on multi-channel electrophysiological recording and interneuronal correlation analysis, we found that the BPA-exposed cats exhibited an obvious up-regulation in noise correlation in the primary visual cortex (area 17, A17), thus providing a population neuronal coding basis for their perceptual dysfunction. Moreover, single neuron responses in A17 of BPA-exposed cats revealed a slight but marked decrease in CS compared to that of control cats. Additionally, these neuronal responses presented an overt decrease in signal-to-noise ratio, accompanied by increased trial-to-trial response variability (i.e., noise). To some extent, these neuron population and unit dysfunctions in A17 of BPA-exposed cats were attributable to decreased response activity of fast-spiking neurons. Together, our findings demonstrate that chronic BPA exposure restricts contrast perception, in response to impoverished neuronal coding ability in A17.

A Quantitative Comparison of Inhibitory Interneuron Size and Distribution between Mouse and Macaque V1, Using Calcium-Binding Proteins

The mouse is a useful and popular model for studying of visual cortical function. To facilitate the translation of results from mice to primates, it is important to establish the extent of cortical organization equivalence between species and to identify possible differences. We focused on the different types of interneurons as defined by calcium-binding protein (CBP) expression in the layers of primary visual cortex (V1) in mouse and rhesus macaque. CBPs parvalbumin (PV), calbindin (CB), and calretinin (CR) provide a standard, largely nonoverlapping, labeling scheme in macaque, with preserved corresponding morphologies in mouse, despite a slightly higher overlap. Other protein markers, which are relevant in mouse, are not preserved in macaque. We fluorescently tagged CBPs in V1 of both species, using antibodies raised against preserved aminoacid sequences. Our data demonstrate important similarities between the expression patterns of interneuron classes in the different layers between rodents and primates. However, in macaque, expression of PV and CB is more abundant, CR expression is lower, and the laminar distribution of interneuron populations is more differentiated. Our results reveal an integrated view of interneuron types that provides a basis for translating results from rodents to primates, and suggest a reconciliation of previous results.

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model

The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement computations, as revealed by the study of mouse primary visual cortex. Further, we discuss how studying the mouse has broadened our understanding of the range of computations performed by visual cortex. Finally, we address how future approaches will fulfill the promise of the mouse in elucidating fundamental operations of cortex.

Precision multidimensional neural population code recovered from single intracellular recordings

Neurons in sensory cortices are more naturally and deeply integrated than any current neural population recording tools (e.g. electrode arrays, fluorescence imaging). Two concepts facilitate efforts to observe population neural code with single-cell recordings. First, even the highest quality single-cell recording studies find a fraction of the stimulus information in high-dimensional population recordings. Finding any of this missing information provides proof of principle. Second, neurons and neural populations are understood as coupled nonlinear differential equations. Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell stimulus decoding. We obtained intracellular recordings of fluctuating transmembrane current and potential in mouse visual cortex during stimulation with drifting gratings. We use mean deflection from baseline when comparing to prior single-cell studies because action potentials are too sparse and the deflection response to drifting grating stimuli (e.g. tuning curves) are well studied. Equation-based decoders allowed more precise single-trial stimulus discrimination than tuning-curve-base decoders. Performance varied across recorded signal types in a manner consistent with population recording studies and both classification bases evinced distinct stimulus-evoked phases of population dynamics, providing further corroboration. Naturally and deeply integrated observations of population dynamics would be invaluable. We offer proof of principle and a versatile framework.

Organization, Function, and Development of the Mouse Retinogeniculate Synapse

Visual information is encoded in distinct retinal ganglion cell (RGC) types in the eye tuned to specific features of the visual space. These streams of information project to the visual thalamus, the first station of the image-forming pathway. In the mouse, this connection between RGCs and thalamocortical neurons, the retinogeniculate synapse, has become a powerful experimental model for understanding how circuits in the thalamus are constructed to process these incoming lines of information. Using modern molecular and genetic tools, recent studies have suggested a more complex circuit organization than was previously understood. In this review, we summarize the current understanding of the structural and functional organization of the retinogeniculate synapse in the mouse. We discuss a framework by which a seemingly complex circuit can effectively integrate and parse information to downstream stations of the visual pathway. Finally, we review how activity and visual experience can sculpt this exquisite connectivity.

CMOS-based bio-image sensor spatially resolves neural activity-dependent proton dynamics in the living brain

Recent studies have shown that protons can function as neurotransmitters in cultured neurons. To further investigate regional and neural activity-dependent proton dynamics in the brain, the development of a device with both wide-area detectability and high spatial-ltemporal resolution is necessary. Therefore, we develop an image sensor with a high spatial-temporal resolution specifically designed for measuring protons in vivo. Here, we demonstrate that spatially deferent neural stimulation by visual stimulation induced distinct patterns of proton changes in the visual cortex. This result indicates that our biosensor can detect micrometer and millisecond scale changes of protons across a wide area. Our study demonstrates that a CMOS-based proton image sensor with high spatial and temporal precision can be used to detect pH changes associated with biological events. We believe that our sensor may have broad applicability in future biological studies.

Protons have been discovered to play a role in neuronal signaling, but current methods to measure pH in the brain of animal models are limited. Here the authors develop a miniaturized proton image sensor that fits into a living mouse brain and can measure pH changes at micrometer and millisecond resolution scales.

Enhanced robustness of convolutional networks with a push–pull inhibition layer

Convolutional neural networks (CNNs) lack robustness to test image corruptions that are not seen during training. In this paper, we propose a new layer for CNNs that increases their robustness to several types of corruptions of the input images. We call it a ‘push–pull’ layer and compute its response as the combination of two half-wave rectified convolutions, with kernels of different size and opposite polarity. Its implementation is based on a biologically motivated model of certain neurons in the visual system that exhibit response suppression, known as push–pull inhibition. We validate our method by replacing the first convolutional layer of the LeNet, ResNet and DenseNet architectures with our push–pull layer. We train the networks on original training images from the MNIST and CIFAR data sets and test them on images with several corruptions, of different types and severities, that are unseen by the training process. We experiment with various configurations of the ResNet and DenseNet models on a benchmark test set with typical image corruptions constructed on the CIFAR test images. We demonstrate that our push–pull layer contributes to a considerable improvement in robustness of classification of corrupted images, while maintaining state-of-the-art performance on the original image classification task. We released the code and trained models at the url http://github.com/nicstrisc/Push-Pull-CNN-layer.

A Differential Circuit via Retino-Colliculo-Pulvinar Pathway Enhances Feature Selectivity in Visual Cortex through Surround Suppression

In the mammalian visual system, information from the retina streams into parallel bottom-up pathways. It remains unclear how these pathways interact to contribute to contextual modulation of visual cortical processing. By optogenetic inactivation and activation of mouse lateral posterior nucleus (LP) of thalamus, a homolog of pulvinar, or its projection to primary visual cortex (V1), we found that LP contributes to surround suppression of layer (L) 2/3 responses in V1 by driving L1 inhibitory neurons. This results in subtractive suppression of visual responses and an overall enhancement of orientation, direction, spatial, and size selectivity. Neurons in V1-projecting LP regions receive bottom-up input from the superior colliculus (SC) and respond preferably to non-patterned visual noise. The noise-dependent LP activity allows V1 to "cancel" noise effects and maintain its orientation selectivity under varying noise background. Thus, the retina-SC-LP-V1 pathway forms a differential circuit with the canonical retino-geniculate pathway to achieve context-dependent sharpening of visual representations.

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

Spatial Asymmetry and Short-Term Suppression Underlie Direction Selectivity of Synaptic Excitation in the Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. With in vivo whole-cell voltage-clamp recordings from layer (L)4 neurons in the mouse visual cortex, we observed a strong correlation between DS and spatial asymmetry in the distribution of excitatory input strengths. This raises an interesting possibility that the latter may contribute to DS. The preferred direction of excitatory input was found from the stronger to weaker side of its spatial receptive field. A simple linear summation of asymmetrically distributed excitatory responses to stationary flash stimuli however failed to predict the correct directionality: it at best resulted in weak DS with preferred direction opposite to what was observed experimentally. Further studies with sequential 2 flash-bar stimulation revealed a short-term suppression of excitatory input evoked by the late bar. More importantly, the level of the suppression positively correlated with the relative amplitude of the early-bar response. Implementing this amplitude-dependent suppressive interaction can successfully predict DS of excitatory input. Our results suggest that via nonlinear temporal interactions, the spatial asymmetry can be transformed into differential temporal integration of inputs under opposite directional movements. This mechanism may contribute to the DS of excitatory inputs to L4 neurons.

Visual physiology of the layer 4 cortical circuit in silico

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.

How can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry by employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in response to a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 –in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

Recently, sophisticated optogenetic tools for mouse have enabled many detailed studies of the neuronal circuits of its primary visual cortex (V1), providing much more specific information than is available for cat or monkey. Among various other differences, they show a striking contrast dependency in orientation selectivity in mouse V1 rather than the well-known contrast invariance for cat and monkey. Constrained by the existing experiment data, we develop a comprehensive large-scale model of an effective input layer of mouse V1 that successfully reproduces the contrast-dependent phenomena and many other response properties. The model helps to probe different mechanisms based on excitation–inhibition balance that underlie both contrast dependencies and invariance, and it provides implications for future studies on these circuits.

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice

The synaptic mechanisms of feature coding in the visual cortex are poorly understood, particularly in awake animals. The ratio between excitation (E) and inhibition (I) might be constant across stimulus space, controlling only the gain and timing of neuronal responses, or it might change, directly contributing to feature coding. Whole-cell recordings in L2/3 of awake mice revealed that the E/I ratio systematically declines with increasing stimulus contrast or size. Suppressing somatostatin (SOM) neurons enhanced the E and I underlying size tuning, explaining SOM neurons' role in surround suppression. These data imply that contrast and size tuning result from a combination of a changing E/I ratio and the tuning of total synaptic input. Furthermore, they provide experimental support in awake animals for the "Stabilized Supralinear Network," a model that explains diverse cortical phenomena, and suggest that a decreasing E/I ratio with increasing cortical drive could contribute to many different cortical computations.

Contribution of Innate Cortical Mechanisms to the Maturation of Orientation Selectivity in Parvalbumin Interneurons

The maturation of cortical parvalbumin-positive (PV) interneurons depends on the interaction of innate and experience-dependent factors. Dark-rearing experiments suggest that visual experience determines when broad orientation selectivity emerges in visual cortical PV interneurons. Here, using neural transplantation and in vivo calcium imaging of mouse visual cortex, we investigated whether innate mechanisms contribute to the maturation of orientation selectivity in PV interneurons. First, we confirmed earlier findings showing that broad orientation selectivity emerges in PV interneurons by 2 weeks after vision onset, ∼35 d after these cells are born. Next, we assessed the functional development of transplanted PV (tPV) interneurons. Surprisingly, 25 d after transplantation (DAT) and >2 weeks after vision onset, we found that tPV interneurons have not developed broad orientation selectivity. By 35 DAT, however, broad orientation selectivity emerges in tPV interneurons. Transplantation does not alter orientation selectivity in host interneurons, suggesting that the maturation of tPV interneurons occurs independently from their endogenous counterparts. Together, these results challenge the notion that the onset of vision solely determines when PV interneurons become broadly tuned. Our results reveal that an innate cortical mechanism contributes to the emergence of broad orientation selectivity in PV interneurons.

SIGNIFICANCE STATEMENT

Early visual experience and innate developmental programs interact to shape cortical circuits. Visual-deprivation experiments have suggested that the onset of visual experience determines when interneurons mature in the visual cortex. Here we used neuronal transplantation and cellular imaging of visual responses to investigate the maturation of parvalbumin-positive (PV) interneurons. Our results suggest that the emergence of broad orientation selectivity in PV interneurons is innately timed.

Altered visual cortical processing in a mouse model of MECP2 duplication syndrome

As an epigenetic modulator of gene expression, Methyl-CpG binding protein 2 (MeCP2) is essential for normal neurological function. Dysfunction of MeCP2 is associated with a variety of neurological disorders. MECP2 gene duplication in human causes neuropsychiatric symptoms such as mental retardation and autism. MeCP2 overexpression in mice results in neurobehavioural disorders, dendritic abnormalities, and synaptic defects. However, how gain of MeCP2 function influences cortical processing of sensory information remains unclear. In this study, we examined visual processing in a mouse model of MECP2 duplication syndrome (MECP2 Tg1 mouse) at 8 and 14 weeks, which were before and after the onset of behavioural symptoms, respectively. In vivo extracellular recordings from primary visual cortex (V1) showed that neurons in Tg1 mice at both adult ages preferred higher spatial frequencies (SFs) than those in wild-type (WT) littermate controls, and the semi-saturation contrasts of neurons were lower in Tg1 mice at 8 weeks but not at 14 weeks. Behavioural experiments showed that the performance for visual detection at high SFs and low contrasts was higher in MECP2 Tg1 mice. Thus, MeCP2 gain-of-function in mice leads to higher visual acuity and contrast sensitivity, both at the levels of cortical response and behavioural performance.

Synaptic Contributions to Receptive Field Structure and Response Properties in the Rodent Lateral Geniculate Nucleus of the Thalamus

Comparative physiological and anatomical studies have greatly advanced our understanding of sensory systems. Many lines of evidence show that the murine lateral geniculate nucleus (LGN) has unique attributes, compared with other species such as cat and monkey. For example, in rodent, thalamic receptive field structure is markedly diverse, and many cells are sensitive to stimulus orientation and direction. To explore shared and different strategies of synaptic integration across species, we made whole-cell recordings in vivo from the murine LGN during the presentation of visual stimuli, analyzed the results with different computational approaches, and compared our findings with those from cat. As for carnivores, murine cells with classical center-surround receptive fields had a "push-pull" structure of excitation and inhibition within a given On or Off subregion. These cells compose the largest single population in the murine LGN (∼40%), indicating that push-pull is key in the form vision pathway across species. For two cell types with overlapping On and Off responses, which recalled either W3 or suppressed-by-contrast ganglion cells in murine retina, inhibition took a different form and was most pronounced for spatially extensive stimuli. Other On-Off cells were selective for stimulus orientation and direction. In these cases, retinal inputs were tuned and, for oriented cells, the second-order subunit of the receptive field predicted the preferred angle. By contrast, suppression was not tuned and appeared to sharpen stimulus selectivity. Together, our results provide new perspectives on the role of excitation and inhibition in retinothalamic processing.

SIGNIFICANCE STATEMENT

We explored the murine lateral geniculate nucleus from a comparative physiological perspective. In cat, most retinal cells have center-surround receptive fields and push-pull excitation and inhibition, including neurons with the smallest (highest acuity) receptive fields. The same is true for thalamic relay cells. In mouse retina, the most numerous cell type has the smallest receptive fields but lacks push-pull. The most common receptive field in rodent thalamus, however, is center-surround with push-pull. Thus, receptive field structure supersedes size per se for form vision. Further, for many orientation-selective cells, the second-order component of the receptive field aligned with stimulus preference, whereas suppression was untuned. Thus, inhibition may improve spatial resolution and sharpen other forms of selectivity in rodent lateral geniculate nucleus.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts. Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Orientation selectivity in rat primary visual cortex emerges earlier with low-contrast and high-luminance stimuli

In natural vision, rapid and sustained variations in luminance and contrast change the reliability of information available about a visual scene, and markedly affect both neuronal and behavioural responses. The hallmark property of neurons in primary visual cortex (V1), orientation selectivity, is unaffected by changes in stimulus contrast, but it remains unclear how sustained differences in mean luminance and contrast affect the time-course of orientation selectivity, and the amount of information that neurons carry about orientation. We used reverse correlation with characterize the temporal dynamics of orientation selectivity in rat V1 neurons under four luminance-contrast conditions. We show that orientation selectivity and mutual information between neuronal responses and stimulus orientation are invariant to contrast or mean luminance. Critically, the time-course of the emergence of orientation selectivity was affected by both factors; response latencies were longer for low- than high-luminance gratings, and surprisingly, response latencies were also longer for high- than low-contrast gratings. Modelling suggests that luminance-modulated changes in feedforward gain, in combination with hyperpolarization caused by high contrasts can account for our physiological data. The hyperpolarization at high contrasts may increase signal-to-noise ratios, whereas a more depolarized membrane may lead to greater sensitivity to weak stimuli.

Functional dissection of inhibitory microcircuits in the visual cortex

Cerebral cortex contains various types of GABAergic neurons exerting local inhibition. Although the number of GABAergic inhibitory neurons is much smaller than glutamatergic excitatory neurons, they show greater diversity in their morphological and physiological properties. Genetic markers for distinct sub-classes of GABAergic neurons have been identified, and technical advances achieved in the past few decades have brought about a demonstration of a unique function of each sub-class of GABAergic neurons in the cortex. In particular, visual processing in the cortex requires inhibitory function of various GABAergic neurons. Here, we summarize current understandings on the function of inhibitory neurons in the cortex, especially focusing on their roles in visual processing.

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking responses. We used a conductance-based model of the visual system in higher mammals to investigate how two specific features of the thalamo-cortical pathway, namely push-pull receptive field organization and fast synaptic depression, can contribute to this contextual reshaping of V1 responses. By comparing cortical dynamics evoked respectively by natural vs. artificial stimuli in a comprehensive parametric space analysis, we demonstrate that the reliability and sparseness of the spiking responses during natural vision is not a mere consequence of the increased bandwidth in the sensory input spectrum. Rather, it results from the combined impacts of fast synaptic depression and push-pull inhibition, the later acting for natural scenes as a form of “effective” feed-forward inhibition as demonstrated in other sensory systems. Thus, the combination of feedforward-like inhibition with fast thalamo-cortical synaptic depression by simple cells receiving a direct structured input from thalamus composes a generic computational mechanism for generating a sparse and reliable encoding of natural sensory events.

Cross-Modality Sharpening of Visual Cortical Processing through Layer-1-Mediated Inhibition and Disinhibition

Cross-modality interaction in sensory perception is advantageous for animals' survival. How cortical sensory processing is cross-modally modulated and what are the underlying neural circuits remain poorly understood. In mouse primary visual cortex (V1), we discovered that orientation selectivity of layer (L)2/3, but not L4, excitatory neurons was sharpened in the presence of sound or optogenetic activation of projections from primary auditory cortex (A1) to V1. The effect was manifested by decreased average visual responses yet increased responses at the preferred orientation. It was more pronounced at lower visual contrast and was diminished by suppressing L1 activity. L1 neurons were strongly innervated by A1-V1 axons and excited by sound, while visual responses of L2/L3 vasoactive intestinal peptide (VIP) neurons were suppressed by sound, both preferentially at the cell's preferred orientation. These results suggest that the cross-modality modulation is achieved primarily through L1 neuron- and L2/L3 VIP-cell-mediated inhibitory and disinhibitory circuits.

Optogenetic astrocyte activation modulates response selectivity of visual cortex neurons in vivo

Astrocytes play important roles in synaptic transmission and plasticity. Despite in vitro evidence, their causal contribution to cortical network activity and sensory information processing in vivo remains unresolved. Here we report that selective photostimulation of astrocytes with channelrhodopsin-2 in primary visual cortex enhances both excitatory and inhibitory synaptic transmission, through the activation of type 1a metabotropic glutamate receptors. Photostimulation of astrocytes in vivo increases the spontaneous firing of parvalbumin-positive (PV⁺) inhibitory neurons, while excitatory and somatostatin-positive (SOM⁺) neurons show either an increase or decrease in their activity. Moreover, PV⁺ neurons show increased baseline visual responses and reduced orientation selectivity to visual stimuli, whereas excitatory and SOM⁺ neurons show either increased or decreased baseline visual responses together with complementary changes in orientation selectivity. Therefore, astrocyte activation, through the dual control of excitatory and inhibitory drive, influences neuronal integrative features critical for sensory information processing.

Astrocytes regulate activity within neuronal networks. Here, the authors use photostimulation to activate astrocytes in the mouse visual cortex, and find that this increases excitatory and inhibitory neuronal synaptic transmission via activation of type 1a metabotropic glutamate receptors.

Synaptic Basis for Differential Orientation Selectivity between Complex and Simple Cells in Mouse Visual Cortex

In the primary visual cortex (V1), orientation-selective neurons can be categorized into simple and complex cells primarily based on their receptive field (RF) structures. In mouse V1, although previous studies have examined the excitatory/inhibitory interplay underlying orientation selectivity (OS) of simple cells, the synaptic bases for that of complex cells have remained obscure. Here, by combining in vivo loose-patch and whole-cell recordings, we found that complex cells, identified by their overlapping on/off subfields, had significantly weaker OS than simple cells at both spiking and subthreshold membrane potential response levels. Voltage-clamp recordings further revealed that although excitatory inputs to complex and simple cells exhibited a similar degree of OS, inhibition in complex cells was more narrowly tuned than excitation, whereas in simple cells inhibition was more broadly tuned than excitation. The differential inhibitory tuning can primarily account for the difference in OS between complex and simple cells. Interestingly, the differential synaptic tuning correlated well with the spatial organization of synaptic input: the inhibitory visual RF in complex cells was more elongated in shape than its excitatory counterpart and also was more elongated than that in simple cells. Together, our results demonstrate that OS of complex and simple cells is differentially shaped by cortical inhibition based on its orientation tuning profile relative to excitation, which is contributed at least partially by the spatial organization of RFs of presynaptic inhibitory neurons.

SIGNIFICANCE STATEMENT

Simple and complex cells, two classes of principal neurons in the primary visual cortex (V1), are generally thought to be equally selective for orientation. In mouse V1, we report that complex cells, identified by their overlapping on/off subfields, has significantly weaker orientation selectivity (OS) than simple cells. This can be primarily attributed to the differential tuning selectivity of inhibitory synaptic input: inhibition in complex cells is more narrowly tuned than excitation, whereas in simple cells inhibition is more broadly tuned than excitation. In addition, there is a good correlation between inhibitory tuning selectivity and the spatial organization of inhibitory inputs. These complex and simple cells with differential degree of OS may provide functionally distinct signals to different downstream targets.

Contrast-dependent orientation discrimination in the mouse

As an important animal model to study the relationship between behaviour and neural activity, the mouse is able to perform a variety of visual tasks, such as orientation discrimination and contrast detection. However, it is not clear how stimulus contrast influences the performance of orientation discrimination in mice. In this study, we used two task designs, two-alternative forced choice (2AFC) and go/no-go, to examine the performance of mice to discriminate two orthogonal orientations at different contrasts. We found that the performance tended to increase with contrast, and the performance at high contrast was better when the stimulus set contained a single contrast than multiple contrasts. Physiological experiments in V1 showed that neural discriminability of two orthogonal orientations increased with contrast. Furthermore, orientation discriminability of V1 neurons at high contrast was higher in the single than in the multiple contrast condition, largely due to smaller response variance in the single contrast condition. Thus, the performance of mice to discriminate orientations at high contrast is adapted to the contrast range in the stimuli, partly attributed to the contrast-range dependent capacity of V1 neurons to discriminate orientations.

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.

In primary visual cortex of mammals, neurons are selective to the orientation of contrast edges. In some species, as cats and monkeys, neurons preferring similar orientations are adjacent on the cortical surface, leading to smooth orientation maps. In rodents, in contrast, such spatial orientation maps do not exist, and neurons of different specificities are mixed in a salt-and-pepper fashion. During development, however, a “functional” map of orientation selectivity emerges, where connections between neurons of similar preferred orientations are selectively enhanced. Here we show how such feature-specific connectivity can arise in realistic neocortical networks of excitatory and inhibitory neurons. Our results demonstrate how recurrent dynamics can work in cooperation with synaptic plasticity to form networks where neurons preferring similar stimulus features connect more strongly together. Such networks, in turn, are known to enhance the specificity of neuronal responses to a stimulus. Our study thus reveals how self-organizing connectivity in neuronal networks enable them to achieve new or enhanced functions, and it underlines the essential role of recurrent inhibition and plasticity in this process.

Functional dissection of synaptic circuits: in vivo patch-clamp recording in neuroscience

Neuronal activity is dominated by synaptic inputs from excitatory or inhibitory neural circuits. With the development of in vivo patch-clamp recording, especially in vivo voltage-clamp recording, researchers can not only directly measure neuronal activity, such as spiking responses or membrane potential dynamics, but also quantify synaptic inputs from excitatory and inhibitory circuits in living animals. This approach enables researchers to directly unravel different synaptic components and to understand their underlying roles in particular brain functions. Combining in vivo patch-clamp recording with other techniques, such as two-photon imaging or optogenetics, can provide even clearer functional dissection of the synaptic contributions of different neurons or nuclei. Here, we summarized current applications and recent research progress using the in vivo patch-clamp recording method and focused on its role in the functional dissection of different synaptic inputs. The key factors of a successful in vivo patch-clamp experiment and possible solutions based on references and our experiences were also discussed.

Ocular dominance plasticity disrupts binocular inhibition-excitation matching in visual cortex

BACKGROUND

To ensure that neuronal networks function in a stable fashion, neurons receive balanced inhibitory and excitatory inputs. In various brain regions, this balance has been found to change temporarily during plasticity. Whether changes in inhibition have an instructive or permissive role in plasticity remains unclear. Several studies have addressed this question using ocular dominance plasticity in the visual cortex as a model, but so far, it remains controversial whether changes in inhibition drive this form of plasticity by directly affecting eye-specific responses or through increasing the plasticity potential of excitatory connections.

RESULTS

We tested how three major classes of interneurons affect eye-specific responses in normally reared or monocularly deprived mice by optogenetically suppressing their activity. We find that in contrast to somatostatin-expressing or vasoactive intestinal polypeptide-expressing interneurons, parvalbumin (PV)-expressing interneurons strongly inhibit visual responses. In individual neurons of normal mice, inhibition and excitation driven by either eye are balanced, and suppressing PV interneurons does not alter ocular preference. Monocular deprivation disrupts the binocular balance of inhibition and excitation in individual neurons, causing suppression of PV interneurons to change their ocular preference. Importantly, however, these changes do not consistently favor responses to one of the eyes at the population level.

CONCLUSIONS

Monocular deprivation disrupts the binocular balance of inhibition and excitation of individual cells. This disbalance does not affect the overall expression of ocular dominance. Our data therefore support a permissive rather than an instructive role of inhibition in ocular dominance plasticity.

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.

A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.

Inhibitory interneuron classes express complementary AMPA-receptor patterns in macaque primary visual cortex

Glutamate receptors mediate excitatory neurotransmission. A very prevalent type of glutamate receptor in the neocortex is the AMPA receptor (AMPAR). AMPARs mediate fast synaptic transmission and their functionality depends on the subunit composition. In primary visual cortex (area V1), the density and subunit composition of AMPARs differ among cortical layers and among cell types. The AMPARs expressed by the different types of inhibitory interneurons, which are crucial for network function, have not yet been characterized systematically. We investigated the distribution of AMPAR subunits in macaque V1 for three distinct subpopulations of inhibitory interneurons: parvalbumin-immunoreactive (PV-IR) interneurons, calbindin-immunoreactive (CB-IR) interneurons, and calretinin-immunoreactive (CR-IR) interneurons. We found that PV-IR cells, which have previously been identified as fast spiking, show high expression of the GluA2 and GluA3 subunits. In contrast, CB-IR and CR-IR cells, which tend to be intermediate spiking, show high expression of the GluA1 and GluA4 subunits. Thus, our data demonstrate that the expression of AMPARs divides inhibitory interneurons in macaque V1 into two categories that are compatible with existing classification methods based on calcium-binding proteins and firing behavior. Moreover, our findings suggest new approaches to target the different inhibitory interneuron classes pharmacologically in vivo.

Cell directional spread determines accuracy, precision, and length of the neuronal population vector

The neuronal population vector (NPV) for movement direction is the sum of weighted neuronal directional contributions. Based on theoretical considerations, we proposed recently that the sharpness of tuning will impact the directional precision, accuracy, and length of the NPV, such that sharper tuning will yield NPV with higher precision, higher accuracy, and shorter length (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013). Furthermore, we proposed that controlling the inhibitory drive in a local network could be the mechanism by which the sharpness of directional tuning would be varied, resulting in a continuous specification and control of movement's directional precision, accuracy, and speed (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013, Fig. 5). As a first step in testing this idea, here we analyzed data from 899 cells recorded in the motor cortex during performance of a center → out task. There were two major findings. First, directional selectivity varied with cell activity, such that it was higher in cells with lower mean discharge rates. And second, NPVs calculated from subsets of cells with higher directional selectivity (and, correspondingly, lower mean discharge rates) were more accurate (i.e., closer to the movement), precise (i.e., less variable), and shorter (i.e., slower; Schwartz in Science 265:540-542, 1994). These findings confirm our predictions above made from modeling (Mahan and Georgopoulos in Front Neural Circuits 7:92, 2013) and provide a simple mechanism by which desired attributes of the directional motor command can be implemented. We hypothesize that the inhibitory drive in a local network is controlled directly and independently of recurrent collaterals or common excitatory inputs to other cells. This could be achieved by a private excitation/inhibition of key inhibitory interneurons in a way similar to that in operation for Renshaw cells in the spinal cord. The presence of such a private line of inhibitory control remains to be investigated.

Binocular input coincidence mediates critical period plasticity in the mouse primary visual cortex

Classical studies on the development of ocular dominance (OD) organization in primary visual cortex (V1) have revealed a postnatal critical period (CP), during which visual inputs between the two eyes are most effective in shaping cortical circuits through synaptic competition. A brief closure of one eye during CP caused a pronounced shift of response preference of V1 neurons toward the open eye, a form of CP plasticity in the developing V1. However, it remains unclear what particular property of binocular inputs during CP is responsible for mediating this experience-dependent OD plasticity. Using whole-cell recording in mouse V1, we found that visually driven synaptic inputs from the two eyes to binocular cells in layers 2/3 and 4 became highly coincident during CP. Enhancing cortical GABAergic transmission activity by brain infusion with diazepam not only caused a precocious onset of the high coincidence of binocular inputs and OD plasticity in pre-CP mice, but rescued both of them in dark-reared mice, suggesting a tight link between coincident binocular inputs and CP plasticity. In Thy1-ChR2 mice, chronic disruption of this binocular input coincidence during CP by asynchronous optogenetic activation of retinal ganglion cells abolished the OD plasticity. Computational simulation using a feed-forward network model further suggests that the coincident inputs could mediate this CP plasticity through a homeostatic synaptic learning mechanism with synaptic competition. These results suggest that the high-level correlation of binocular inputs is a hallmark of the CP of developing V1 and serves as neural substrate for the induction of OD plasticity.

GABAergic synapses: their plasticity and role in sensory cortex

The mammalian neocortex is composed of a variety of cell types organized in a highly interconnected circuit. GABAergic neurons account for only about 20% of cortical neurons. However, they show widespread connectivity and a high degree of diversity in morphology, location, electrophysiological properties and gene expression. In addition, distinct populations of inhibitory neurons have different sensory response properties, capacities for plasticity and sensitivities to changes in sensory experience. In this review we summarize experimental evidence regarding the properties of GABAergic neurons in primary sensory cortex. We will discuss how distinct GABAergic neurons and different forms of GABAergic inhibitory plasticity may contribute to shaping sensory cortical circuit activity and function.

Strengthening of Direction Selectivity by Broadly Tuned and Spatiotemporally Slightly Offset Inhibition in Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. How the integration of synaptic excitation and inhibition contributes to DS however remains not well-understood. Here, in vivo whole-cell voltage-clamp recordings in mouse primary visual cortex (V1) revealed that layer 4 simple cells received direction-tuned excitatory inputs but barely tuned inhibitory inputs under drifting-bar stimulation. Excitation and inhibition exhibited differential temporal offsets under movements of opposite directions: excitation peaked earlier than inhibition at the preferred direction, and vice versa at the null direction. This could be attributed to a small spatial mismatch between overlapping excitatory and inhibitory receptive fields: the distribution of excitatory input strengths was skewed and the skewness was strongly correlated with the DS of excitatory input, whereas that of inhibitory input strengths was spatially symmetric. Neural modeling revealed that the relatively stronger inhibition under null directional movements, as well as the specific spatial-temporal offsets between excitation and inhibition, allowed inhibition to enhance the DS of output responses by suppressing the null response more effectively than the preferred response. Our data demonstrate that while tuned excitatory input provides the basis for DS in mouse V1, the largely untuned and spatiotemporally offset inhibition contributes importantly to sharpening of DS.

Mean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons

Mechanisms underlying the emergence of orientation selectivity in the primary visual cortex are highly debated. Here we study the contribution of inhibition-dominated random recurrent networks to orientation selectivity, and more generally to sensory processing. By simulating and analyzing large-scale networks of spiking neurons, we investigate tuning amplification and contrast invariance of orientation selectivity in these networks. In particular, we show how selective attenuation of the common mode and amplification of the modulation component take place in these networks. Selective attenuation of the baseline, which is governed by the exceptional eigenvalue of the connectivity matrix, removes the unspecific, redundant signal component and ensures the invariance of selectivity across different contrasts. Selective amplification of modulation, which is governed by the operating regime of the network and depends on the strength of coupling, amplifies the informative signal component and thus increases the signal-to-noise ratio. Here, we perform a mean-field analysis which accounts for this process.

Differential Receptive Field Properties of Parvalbumin and Somatostatin Inhibitory Neurons in Mouse Auditory Cortex

Cortical inhibitory circuits play important roles in shaping sensory processing. In auditory cortex, however, functional properties of genetically identified inhibitory neurons are poorly characterized. By two-photon imaging-guided recordings, we specifically targeted 2 major types of cortical inhibitory neuron, parvalbumin (PV) and somatostatin (SOM) expressing neurons, in superficial layers of mouse auditory cortex. We found that PV cells exhibited broader tonal receptive fields with lower intensity thresholds and stronger tone-evoked spike responses compared with SOM neurons. The latter exhibited similar frequency selectivity as excitatory neurons. The broader/weaker frequency tuning of PV neurons was attributed to a broader range of synaptic inputs and stronger subthreshold responses elicited, which resulted in a higher efficiency in the conversion of input to output. In addition, onsets of both the input and spike responses of SOM neurons were significantly delayed compared with PV and excitatory cells. Our results suggest that PV and SOM neurons engage in auditory cortical circuits in different manners: while PV neurons may provide broadly tuned feedforward inhibition for a rapid control of ascending inputs to excitatory neurons, the delayed and more selective inhibition from SOM neurons may provide a specific modulation of feedback inputs on their distal dendrites.

Adrenergic gating of Hebbian spike-timing-dependent plasticity in cortical interneurons

In pyramidal cells, the induction of spike-dependent plasticity (STDP) follows a simple Hebbian rule in which the order of presynaptic and postsynaptic firing dictates the induction of LTP or LTD. In contrast, cortical fast spiking (FS) interneurons, which control the rate and timing of pyramidal cell firing, reportedly express timing-dependent LTD, but not timing-dependent LTP. Because a mismatch in STDP rules could impact the maintenance of the excitation/inhibition balance, we examined the neuromodulation of STDP in FS cells of mouse visual cortex. We found that stimulation of adrenergic receptors enables the induction of Hebbian bidirectional STDP in FS cells in a manner consistent with a pull-push mechanism previously characterized in pyramidal cells. However, in pyramidal cells, STDP induction depends on NMDA receptors, whereas in FS cells it depends on mGluR5 receptors. We propose that neuromodulators control the polarity of STDP in different synapses in the same manner, and independently of the induction mechanism, by acting downstream in the plasticity cascade. By doing so, neuromodulators may allow coordinated plastic changes in FS and pyramidal cells.

Orientation-selective responses in the mouse lateral geniculate nucleus

The dorsal lateral geniculate nucleus (dLGN) receives visual information from the retina and transmits it to the cortex. In this study, we made extracellular recordings in the dLGN of both anesthetized and awake mice, and found that a surprisingly high proportion of cells were selective for stimulus orientation. The orientation selectivity of dLGN cells was unchanged after silencing the visual cortex pharmacologically, indicating that it is not due to cortical feedback. The orientation tuning of some dLGN cells correlated with their elongated receptive fields, while in others orientation selectivity was observed despite the fact that their receptive fields were circular, suggesting that their retinal input might already be orientation selective. Consistently, we revealed orientation/axis-selective ganglion cells in the mouse retina using multielectrode arrays in an in vitro preparation. Furthermore, the orientation tuning of dLGN cells was largely maintained at different stimulus contrasts, which could be sufficiently explained by a simple linear feedforward model. We also compared the degree of orientation selectivity in different visual structures under the same recording condition. Compared with the dLGN, orientation selectivity is greatly improved in the visual cortex, but is similar in the superior colliculus, another major retinal target. Together, our results demonstrate prominent orientation selectivity in the mouse dLGN, which may potentially contribute to visual processing in the cortex.

Tracing inputs to inhibitory or excitatory neurons of mouse and cat visual cortex with a targeted rabies virus

BACKGROUND

Cortical inhibition plays a critical role in controlling and modulating cortical excitation, and a more detailed understanding of the neuronal circuits contributing to each will provide more insight into their roles in complex cortical computations. Traditional neuronal tracers lack a means for easily distinguishing between circuits of inhibitory and excitatory neurons. To overcome this limitation, we have developed a technique for retrogradely labeling inputs to local clusters of inhibitory or excitatory neurons, but not both, using neurotropic adenoassociated and lentiviral vectors, cell-type-specific promoters, and a modified rabies virus.

RESULTS

Applied to primary visual cortex (V1) in mouse, the cell-type-specific tracing technique labeled thousands of presynaptically connected neurons and revealed that the dominant source of input to inhibitory and excitatory neurons is local in origin. Neurons in other visual areas are also labeled; the percentage of these intercortical inputs to excitatory neurons is somewhat higher (~20%) than to inhibitory neurons (<10%), suggesting that intercortical connections have less direct control over inhibition. The inputs to inhibitory neurons were also traced in cat V1, and when aligned with the orientation preference map revealed for the first time that long-range inputs to inhibitory neurons are well tuned to orientation.

CONCLUSIONS

These novel findings for inhibitory and excitatory circuits in the visual cortex demonstrate the efficacy of our new technique and its ability to work across species, including larger-brained mammals such as the cat. This paves the way for a better understanding of the roles of specific cell types in higher-order perceptual and cognitive processes.