Showing papers on "Receptive field published in 2013"

The emergence of functional microcircuits in visual cortex

Abstract: A study of mouse visual cortex relating patterns of excitatory synaptic connectivity to visual response properties of neighbouring neurons shows that, after eye opening, local connectivity reorganizes extensively: more connections form selectively between neurons with similar visual responses and connections are eliminated between visually unresponsive neurons, but the overall connectivity rate does not change. Intrinsic and experiential factors guide the patterning of neural pathways and the establishment of sensory response properties during postnatal development. Although sensory processing is known to depend on the precise wiring of cortical microcircuits, how this functional connectivity develops remains unclear. Based on electrical recordings of neighbouring neurons and changing network dynamics measures using calcium imaging, Thomas Mrsic-Flogel and colleagues offer a proposal that neuronal feature preference is initially acquired before sensory experience in a feed-forward manner, and with patterned input later driving the formation of precision within the network following the appropriate re-arrangement of synaptic connections. Sensory processing occurs in neocortical microcircuits in which synaptic connectivity is highly structured1,2,3,4 and excitatory neurons form subnetworks that process related sensory information5,6. However, the developmental mechanisms underlying the formation of functionally organized connectivity in cortical microcircuits remain unknown. Here we directly relate patterns of excitatory synaptic connectivity to visual response properties of neighbouring layer 2/3 pyramidal neurons in mouse visual cortex at different postnatal ages, using two-photon calcium imaging in vivo and multiple whole-cell recordings in vitro. Although neural responses were already highly selective for visual stimuli at eye opening, neurons responding to similar visual features were not yet preferentially connected, indicating that the emergence of feature selectivity does not depend on the precise arrangement of local synaptic connections. After eye opening, local connectivity reorganized extensively: more connections formed selectively between neurons with similar visual responses and connections were eliminated between visually unresponsive neurons, but the overall connectivity rate did not change. We propose a sequential model of cortical microcircuit development based on activity-dependent mechanisms of plasticity whereby neurons first acquire feature preference by selecting feedforward inputs before the onset of sensory experience—a process that may be facilitated by early electrical coupling between neuronal subsets7,8,9—and then patterned input drives the formation of functional subnetworks through a redistribution of recurrent synaptic connections.

Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo

Abstract: Neuronal dendrites are electrically excitable: they can generate regenerative events such as dendritic spikes in response to sufficiently strong synaptic input. Although such events have been observed in many neuronal types, it is not well understood how active dendrites contribute to the tuning of neuronal output in vivo. Here we show that dendritic spikes increase the selectivity of neuronal responses to the orientation of a visual stimulus (orientation tuning). We performed direct patch-clamp recordings from the dendrites of pyramidal neurons in the primary visual cortex of lightly anaesthetized and awake mice, during sensory processing. Visual stimulation triggered regenerative local dendritic spikes that were distinct from back-propagating action potentials. These events were orientation tuned and were suppressed by either hyperpolarization of membrane potential or intracellular blockade of NMDA (N-methyl-d-aspartate) receptors. Both of these manipulations also decreased the selectivity of subthreshold orientation tuning measured at the soma, thus linking dendritic regenerative events to somatic orientation tuning. Together, our results suggest that dendritic spikes that are triggered by visual input contribute to a fundamental cortical computation: enhancing orientation selectivity in the visual cortex. Thus, dendritic excitability is an essential component of behaviourally relevant computations in neurons.

Diverse Visual Features Encoded in Mouse Lateral Geniculate Nucleus

Abstract: The thalamus is crucial in determining the sensory information conveyed to cortex. In the visual system, the thalamic lateral geniculate nucleus (LGN) is generally thought to encode simple center-surround receptive fields, which are combined into more sophisticated features in cortex, such as orientation and direction selectivity. However, recent evidence suggests that a more diverse set of retinal ganglion cells projects to the LGN. We therefore used multisite extracellular recordings to define the repertoire of visual features represented in the LGN of mouse, an emerging model for visual processing. In addition to center-surround cells, we discovered a substantial population with more selective coding properties, including direction and orientation selectivity, as well as neurons that signal absence of contrast in a visual scene. The direction and orientation selective neurons were enriched in regions that match the termination zones of direction selective ganglion cells from the retina, suggesting a source for their tuning. Together, these data demonstrate that the mouse LGN contains a far more elaborate representation of the visual scene than current models posit. These findings should therefore have a significant impact on our understanding of the computations performed in mouse visual cortex.

Tuned thalamic excitation is amplified by visual cortical circuits

Abstract: Cortical neurons in thalamic recipient layers receive excitation from the thalamus and the cortex. The relative contribution of these two sources of excitation to sensory tuning is poorly understood. We optogenetically silenced the visual cortex of mice to isolate thalamic excitation onto layer 4 neurons during visual stimulation. Thalamic excitation contributed to a third of the total excitation and was organized in spatially offset, yet overlapping, ON and OFF receptive fields. This receptive field structure predicted the orientation tuning of thalamic excitation. Finally, both thalamic and total excitation were similarly tuned to orientation and direction and had the same temporal phase relationship to the visual stimulus. Our results indicate that tuning of thalamic excitation is unlikely to be imparted by direction- or orientation-selective thalamic neurons and that a principal role of cortical circuits is to amplify tuned thalamic excitation.

Feature detection and orientation tuning in the Drosophila central complex

Abstract: Many animals, including insects, are known to use visual landmarks to orient in their environment. In Drosophila melanogaster, behavioural genetics studies have identified a higher brain structure called the central complex as being required for the fly's innate responses to vertical visual features and its short- and long-term memory for visual patterns. But whether and how neurons of the fly central complex represent visual features are unknown. Here we use two-photon calcium imaging in head-fixed walking and flying flies to probe visuomotor responses of ring neurons--a class of central complex neurons that have been implicated in landmark-driven spatial memory in walking flies and memory for visual patterns in tethered flying flies. We show that dendrites of ring neurons are visually responsive and arranged retinotopically. Ring neuron receptive fields comprise both excitatory and inhibitory subfields, resembling those of simple cells in the mammalian primary visual cortex. Ring neurons show strong and, in some cases, direction-selective orientation tuning, with a notable preference for vertically oriented features similar to those that evoke innate responses in flies. Visual responses were diminished during flight, but, in contrast with the hypothesized role of the central complex in the control of locomotion, not modulated during walking. Taken together, these results indicate that ring neurons represent behaviourally relevant visual features in the fly's environment, enabling downstream central complex circuits to produce appropriate motor commands. More broadly, this study opens the door to mechanistic investigations of circuit computations underlying visually guided action selection in the Drosophila central complex.

Genetic identification of C fibres that detect massage-like stroking of hairy skin in vivo

Abstract: Stroking of the skin produces pleasant sensations that can occur during social interactions with conspecifics, such as grooming Despite numerous physiological studies (reviewed in ref 2), molecularly defined sensory neurons that detect pleasant stroking of hairy skin in vivo have not been reported Previously, we identified a rare population of unmyelinated sensory neurons in mice that express the G-protein-coupled receptor MRGPRB4 These neurons exclusively innervate hairy skin with large terminal arborizations that resemble the receptive fields of C-tactile (CT) afferents in humans Unlike other molecularly defined mechanosensory C-fibre subtypes, MRGPRB4^+ neurons could not be detectably activated by sensory stimulation of the skin ex vivo Therefore, we developed a preparation for calcium imaging in the spinal projections of these neurons during stimulation of the periphery in intact mice Here we show that MRGPRB4^+ neurons are activated by massage-like stroking of hairy skin, but not by noxious punctate mechanical stimulation By contrast, a different population of C fibres expressing MRGPRD was activated by pinching but not by stroking, consistent with previous physiological and behavioural data Pharmacogenetic activation of Mrgprb4-expressing neurons in freely behaving mice promoted conditioned place preference, indicating that such activation is positively reinforcing and/or anxiolytic These data open the way to understanding the function of MRGPRB4 neurons during natural behaviours, and provide a general approach to the functional characterization of genetically identified subsets of somatosensory neurons in vivo

Parvalbumin-Expressing Inhibitory Interneurons in Auditory Cortex Are Well-Tuned for Frequency

Abstract: In the auditory cortex, synaptic inhibition is known to be involved in shaping receptive fields, enhancing temporal precision, and regulating gain. Cortical inhibition is provided by local GABAergic interneurons, which comprise 10–20% of the cortical population and can be separated into numerous subclasses. The morphological and physiological diversity of interneurons suggests that these different subclasses have unique roles in sound processing; however, these roles are yet unknown. Understanding the receptive field properties of distinct inhibitory cell types will be critical to elucidating their computational function in cortical circuits. Here we characterized the tuning and response properties of parvalbumin-positive (PV+) interneurons, the largest inhibitory subclass. We used channelrhodopsin-2 (ChR2) as an optogenetic tag to identify PV+ and PV− neurons in vivo in transgenic mice. In contrast to PV+ neurons in mouse visual cortex, which are broadly tuned for orientation, we found that auditory cortical PV+ neurons were well tuned for frequency, although very tightly tuned PV+ cells were uncommon. This suggests that PV+ neurons play a minor role in shaping frequency tuning, and is consistent with the idea that PV+ neurons nonselectively pool input from the local network. PV+ interneurons had shallower response gain and were less intensity-tuned than PV− neurons, suggesting that PV+ neurons provide dynamic gain control and shape intensity tuning in auditory cortex. PV+ neurons also had markedly faster response latencies than PV− neurons, consistent with a computational role in enhancing the temporal precision of cortical responses.

Long-term modification of cortical synapses improves sensory perception

Abstract: Synapses and receptive fields of the cerebral cortex are plastic. However, changes to specific inputs must be coordinated within neural networks to ensure that excitability and feature selectivity are appropriately configured for perception of the sensory environment. We induced long-lasting enhancements and decrements to excitatory synaptic strength in rat primary auditory cortex by pairing acoustic stimuli with activation of the nucleus basalis neuromodulatory system. Here we report that these synaptic modifications were approximately balanced across individual receptive fields, conserving mean excitation while reducing overall response variability. Decreased response variability should increase detection and recognition of near-threshold or previously imperceptible stimuli. We confirmed both of these hypotheses in behaving animals. Thus, modification of cortical inputs leads to wide-scale synaptic changes, which are related to improved sensory perception and enhanced behavioral performance.

Inferring nonlinear neuronal computation based on physiologically plausible inputs.

Abstract: The computation represented by a sensory neuron's response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron's inputs. Incorporating such ‘upstream nonlinearities’ within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron's response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.

Two-Photon Imaging of Nonlinear Glutamate Release Dynamics at Bipolar Cell Synapses in the Mouse Retina

Abstract: Alpha/Y-type retinal ganglion cells encode visual information with a receptive field composed of nonlinear subunits. This nonlinear subunit structure enhances sensitivity to patterns composed of high spatial frequencies. The Y-cell's subunits are the presynaptic bipolar cells, but the mechanism for the nonlinearity remains incompletely understood. We investigated the synaptic basis of the subunit nonlinearity by combining whole-cell recording of mouse Y-type ganglion cells with two-photon fluorescence imaging of a glutamate sensor (iGluSnFR) expressed on their dendrites and throughout the inner plexiform layer. A control experiment designed to assess iGluSnFR's dynamic range showed that fluorescence responses from Y-cell dendrites increased proportionally with simultaneously recorded excitatory current. Spatial resolution was sufficient to readily resolve independent release at intermingled ON and OFF bipolar terminals. iGluSnFR responses at Y-cell dendrites showed strong surround inhibition, reflecting receptive field properties of presynaptic release sites. Responses to spatial patterns located the origin of the Y-cell nonlinearity to the bipolar cell output, after the stage of spatial integration. The underlying mechanism differed between OFF and ON pathways: OFF synapses showed transient release and strong rectification, whereas ON synapses showed relatively sustained release and weak rectification. At ON synapses, the combination of fast release onset with slower release offset explained the nonlinear response of the postsynaptic ganglion cell. Imaging throughout the inner plexiform layer, we found transient, rectified release at the central-most levels, with increasingly sustained release near the borders. By visualizing glutamate release in real time, iGluSnFR provides a powerful tool for characterizing glutamate synapses in intact neural circuits.

Corticocortical Feedback Contributes to Surround Suppression in V1 of the Alert Primate

Abstract: Feedback connections are prevalent throughout the cerebral cortex, yet their function remains poorly understood. Previous studies in anesthetized monkeys found that inactivating feedback from extrastriate visual cortex produced effects in striate cortex that were relatively weak, generally suppressive, largest for visual stimuli confined to the receptive field center, and detectable only at low stimulus contrast. We studied the influence of corticocortical feedback in alert monkeys using cortical cooling to reversibly inactivate visual areas 2 (V2) and 3 (V3) while characterizing receptive field properties in primary visual cortex (V1). We show that inactivation of feedback from areas V2 and V3 results in both response suppression and facilitation for stimuli restricted to the receptive field center, in most cases leading to a small reduction in the degree of orientation selectivity but no change in orientation preference. For larger-diameter stimuli that engage regions beyond the center of the receptive field, eliminating feedback from V2 and V3 results in strong and consistent response facilitation, effectively reducing the strength of surround suppression in V1 for stimuli of both low and high contrast. For extended contours, eliminating feedback had the effect of reducing end stopping. Inactivation effects were largest for neurons that exhibited strong surround suppression before inactivation, and their timing matched the dynamics of surround suppression under control conditions. Our results provide direct evidence that feedback contributes to surround suppression, which is an important source of contextual influences essential to vision.

Linear transformation of thalamocortical input by intracortical excitation.

Abstract: Neurons in thalamorecipient layers of sensory cortices integrate thalamocortical and intracortical inputs. Although we know that their functional properties can arise from the convergence of thalamic inputs, intracortical circuits could also be involved in thalamocortical transformations of sensory information. We silenced intracortical excitatory circuits with optogenetic activation of parvalbumin-positive inhibitory neurons in mouse primary visual cortex and compared visually evoked thalamocortical input with total excitation in the same layer 4 pyramidal neurons. We found that intracortical excitatory circuits preserved the orientation and direction tuning of thalamocortical excitation, with a linear amplification of thalamocortical signals of about threefold. The spatial receptive field of thalamocortical input was slightly elongated and was expanded by intracortical excitation in an approximately proportional manner. Thus, intracortical excitatory circuits faithfully reinforce the representation of thalamocortical information and may influence the size of the receptive field by recruiting additional inputs.

Emergence of orientation selectivity in the mammalian visual pathway

Abstract: Orientation selectivity is a property of mammalian primary visual cortex (V1) neurons, yet its emergence along the visual pathway varies across species. In carnivores and primates, elongated receptive fields first appear in V1, whereas in lagomorphs such receptive fields emerge earlier, in the retina. Here we examine the mouse visual pathway and reveal the existence of orientation selectivity in lateral geniculate nucleus (LGN) relay cells. Cortical inactivation does not reduce this orientation selectivity, indicating that cortical feedback is not its source. Orientation selectivity is similar for LGN relay cells spiking and subthreshold input to V1 neurons, suggesting that cortical orientation selectivity is inherited from the LGN in mouse. In contrast, orientation selectivity of cat LGN relay cells is small relative to subthreshold inputs onto V1 simple cells. Together, these differences show that although orientation selectivity exists in visual neurons of both rodents and carnivores, its emergence along the visual pathway, and thus its underlying neuronal circuitry, is fundamentally different.

Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.

Abstract: Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.

Orientation-selective Responses in the Mouse Lateral Geniculate Nucleus

Abstract: 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.

A computational theory of visual receptive fields

Abstract: A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space---time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space---time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space---time tilted receptive fields in V1, all within the same unified theory In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms

Cortical-Like Receptive Fields in the Lateral Geniculate Nucleus of Marmoset Monkeys

Abstract: Most neurons in primary visual cortex (V1) exhibit high selectivity for the orientation of visual stimuli. In contrast, neurons in the main thalamic input to V1, the lateral geniculate nucleus (LGN), are considered to be only weakly orientation selective. Here we characterize a sparse population of cells in marmoset LGN that show orientation and spatial frequency selectivity as great as that of cells in V1. The recording position in LGN and histological reconstruction of these cells shows that they are part of the koniocellular (K) pathways. Accordingly we have named them K-o (“koniocellular-orientation”) cells. Most K-o cells prefer vertically oriented gratings; their contrast sensitivity and TF tuning are similar to those of parvocellular cells, and they receive negligible functional input from short wavelength-sensitive (“blue”) cone photoreceptors. Four K-o cells tested displayed binocular responses. Our results provide further evidence that in primates as in nonprimate mammals the cortical input streams include a diversity of visual representations. The presence of K-o cells increases functional homologies between K pathways in primates and “sluggish/W” pathways in nonprimate visual systems.

A two-stage cascade model of BOLD responses in human visual cortex.

Abstract: Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.

Computational Identification of Receptive Fields

Abstract: Natural stimuli elicit robust responses of neurons throughout sensory pathways, and therefore their use provides unique opportunities for understanding sensory coding. This review describes statistical methods that can be used to characterize neural feature selectivity, focusing on the case of natural stimuli. First, we discuss how such classic methods as reverse correlation/spike-triggered average and spike-triggered covariance can be generalized for use with natural stimuli to find the multiple relevant stimulus features that affect the responses of a given neuron. Second, ways to characterize neural feature selectivity while assuming that the neural responses exhibit a certain type of invariance, such as position invariance for visual neurons, are discussed. Finally, we discuss methods that do not require one to make an assumption of invariance and instead can determine the type of invariance by analyzing relationships between the multiple stimulus features that affect the neural responses.

Spatial Integration in Mouse Primary Visual Cortex

Abstract: Responses of many neurons in primary visual cortex (V1) are suppressed by stimuli exceeding the classical receptive field (RF), an important property that might underlie the computation of visual saliency. Traditionally, it has proven difficult to disentangle the underlying neural circuits, including feedforward, horizontal intracortical, and feedback connectivity. Since circuit-level analysis is particularly feasible in the mouse, we asked whether neural signatures of spatial integration in mouse V1 are similar to those of higher-order mammals and investigated the role of parvalbumin-expressing (PV+) inhibitory interneurons. Analogous to what is known from primates and carnivores, we demonstrate that, in awake mice, surround suppression is present in the majority of V1 neurons and is strongest in superficial cortical layers. Anesthesia with isoflurane-urethane, however, profoundly affects spatial integration: it reduces the laminar dependency, decreases overall suppression strength, and alters the temporal dynamics of responses. We show that these effects of brain state can be parsimoniously explained by assuming that anesthesia affects contrast normalization. Hence, the full impact of suppressive influences in mouse V1 cannot be studied under anesthesia with isoflurane-urethane. To assess the neural circuits of spatial integration, we targeted PV+ interneurons using optogenetics. Optogenetic depolarization of PV+ interneurons was associated with increased RF size and decreased suppression in the recorded population, similar to effects of lowering stimulus contrast, suggesting that PV+ interneurons contribute to spatial integration by affecting overall stimulus drive. We conclude that the mouse is a promising model for circuit-level mechanisms of spatial integration, which relies on the combined activity of different types of inhibitory interneurons.

Different Orientation Tuning of Near- and Far-Surround Suppression in Macaque Primary Visual Cortex Mirrors Their Tuning in Human Perception

Abstract: In primary visual cortex (V1), neuronal responses to stimuli inside the receptive field (RF) are usually suppressed by stimuli in the RF surround. This suppression is orientation specific. Similarly, in human vision surround stimuli can suppress perceived contrast of a central stimulus in an orientation-dependent manner. The surround consists of two regions likely generated by different circuits: a near-surround generated predominantly by geniculocortical and intra-V1 horizontal connections, and a far-surround generated exclusively by interareal feedback. Using stimuli confined to the near- or far-surround of V1 neurons, and similar stimuli in human psychophysics, we find that near-surround suppression is more sharply orientation tuned than far-surround suppression in both macaque V1 and human perception. These results point to a similarity between surround suppression in macaque V1 and human vision, and suggest that feedback circuits are less orientation biased than horizontal circuits. We find the sharpest tuning of near-surround suppression in V1 layers (3, 4B, 4Cα) with patterned and orientation-specific horizontal connections. Sharpest tuning of far-surround suppression occurs in layer 4B, suggesting greater orientation specificity of feedback to this layer. Different orientation tuning of near- and far-surround suppression may reflect a statistical bias in natural images, whereby nearby edges have higher probability than distant edges of being co-oriented and belonging to the same contour. Surround suppression would, thus, increase the coding efficiency of frequently co-occurring contours and the saliency of less frequent ones. Such saliency increase can help detect small orientation differences in nearby edges (for contour completion), but large orientation differences in distant edges (for directing saccades/attention).

Contrast Dependence and Differential Contributions from Somatostatin- and Parvalbumin-Expressing Neurons to Spatial Integration in Mouse V1

Abstract: A characteristic feature in the primary visual cortex is that visual responses are suppressed as a stimulus extends beyond the classical receptive field. Here, we examined the role of inhibitory neurons expressing somatostatin (SOM+) or parvalbumin (PV+) on surround suppression and preferred receptive field size. We recorded multichannel extracellular activity in V1 of transgenic mice expressing channelrhodopsin in SOM+ neurons or PV+ neurons. Preferred size and surround suppression were measured using drifting square-wave gratings of varying radii and at two contrasts. Consistent with findings in primates, we found that the preferred size was larger for lower contrasts across all cortical depths, whereas the suppression index (SI) showed a trend to decrease with contrast. We then examined the effect of these metrics on units that were suppressed by photoactivation of either SOM+ or PV+ neurons. When activating SOM+ neurons, we found a significant increase in SI at cortical depths >400 μm, whereas activating PV+ neurons caused a trend toward lower SIs regardless of cortical depth. Conversely, activating PV+ neurons significantly increased preferred size across all cortical depths, similar to lowering contrast, whereas activating SOM+ neurons had no systematic effect on preferred size across all depths. These data suggest that SOM+ and PV+ neurons contribute differently to spatial integration. Our findings are compatible with the notion that SOM+ neurons mediate surround suppression, particularly in deeper cortex, whereas PV+ activation decreases the drive of the input to cortex and therefore resembles the effects on spatial integration of lowering contrast.

Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction

Abstract: Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360°. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.