Mouse V1 Neurons Research Articles

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates

A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF. This heterogeneity correlated with deviations from optimal Gabor filters and was consistent across cortical layers and recording modalities (calcium vs. spikes). In contrast, model-synthesized MEIs for macaque V1 neurons were predominantly Gabor like, consistent with previous studies. These findings suggest that complex spatial feature selectivity emerges earlier in the visual pathway in mice than in primates. This may provide a faster, though less general, method of extracting task-relevant information.

Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky.

Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.

Comprehensive software suite for functional analysis and synaptic input mapping of dendritic spines imaged in vivo.

Advances in genetically encoded sensors and two-photon imaging have unlocked functional imaging at the level of single dendritic spines. Synaptic activity can be measured in real time in awake animals. However, tools are needed to facilitate the analysis of the large datasets acquired by the approach. Commonly available software suites for imaging calcium transients in cell bodies are ill-suited for spine imaging as dendritic spines have structural characteristics distinct from those of the cell bodies. We present an automated tuning analysis tool (AUTOTUNE), which provides analysis routines specifically developed for the extraction and analysis of signals from subcellular compartments, including dendritic subregions and spines. Although the acquisition of in vivo functional synaptic imaging data is increasingly accessible, a hurdle remains in the computation-heavy analyses of the acquired data. The aim of this study is to overcome this barrier by offering a comprehensive software suite with a user-friendly interface for easy access to nonprogrammers. We demonstrate the utility and effectiveness of our software with demo analyses of dendritic imaging data acquired from layer 2/3 pyramidal neurons in mouse V1 in vivo. A user manual and demo datasets are also provided. AUTOTUNE provides a robust workflow for analyzing functional imaging data from neuronal dendrites. Features include source image registration, segmentation of regions-of-interest and detection of structural turnover, fluorescence transient extraction and smoothing, subtraction of signals from putative backpropagating action potentials, and stimulus and behavioral parameter response tuning analyses. AUTOTUNE is open-source and extendable for diverse functional synaptic imaging experiments. The ease of functional characterization of dendritic spine activity provided by our software can accelerate new functional studies that complement decades of morphological studies of dendrites, and further expand our understanding of neural circuits in health and in disease.

Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons

Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.

The neurons in mouse V1 show different degrees of spatial clustering

Higher mammals’ primary visual cortex exhibits columnar organization, where neurons with similar response preferences are clustered. In contrast, rodents are presumed to lack this fine-scale organization; their neurons appear to be randomly arranged, described as a salt-and-pepper map. However, recent studies suggested a weak but significant spatial clustering of tuning in the salt-and-pepper map, similar to columnar organization. Thus, the salt-and-pepper map possesses the characteristics of both columnar organization and random arrangement. This raises the question about whether this mixed organization is attributed to different types of neurons. Here, we examined the tuning of primary visual cortical neurons in awake mice with a two-photon calcium imaging dataset, which were released by Allen Institute MindScope Program. First, we demonstrated that neurons with similar response preferences were clustered by showing that neighboring neurons tended to have similar orientation and temporal frequency preferences. Then, we compared the clustering of tuning between simple cells and complex cells and found the clustering of tuning among simple cells was significantly more prominent than that among complex cells. Furthermore, the simple/complex cell classification correlated with the stability of neuronal response. Neurons with stable responses were arranged independent of their tuning similarity, whereas unstable neurons were clustered according to their tuning similarity. These findings might represent a balance between efficiency and robustness: relatively independent tuning among stable neurons represents visual information efficiently, whereas unstable neurons with similar response preferences are clustered to obtain a robust representation with population codes.

A Distinct Population of L6 Neurons in Mouse V1 Mediate Cross-Callosal Communication.

Through the corpus callosum, interhemispheric communication is mediated by callosal projection (CP) neurons. Using retrograde labeling, we identified a population of layer 6 (L6) excitatory neurons as the main conveyer of transcallosal information in the monocular zone of the mouse primary visual cortex (V1). Distinct from L6 corticothalamic (CT) population, V1 L6 CP neurons contribute to an extensive reciprocal network across multiple sensory cortices over two hemispheres. Receiving both local and long-range cortical inputs, they encode orientation, direction, and receptive field information, while are also highly spontaneous active. The spontaneous activity of L6 CP neurons exhibits complex relationships with brain states and stimulus presentation, distinct from the spontaneous activity patterns of the CT population. The anatomical and functional properties of these L6 CP neurons enable them to broadcast visual and nonvisual information across two hemispheres, and thus may play a role in regulating and coordinating brain-wide activity events.

Analysis of segmentation ontology reveals the similarities and differences in connectivity onto L2/3 neurons in mouse V1

Quantitatively comparing brain-wide connectivity of different types of neuron is of vital importance in understanding the function of the mammalian cortex. Here we have designed an analytical approach to examine and compare datasets from hierarchical segmentation ontologies, and applied it to long-range presynaptic connectivity onto excitatory and inhibitory neurons, mainly located in layer 2/3 (L2/3), of mouse primary visual cortex (V1). We find that the origins of long-range connections onto these two general cell classes—as well as their proportions—are quite similar, in contrast to the inputs on to a cell type in L6. These anatomical data suggest that distal inputs received by the general excitatory and inhibitory classes of neuron in L2/3 overlap considerably.

VIP interneurons in mouse primary visual cortex selectively enhance responses to weak but specific stimuli.

Vasoactive intestinal peptide-expressing (VIP) interneurons in the cortex regulate feedback inhibition of pyramidal neurons through suppression of somatostatin-expressing (SST) interneurons and, reciprocally, SST neurons inhibit VIP neurons. Although VIP neuron activity in the primary visual cortex (V1) of mouse is highly correlated with locomotion, the relevance of locomotion-related VIP neuron activity to visual coding is not known. Here we show that VIP neurons in mouse V1 respond strongly to low contrast front-to-back motion that is congruent with self-motion during locomotion but are suppressed by other directions and contrasts. VIP and SST neurons have complementary contrast tuning. Layer 2/3 contains a substantially larger population of low contrast preferring pyramidal neurons than deeper layers, and layer 2/3 (but not deeper layer) pyramidal neurons show bias for front-to-back motion specifically at low contrast. Network modeling indicates that VIP-SST mutual antagonism regulates the gain of the cortex to achieve sensitivity to specific weak stimuli without compromising network stability.

Integrative analysis of in vivo recording with single-cell RNA-seq data reveals molecular properties of light-sensitive neurons in mouse V1

Vision formation is classically based on projections from retinal ganglion cells (RGC) to the lateral geniculate nucleus (LGN) and the primary visual cortex (V1). Neurons in the mouse V1 are tuned to light stimuli. Although the cellular information of the retina and the LGN has been widely studied, the transcriptome profiles of single light-stimulated neuron in V1 remain unknown. In our study, in vivo calcium imaging and whole-cell electrophysiological patch-clamp recording were utilized to identify 53 individual cells from layer 2/3 of V1 as light-sensitive (LS) or non-light-sensitive (NS) by single-cell light-evoked calcium evaluation and action potential spiking. The contents of each cell after functional tests were aspirated in vivo through a patch-clamp pipette for mRNA sequencing. Moreover, the three-dimensional (3-D) morphological characterizations of the neurons were reconstructed in a live mouse after the whole-cell recordings. Our sequencing results indicated that V1 neurons with a high expression of genes related to transmission regulation, such as Rtn4r and Rgs7, and genes involved in membrane transport, such as Na+/K+ ATPase and NMDA-type glutamatergic receptors, preferentially responded to light stimulation. Furthermore, an antagonist that blocks Rtn4r signals could inactivate the neuronal responses to light stimulation in live mice. In conclusion, our findings of the vivo-seq analysis indicate the key role of the strength of synaptic transmission possesses neurons in V1 of light sensory.

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network

A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal responses. Here, by incorporating convolutional neural network (CNN) for encoding models of neurons in the visual cortex, we developed a new method of nonlinear response characterisation, especially nonlinear estimation of receptive fields (RFs), without assumptions regarding the type of nonlinearity. Briefly, after training CNN to predict the visual responses to natural images, we synthesised the RF image such that the image would predictively evoke a maximum response. We first demonstrated the proof-of-principle using a dataset of simulated cells with various types of nonlinearity. We could visualise RFs with various types of nonlinearity, such as shift-invariant RFs or rotation-invariant RFs, suggesting that the method may be applicable to neurons with complex nonlinearities in higher visual areas. Next, we applied the method to a dataset of neurons in mouse V1. We could visualise simple-cell-like or complex-cell-like (shift-invariant) RFs and quantify the degree of shift-invariance. These results suggest that CNN encoding model is useful in nonlinear response analyses of visual neurons and potentially of any sensory neurons.

Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex

SUMMARYThe connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map (such as rodents and lagomorphs) are poorly understood. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. The model predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing a shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole-cell recordings.

Visually Driven Neuropil Activity and Information Encoding in Mouse Primary Visual Cortex.

Cortical neuropil modulations recorded by calcium imaging reflect the activity of large aggregates of axo-dendritic processes and synaptic compartments from a large number of neurons. The organization of this activity impacts neuronal firing but is not well understood. Here we used in vivo 2-photon imaging with Oregon Green Bapta (OGB) and GCaMP6s to study neuropil visual responses to moving gratings in layer 2/3 of mouse area V1. We found neuropil responses to be strongly modulated and more reliable than neighboring somatic activity. Furthermore, stimulus independent modulations in neuropil activity, i.e., noise correlations, were highly coherent across the cortical surface, up to distances of at least 200 μm. Pairwise neuropil-to-neuropil-patch noise correlation strength was much higher than cell-to-cell noise correlation strength and depended strongly on brain state, decreasing in quiet wakefulness relative to light anesthesia. The profile of neuropil noise correlation strength decreased gently with distance, dropping by ~11% at a distance of 200 μm. This was comparatively slower than the profile of cell-to-cell noise correlations, which dropped by ~23% at 200 μm. Interestingly, in spite of the “salt & pepper” organization of orientation and direction encoding across mouse V1 neurons, populations of neuropil patches, even of moderately large size (radius ~100 μm), showed high accuracy for discriminating perpendicularly moving gratings. This was commensurate to the accuracy of corresponding cell populations. The dynamic, stimulus dependent, nature of neuropil activity further underscores the need to carefully separate neuropil from cell soma activity in contemporary imaging studies.

Binocular Disparity Selectivity Weakened after Monocular Deprivation in Mouse V1.

Experiences during the critical period sculpt the circuitry within the neocortex, leading to changes in the functional responses of sensory neurons. Monocular deprivation (MD) during the visual critical period causes shifts in ocular preference, or dominance, toward the open eye in primary visual cortex (V1) and disrupts the normal development of acuity. In carnivores and primates, MD also disrupts the emergence of binocular disparity selectivity, a cue resulting from integrating ocular inputs. This disruption may be a result of the increase in neurons driven exclusively by the open eye that follows deprivation or a result of a mismatch in the convergence of ocular inputs. To distinguish between these possibilities, we measured the ocular dominance (OD) and disparity selectivity of neurons from male and female mouse V1 following MD. Normal mouse V1 neurons are dominated by contralateral eye input and contralateral eye deprivation shifts mouse V1 neurons toward more balanced responses between the eyes. This shift toward binocularity, as assayed by OD, decreased disparity sensitivity. MD did not alter the initial maturation of binocularity, as disparity selectivity before the MD was indistinguishable from normal mature animals. Decreased disparity tuning was most pronounced in binocular and ipsilaterally biased neurons, which are the populations that have undergone the largest shifts in OD. In concert with the decline in disparity selectivity, we observed a shift toward lower spatial frequency selectivity for the ipsilateral eye following MD. These results suggest an emergence of novel synaptic inputs during MD that disrupt the representation of disparity selectivity.SIGNIFICANCE STATEMENT We demonstrate that monocular deprivation during the developmental critical period impairs binocular integration in mouse primary visual cortex. This impairment occurs despite an increase in the degree to which neurons become more binocular. We further demonstrate that our deprivation did not impair the maturation of disparity selectivity. Disparity selectivity has already reached a matured level before the monocular deprivation. The loss of disparity tuning is primarily observed in neurons dominated by the open eye, suggesting a link between altered inputs and loss of disparity sensitivity. These results suggest that new inputs following deprivation may not maintain the precise spatial relationship between the two eye inputs required for disparity selectivity.

Contrast adaptation is spatial frequency specific in mouse primary visual cortex

Contrast adaptation, generated by prolonged viewing of a high contrast spatial pattern, is known to reduce perceptual sensitivity to subsequently presented stimuli of similar spatial frequency (SF). Neural correlates of this pattern-specific contrast adaptation have been described in several classic studies in cat primary visual cortex (V1). These results have also recently been extended to mice, which is a genetically manipulable animal model. Here we attempt to parse the potential mechanisms contributing to this phenomenon by determining whether the SF specificity of contrast adaptation observed in mouse V1 neurons depends on the spike rate elicited by the adapting gratings. We found that adapting stimuli that drove a neuron more strongly generally produced more adaptation, implicating an intrinsic or fatigue-like process. Importantly, we also observed that slightly stronger contrast adaptation was produced when the adapting SF matched the test SF even when matched and nonmatched adapting gratings elicited similar spike rates indicating extrinsic or network processes contribute as well.

Target-specific properties of thalamocortical synapses onto layer 4 of mouse primary visual cortex.

In primary sensory cortices, thalamocortical (TC) inputs can directly activate excitatory and inhibitory neurons. In vivo experiments in the main input layer (L4) of primary visual cortex (V1) have shown that excitatory and inhibitory neurons have different tuning properties. The different functional properties may arise from distinct intrinsic properties of L4 neurons, but could also depend on cell type-specific properties of the synaptic inputs from the lateral geniculate nucleus of the thalamus (LGN) onto L4 neurons. While anatomical studies identified LGN inputs onto both excitatory and inhibitory neurons in V1, their synaptic properties have not been investigated. Here we used an optogenetic approach to selectively activate LGN terminal fields in acute coronal slices containing V1, and recorded monosynaptic currents from excitatory and inhibitory neurons in L4. LGN afferents made monosynaptic connections with pyramidal (Pyr) and fast-spiking (FS) neurons. TC EPSCs on FS neurons were larger and showed steeper short-term depression in response to repetitive stimulation than those on Pyr neurons. LGN inputs onto Pyr and FS neurons also differed in postsynaptic receptor composition and organization of presynaptic release sites. Together, our results demonstrate that LGN input onto L4 neurons in mouse V1 have target-specific presynaptic and postsynaptic properties. Distinct mechanisms of activation of feedforward excitatory and inhibitory neurons in the main input layer of V1 are likely to endow neurons with different response properties to incoming visual stimuli.

Pyramidal cells make specific connections onto smooth (GABAergic) neurons in mouse visual cortex.

Author SummaryThe mammalian visual cortex, which is part of the cerebral cortex, contains 50 to 100 thousands of neurons per cubic millimetre, most of which are excitatory (85%) and the minority, inhibitory (15%). Unlike neurons in the retina, neurons in the visual cortex are preferentially activated by lines or edges of a particular orientation. This is termed a neuron's “orientation preference.” In the visual cortex of higher mammals like cats and monkeys, neurons that share an orientation preference are clustered in functional columns. However, in rodents like mice, orientation preferences are randomly distributed. In this study, we investigate whether the differences between columnar and non-columnar cortex is correlated with differences in the connectivity patterns between excitatory and inhibitory neurons. Using light and electron microscopy, we mapped the connectivity of pyramidal neurons—the primary excitatory neurons—in the superficial layers of the primary visual cortex (V1) of mice. Our results show that the ratio of excitatory-inhibitory neurons in mouse V1 is similar to that of cat or monkey V1, but in mouse V1 local pyramidal neurons target proportionately many more inhibitory neurons compared to what other studies found in cat or monkey. This difference may indicate the significance of inhibition in maintaining orientation selectivity in the non-columnar visual cortex of rodents like mice and is a distinct difference in the architecture of V1 between mice and higher mammals.

Orientation-tuned surround suppression in mouse visual cortex.

The firing rates of neurons in primary visual cortex (V1) are suppressed by large stimuli, an effect known as surround suppression. In cats and monkeys, the strength of suppression is sensitive to orientation; responses to regions containing uniform orientations are more suppressed than those containing orientation contrast. This effect is thought to be important for scene segmentation, but the underlying neural mechanisms are poorly understood. We asked whether it is possible to study these mechanisms in the visual cortex of mice, because of recent advances in technology for studying the cortical circuitry in mice. It is unknown whether neurons in mouse V1 are sensitive to orientation contrast. We measured the orientation selectivity of surround suppression in the different layers of mouse V1. We found strong surround suppression in layer 4 and the superficial layers, part of which was orientation tuned: iso-oriented surrounds caused more suppression than cross-oriented surrounds. Surround suppression was delayed relative to the visual response and orientation-tuned suppression was delayed further, suggesting two separate suppressive mechanisms. Previous studies proposed that surround suppression depends on the activity of inhibitory somatostatin-positive interneurons in the superficial layers. To test the involvement of the superficial layers we topically applied lidocaine. Silencing of the superficial layers did not prevent orientation-tuned suppression in layer 4. These results show that neurons in mouse V1, which lacks orientation columns, show orientation-dependent surround suppression in layer 4 and the superficial layers and that surround suppression in layer 4 does not require contributions from neurons in the superficial layers.

Controlling Brain States

Neurons in mouse V1 increase their response to visual stimulation during locomotion. In this issue of Neuron, Lee et al. (2014) show that subthreshold optogenetic stimulation of a brainstem locomotion area can mimic the effect of locomotion on sensory processing.

Binocular integration and disparity selectivity in mouse primary visual cortex

Signals from the two eyes are first integrated in primary visual cortex (V1). In many mammals, this binocular integration is an important first step in the development of stereopsis, the perception of depth from disparity. Neurons in the binocular zone of mouse V1 receive inputs from both eyes, but it is unclear how that binocular information is integrated and whether this integration has a function similar to that found in other mammals. Using extracellular recordings, we demonstrate that mouse V1 neurons are tuned for binocular disparities, or spatial differences, between the inputs from each eye, thus extracting signals potentially useful for estimating depth. The disparities encoded by mouse V1 are significantly larger than those encoded by cat and primate. Interestingly, these larger disparities correspond to distances that are likely to be ecologically relevant in natural viewing, given the stereo-geometry of the mouse visual system. Across mammalian species, it appears that binocular integration is a common cortical computation used to extract information relevant for estimating depth. As such, it is a prime example of how the integration of multiple sensory signals is used to generate accurate estimates of properties in our environment.