Visual intracortical and transthalamic pathways carry distinct information to cortical areas.

Sensory processing involves information flow between neocortical areas, assumed to rely on direct intracortical projections. However, cortical areas may also communicate indirectly via higher-order nuclei in the thalamus, such as the pulvinar or lateral posterior nucleus (LP) in the visual system of rodents. The fine-scale organization and function of these cortico-thalamo-cortical pathways remains unclear. We find that responses of mouse LP neurons projecting to higher visual areas likely derive from feedforward input from primary visual cortex (V1) combined with information from many cortical and subcortical areas, including superior colliculus. Signals from LP projections to different higher visual areas are tuned to specific features of visual stimuli and their locomotor context, distinct from the signals carried by direct intracortical projections from V1. Thus, visual transthalamic pathways are functionally specific to their cortical target, different from feedforward cortical pathways, and combine information from multiple brain regions, linking sensory signals with behavioral context.

A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex.

Context guides perception by influencing stimulus saliency. Accordingly, in visual cortex, responses to a stimulus are modulated by context, the visual scene surrounding the stimulus. Responses are suppressed when stimulus and surround are similar but not when they differ. The underlying mechanisms remain unclear. Here, we use optical recordings, manipulations, and computational modeling to show that disinhibitory circuits consisting of vasoactive intestinal peptide (VIP)-expressing and somatostatin (SOM)-expressing inhibitory neurons modulate responses in mouse visual cortex depending on similarity between stimulus and surround, primarily by modulating recurrent excitation. When stimulus and surround are similar, VIP neurons are inactive, and activity of SOM neurons leads to suppression of excitatory neurons. However, when stimulus and surround differ, VIP neurons are active, inhibiting SOM neurons, which leads to relief of excitatory neurons from suppression. We have identified a canonical cortical disinhibitory circuit that contributes to contextual modulation and may regulate perceptual saliency.

Feedback generates a second receptive field in neurons of the visual cortex.

Animals sense the environment through pathways that link sensory organs to the brain. In the visual system, these feedforward pathways define the classical feedforward receptive field (ffRF), the area in space in which visual stimuli excite a neuron1. The visual system also uses visual context-the visual scene surrounding a stimulus-to predict the content of the stimulus2, and accordingly, neurons have been identified that are excited by stimuli outside their ffRF3-8. However, the mechanisms that generate excitation to stimuli outside the ffRF are unclear. Here we show that feedback projections onto excitatory neurons in the mouse primary visual cortex generate a second receptive field that is driven by stimuli outside the ffRF. The stimulation of this feedback receptive field (fbRF) elicits responses that are slower and are delayed in comparison with those resulting from the stimulation of the ffRF. These responses are preferentially reduced by anaesthesia and by silencing higher visual areas. Feedback inputs from higher visual areas have scattered receptive fields relative to their putative targets in the primary visual cortex, which enables the generation of the fbRF. Neurons with fbRFs are located in cortical layers that receive strong feedback projections and are absent in the main input layer, which is consistent with a laminar processing hierarchy. The observation that large, uniform stimuli-which cover both the fbRF and the ffRF-suppress these responses indicates that the fbRF and the ffRF are mutually antagonistic. Whereas somatostatin-expressing inhibitory neurons are driven by these large stimuli, inhibitory neurons that express parvalbumin and vasoactive intestinal peptide have mutually antagonistic fbRF and ffRF, similar to excitatory neurons. Feedback projections may therefore enable neurons to use context to estimate information that is missing from the ffRF and to report differences in stimulus features across visual space, regardless of whether excitation occurs inside or outside the ffRF. By complementing the ffRF, the fbRF that we identify here could contribute to predictive processing.

Specific excitatory connectivity for feature integration in mouse primary visual cortex.

Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a 'like-to-like' scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a 'feature binding' scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1.

Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex.

Sensory perception depends on the context in which a stimulus occurs. Prevailing models emphasize cortical feedback as the source of contextual modulation. However, higher order thalamic nuclei, such as the pulvinar, interconnect with many cortical and subcortical areas, suggesting a role for the thalamus in providing sensory and behavioral context. Yet the nature of the signals conveyed to cortex by higher order thalamus remains poorly understood. Here we use axonal calcium imaging to measure information provided to visual cortex by the pulvinar equivalent in mice, the lateral posterior nucleus (LP), as well as the dorsolateral geniculate nucleus (dLGN). We found that dLGN conveys retinotopically precise visual signals, while LP provides distributed information from the visual scene. Both LP and dLGN projections carry locomotion signals. However, while dLGN inputs often respond to positive combinations of running and visual flow speed, LP signals discrepancies between self-generated and external visual motion. This higher order thalamic nucleus therefore conveys diverse contextual signals that inform visual cortex about visual scene changes not predicted by the animal's own actions.

Model-based analysis of pattern motion processing in mouse primary visual cortex.

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features.

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

One of the hallmarks of neocortical circuits is the predominance of recurrent excitation between pyramidal neurons, which is balanced by recurrent inhibition from smooth GABAergic neurons. It has been previously described that in layer 2/3 of primary visual cortex (V1) of cat and monkey, pyramidal cells filled with horseradish peroxidase connect approximately in proportion to the spiny (excitatory, 95% and 81%, respectively) and smooth (GABAergic, 5% and 19%, respectively) dendrites found in the neuropil. By contrast, a recent ultrastructural study of V1 in a single mouse found that smooth neurons formed 51% of the targets of the superficial layer pyramidal cells. This suggests that either the neuropil of this particular mouse V1 had a dramatically different composition to that of V1 in cat and monkey, or that smooth neurons were specifically targeted by the pyramidal cells in that mouse. We tested these hypotheses by examining similar cells filled with biocytin in a sample of five mice. We found that the average composition of the neuropil in V1 of these mice was similar to that described for cat and monkey V1, but that the superficial layer pyramidal cells do form proportionately more synapses with smooth dendrites than the equivalent neurons in cat or monkey. These distributions may underlie the distinct differences in functional architecture of V1 between rodent and higher mammals.

Distinct functional properties of primary and posteromedial visual area of mouse neocortex.

Visual input provides important landmarks for navigating in the environment, information that in mammals is processed by specialized areas in the visual cortex. In rodents, the posteromedial area (PM) mediates visual information between primary visual cortex (V1) and the retrosplenial cortex, which further projects to the hippocampus. To understand the functional role of area PM requires a detailed analysis of its spatial frequency (SF) and temporal frequency (TF) tuning. Here, we applied two-photon calcium imaging to map neuronal tuning for orientation, direction, SF and TF, and speed in response to drifting gratings in V1 and PM of anesthetized mice. The distributions of orientation and direction tuning were similar in V1 and PM. Notably, in both areas we found a preference for cardinal compared to oblique orientations. The overrepresentation of cardinal tuned neurons was particularly strong in PM showing narrow tuning bandwidths for horizontal and vertical orientations. A detailed analysis of SF and TF tuning revealed a broad range of highly tuned neurons in V1. On the contrary, PM contained one subpopulation of neurons with high spatial acuity and a second subpopulation broadly tuned for low SFs. Furthermore, ∼20% of the responding neurons in V1 and only 12% in PM were tuned to the speed of drifting gratings with PM preferring slower drift rates compared to V1. Together, PM is tuned for cardinal orientations, high SFs, and low speed and is further located between V1 and the retrosplenial cortex consistent with a role in processing natural scenes during spatial navigation.

Representation of visual scenes by local neuronal populations in layer 2/3 of mouse visual cortex.

How are visual scenes encoded in local neural networks of visual cortex? In rodents, visual cortex lacks a columnar organization so that processing of diverse features from a spot in visual space could be performed locally by populations of neighboring neurons. To examine how complex visual scenes are represented by local microcircuits in mouse visual cortex we measured visually evoked responses of layer 2/3 neuronal populations using 3D two-photon calcium imaging. Both natural and artificial movie scenes (10 seconds duration) evoked distributed and sparsely organized responses in local populations of 70-150 neurons within the sampled volumes. About 50% of neurons showed calcium transients during visual scene presentation, of which about half displayed reliable temporal activation patterns. The majority of the reliably responding neurons were activated primarily by one of the four visual scenes applied. Consequently, single-neurons performed poorly in decoding, which visual scene had been presented. In contrast, high levels of decoding performance (>80%) were reached when considering population responses, requiring about 80 randomly picked cells or 20 reliable responders. Furthermore, reliable responding neurons tended to have neighbors sharing the same stimulus preference. Because of this local redundancy, it was beneficial for efficient scene decoding to read out activity from spatially distributed rather than locally clustered neurons. Our results suggest a population code in layer 2/3 of visual cortex, where the visual environment is dynamically represented in the activation of distinct functional sub-networks.