Sensory perception depends on the context within 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.

No abstract

No abstract

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, ϳ20% 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.

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.

Visual intracortical and transthalamic pathways carry distinct information to cortical areasHighlights d Transthalamic pathway through pulvinar indirectly connects lower to higher cortical areas d This pathway combines input from V1 with that of many cortical and subcortical areas d Pulvinar conveys distinct visual and motor information to different higher visual areas d Direct intracortical and transthalamic pathways convey different information

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.

Light and electron microscopy of the primary visual cortex of mice indicates that pyramidal neurons connect preferentially to inhibitory neurons.