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Sensory processing in the neocortex requires both feedforward and feedback information flow between cortical areas 1 . In feedback processing, higher-level representations provide contextual information to lower levels, and facilitate perceptual functions such as contour integration and figure–ground segmentation 2 , 3 . However, we have limited understanding of the circuit and cellular mechanisms that mediate feedback influence. Here we use long-range all-optical connectivity mapping in mice to show that feedback influence from the lateromedial higher visual area (LM) to the primary visual cortex (V1) is spatially organized. When the source and target of feedback represent the same area of visual space, feedback is relatively suppressive. By contrast, when the source is offset from the target in visual space, feedback is relatively facilitating. Two-photon calcium imaging data show that this facilitating feedback is nonlinearly integrated in the apical tuft dendrites of V1 pyramidal neurons: retinotopically offset (surround) visual stimuli drive local dendritic calcium signals indicative of regenerative events, and two-photon optogenetic activation of LM neurons projecting to identified feedback-recipient spines in V1 can drive similar branch-specific local calcium signals. Our results show how neocortical feedback connectivity and nonlinear dendritic integration can together form a substrate to support both predictive and cooperative contextual interactions. Feedback influence from a higher visual area to primary visual cortex in mice engages nonlinear dendritic integration.

Bayesian models of behavior suggest that organisms represent uncertainty associated with sensory variables. However, the neural code of uncertainty remains elusive. A central hypothesis is that uncertainty is encoded in the population activity of cortical neurons in the form of likelihood functions. We tested this hypothesis by simultaneously recording population activity from primate visual cortex during a visual categorization task in which trial-to-trial uncertainty about stimulus orientation was relevant for the decision. We decoded the likelihood function from the trial-to-trial population activity and found that it predicted decisions better than a point estimate of orientation. This remained true when we conditioned on the true orientation, suggesting that internal fluctuations in neural activity drive behaviorally meaningful variations in the likelihood function. Our results establish the role of population-encoded likelihood functions in mediating behavior and provide a neural underpinning for Bayesian models of perception.

The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events 1 – 10 . Despite the importance of prediction-error signals for various neural computations across the brain, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit that is required for generating sensory prediction-error signals in mouse primary visual cortex (V1). We show that violating animals’ predictions by an unexpected visual stimulus preferentially boosts responses of the layer 2/3 V1 neurons that are most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing non-specific surprise or difference signals about how the visual input deviates from the animal’s predictions. This selective amplification is implemented by a cooperative mechanism requiring thalamic input from the pulvinar and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits. Experiments in mice show that a cortico-thalamic circuit generates prediction-error signals in primary visual cortex that amplify visual input that deviates from animals’ expectations.

Predictive coding is a key mechanism to understand the computational processes underlying brain functioning: in a hierarchical network, higher levels predict the activity of lower levels, and the unexplained residuals (i.e., prediction errors) are passed back to higher layers. Because of its recursive nature, we wondered whether predictive coding could be related to brain oscillatory dynamics. First, we show that a simple 2-level predictive coding model of visual cortex, with physiological communication delays between levels, naturally gives rise to alpha-band rhythms, similar to experimental observations. Then, we demonstrate that a multilevel version of the same model can explain the occurrence of oscillatory traveling waves across levels, both forward (during visual stimulation) and backward (during rest). Remarkably, the predictions of our model are matched by the analysis of 2 independent electroencephalography (EEG) datasets, in which we observed oscillatory traveling waves in both directions.

Humans are remarkably efficient at categorizing natural scenes. In fact, scene categories can be decoded from functional MRI (fMRI) data throughout the ventral visual cortex, including the primary visual cortex, the parahippocampal place area (PPA), and the retrosplenial cortex (RSC). Here we ask whether, and where, we can still decode scene category if we reduce the scenes to mere lines. We collected fMRI data while participants viewed photographs and line drawings of beaches, city streets, forests, highways, mountains, and offices. Despite the marked difference in scene statistics, we were able to decode scene category from fMRI data for line drawings just as well as from activity for color photographs, in primary visual cortex through PPA and RSC. Even more remarkably, in PPA and RSC, error patterns for decoding from line drawings were very similar to those from color photographs. These data suggest that, in these regions, the information used to distinguish scene category is similar for line drawings and photographs. To determine the relative contributions of local and global structure to the human ability to categorize scenes, we selectively removed long or short contours from the line drawings. In a category-matching task, participants performed significantly worse when long contours were removed than when short contours were removed. We conclude that global scene structure, which is preserved in line drawings, plays an integral part in representing scene categories.

Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.

The brain's energy budget can be non-invasively assessed with different imaging modalities such as functional MRI (fMRI) and PET (fPET), which are sensitive to oxygen and glucose demands, respectively. The introduction of hybrid PET/MRI systems further enables the simultaneous acquisition of these parameters. Although a recently developed method offers the quantification of task-specific changes in glucose metabolism (CMRGlu) in a single measurement, direct comparison of the two imaging modalities is still difficult because of the different temporal resolutions. Thus, we optimized the protocol and systematically assessed shortened task durations of fPET to approach that of fMRI. Twenty healthy subjects (9 male) underwent one measurement on a hybrid PET/MRI scanner. During the scan, tasks were completed in four blocks for fMRI (4 × 30 s blocks) and fPET: participants tapped the fingers of their right hand repeatedly to the thumb while watching videos of landscapes. For fPET, subjects were randomly assigned to groups of n = 5 with varying task durations of 10, 5, 2 and 1 min, where task durations were kept constant within a measurement. The radiolabeled glucose analogue [18F]FDG was administered as 20% bolus plus constant infusion. The bolus increases the signal-to-noise ratio and leaves sufficient activity to detect task-related effects but poses additional challenges due to a discontinuity in the tracer uptake. First, three approaches to remove task effects from the baseline term were evaluated: (1) multimodal, based on the individual fMRI analysis, (2) atlas-based by removing presumably activated regions and (3) model-based by fitting the baseline with exponential functions. Second, we investigated the need to capture the arterial input function peak with automatic blood sampling for the quantification of CMRGlu. We finally compared the task-specific activation obtained from fPET and fMRI qualitatively and statistically. CMRGlu quantified only with manual arterial samples showed a strong correlation to that obtained with automatic sampling (r = 0.9996). The multimodal baseline definition was superior to the other tested approaches in terms of residuals (p < 0.001). Significant task-specific changes in CMRGlu were found in the primary visual and motor cortices (tM1 = 18.7 and tV1 = 18.3). Significant changes of fMRI activation were found in the same areas (tM1 = 16.0 and tV1 = 17.6) but additionally in the supplementary motor area, ipsilateral motor cortex and secondary visual cortex. Post-hoc t-tests showed strongest effects for task durations of 5 and 2 min (all p < 0.05 FWE corrected), whereas 1 min exhibited pronounced unspecific activation. Percent signal change (PSC) was higher for CMRGlu (∼18%–27%) compared to fMRI (∼2%). No significant association between PSC of task-specific CMRGlu and fMRI was found (r = 0.26). Using a bolus plus constant infusion protocol, the necessary task duration for reliable quantification of task-specific CMRGlu could be reduced to 5 and 2 min, therefore, approaching that of fMRI. Important for valid quantification is a correct baseline definition, which was ideal when task-relevant voxels were determined with fMRI. The absence of a correlation and the different activation pattern between fPET and fMRI suggest that glucose metabolism and oxygen demand capture complementary aspects of energy demands. •Quantification of task-specific CMRGlu with 20% bolus plus constant infusion.•Functional PET task durations down to 1 min were evaluated.•Active primary regions overlap between BOLD and CMRGlu.•No significant correlation between BOLD and CMRGlu.

To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response’s amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

Unified visual perception requires integration of bottom-up and top-down inputs in the primary visual cortex (V1), yet the organization of top-down inputs in V1 remains unclear. Here, we used optogenetics-assisted circuit mapping to identify how multiple top-down inputs from higher-order cortical and thalamic areas engage V1 excitatory and inhibitory neurons. Top-down inputs overlap in superficial layers yet segregate in deep layers. Inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) converge on L6 Pyrs, whereas ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) inputs are processed in parallel in Pyr-type-specific subnetworks (Pyr ←ORBvl and Pyr ←LP ) and drive mutual inhibition between them via local interneurons. Our study deepens understanding of the top-down modulation mechanisms of visual processing and establishes that V2M and ACA inputs in L6 employ integrated processing distinct from the parallel processing of LP and ORBvl inputs in L5. The organization of top-down inputs in primary visual cortex (V1) remains unclear. Here the authors characterized corticocortical and thalamocortical top-down inputs recruiting V1 neurons with cell-type and layer-specificities, and revealed distinct forms of top-down input processing.

•Fast Optical Imaging allows to unveil spatio-temporal dynamics of consciousness•When entering consciousness, visual stimuli elicit a sustained activation of LOC•LOC activity predicts subsequent activity in visual and motor areas•Activity in LOC occurs in the temporal window of VAN•LOC could represent a reliable neural correlate of phenomenal consciousness The study of the neural substrates that serve conscious vision is one of the unsolved questions of cognitive neuroscience. So far, consciousness literature has endeavoured to disentangle which brain areas and in what order are involved in giving rise to visual awareness, but the problem of consciousness still remains unsolved. Availing of two different but complementary sources of data (i.e., Fast Optical Imaging and EEG), we sought to unravel the neural dynamics responsible for the emergence of a conscious visual experience. Our results revealed that conscious vision is characterized by a significant increase of activation in extra-striate visual areas, specifically in the Lateral Occipital Complex (LOC), and that, more interestingly, such activity occurred in the temporal window of the ERP component commonly thought to represent the electrophysiological signature of visual awareness, i.e., the Visual Awareness Negativity (VAN). Furthermore, Granger causality analysis, performed to further investigate the flow of activity occurring in the investigated areas, unveiled that neural processes relating to conscious perception mainly originated in LOC and subsequently spread towards visual and motor areas. In general, the results of the present study seem to advocate for an early contribution of LOC in conscious vision, thus suggesting that it could represent a reliable neural correlate of visual awareness. Conversely, striate visual areas, showing awareness-related activity only in later stages of stimulus processing, could be part of the cascade of neural events following awareness emergence.