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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.

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 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.

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.

In primary visual cortex (V1), neuronal receptive fields are generally thought to be fully established prior to eye-opening, with subsequent experience-dependent refinement controlled by GABAergic inhibition and regulated by homeostatic mechanisms. However, GABAergic interneurons (INs) are diverse and relatively little is known about the early postnatal roles of dendrite-targeting interneurons. Surprisingly, we find that somatostatin-expressing interneurons (SST-INs) in mouse V1 are not visually responsive at eye opening, instead developing visual sensitivity during the third postnatal week. Over the same period, SST-INs exhibit a rapid increase in excitatory innervation without compensatory synaptic scaling. Simultaneous imaging and optogenetic manipulation in juvenile animals reveals that SST-INs largely exert a multiplicative modulation of nearby excitatory neuron responses at all ages, but this effect increases over time. Our results identify a uniquely delayed developmental window for maturation of this inhibitory circuit and its contribution to visual gain normalization. The early postnatal roles of dendrite-targeting interneurons in primary visual cortex (V1) remain elusive. Here, the authors find that somatostatin interneurons in mouse V1 exhibit a uniquely delayed developmental trajectory for innervation and sensory responses, highlighting a window for the emergence of a key mechanism for normalization in cortical circuits.

Inhibition is critical for balanced cortical activity and learning. Parvalbumin-expressing cells (PV) are the most common cortical inhibitory interneurons. Strong PV activation inactivates cortical regions. However, the effect of moderate activation on vision and dependence on activation strength, timing, and task difficulty is not established. We investigated these three major factors during visual discriminations in mice. Moderate PV activation in the primary visual cortex (V1) improved easy but not difficult discriminations. It did so only during the initial 120 ms after stimulus onset, corresponding to the initial feedforward processing sweep. Both easy and difficult discriminations required undisturbed late phase activity beyond 120 ms, highlighting the importance of sustained V1 activity. Combined optogenetic activation and two-photon imaging showed that behavioral effects were associated with V1 response selectivity changes. A circuit model with nonlinear activation and strong competitive interactions between V1 cells captured the data. This demonstrates that early and sustained V1 activity is crucial for perceptual discrimination and delineates conditions when PV activation shapes neuronal selectivity to improve behavior.

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.

Natural scenes consist of complex feature distributions that shape neural responses and perception. However, in contrast to single features like stimulus orientations, the impact of broadband feature distributions remains unclear. We, therefore, presented visual stimuli with parametrically-controlled bandwidths of orientations and spatial frequencies to awake mice while recording neural activity in their primary visual cortex (V1). Increasing orientation but not spatial frequency bandwidth strongly increased the number and response amplitude of V1 neurons. This effect was not explained by single-cell orientation tuning but rather a broadband-specific relief from center-surround suppression. Moreover, neurons in deeper V1 and the superior colliculus responded much stronger to broadband stimuli, especially when mixing orientations and spatial frequencies. Lastly, broadband stimuli increased the separability of neural responses and improved the performance of mice in a visual discrimination task. Our results show that surround modulation increases neural responses to complex natural feature distributions to enhance sensory perception. The neural basis of how the visual cortex processes complex features remains under active investigation. Here, the authors show that broadband stimuli increase neural responses and visual perception due to a reduction in center-surround suppression.

Visual object recognition is driven through the what pathway, a hierarchy of visual areas processing features of increasing complexity and abstractness. The primary visual cortex (V1), this pathway’s origin, exhibits retinotopic organization: neurons respond to stimuli in specific visual field regions. A neuron responding to a central stimulus will not respond to a peripheral one, and vice versa. However, despite this organization, task-relevant feedback about peripheral stimuli can be decoded in unstimulated foveal cortex, and disrupting this feedback impairs discrimination behavior. The information encoded by this feedback remains unclear, as prior studies used computer-generated objects ill-suited to dissociate different representation types. To address this knowledge gap, we investigated the nature of information encoded in periphery-to-fovea feedback using real-world stimuli. Participants performed a same/different discrimination task on peripherally displayed images of vehicles and faces. Using fMRI multivariate decoding, we found that both peripheral and foveal V1 could decode images separated by low-level perceptual models (vehicles) but not those separated by semantic models (faces). This suggests the feedback primarily carries low-level perceptual information. In contrast, higher visual areas resolved semantically distinct images. A functional connectivity analysis revealed foveal V1 connections to both peripheral V1 and later-stage visual areas. These findings indicate that while both early and late visual areas may contribute to information transfer from peripheral to foveal processing streams, higher-to-lower area transfer may involve information loss. •Information about peripherally presented real-world stimuli can be decoded in the unstimulated foveal cortex.•Only a subset of real-world stimulus categories could be decoded from foveal cortex in V1, suggesting potential information loss in the feed back.•Functional connectivity analysis suggests that the feedback is managed by neural circuits within V1.

The brain’s ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when neural populations in macaque primary visual cortex are triggered to exhibit a periodic response to a repetitive sequence of optogenetic laser flashes, they learn to accurately reproduce the temporal sequence even when light stimulation is turned off. Despite the fact that individual cells had a poor capacity to extract temporal information, the population of neurons reproduced the periodic sequence in a temporally precise manner. The same neural population could learn different frequencies of external stimulation, and the ability to extract temporal information was found in all cortical layers. These results demonstrate a remarkable ability of sensory cortical populations to extract and reproduce complex temporal structure from unsupervised external stimulation even when stimuli are perceptually irrelevant. How the brain extracts and reproduces temporal regularities from incoming sensory information is poorly understood. Here, the authors discover the ability of neural populations in the primary visual cortex of behaving monkeys to extract precise temporal structure from unsupervised repetitive external stimulation.