Conditioning sharpens the spatial representation of rewarded stimuli in mouse primary visual cortex

The neural circuit of Superior colliculus to ventral tegmental area modulates visual cue associated with rewarding behavior in optical intracranial Self-Stimulation in mice

Visual system is the most important system of animal to cognize the information in outside world, and reward-related visual cues are the key factors in the consolidation and retrieval of reward memory. However, the neural circuit mechanism is still unclear. Superior Colliculus (SC) receive direct input from the retina and belong to the earliest stages of visual processing. Recent studies identified a specific pathway from SC to ventral tegmental area (VTA) that underlie specific innate behaviors, eg. flight or freezing, approach behaviors and so on. In present research, we investigated that inhibition of SC to VTA circuit with chemogenetics suppressed light cue-associated reward-seeking behaviors, while activation of the SC-VTA circuit with chemogenetic technology triggered the reward-seeking behaviors in optical intracranial self-stimulation for VTA DA neurons (oICSS) in mice. These findings suggest that neural circuit of SC-VTA mediates the retrieval of reward memory associated with visual cues, which will provide a new field for revealing the neural mechanism of pathological memory such as addiction.

Reward Modulates Visual Responses in the Superficial Superior Colliculus of Mice

The processing of sensory input is constantly adapting to behavioral demands and internal states. The drive to obtain reward, e.g., searching for water when thirsty, is a strong behavioral demand and associating the reward with its source, a certain environment or action, is paramount for survival. Here, we show that water reward increases subsequent visual activity in the superficial layers of the superior colliculus (SC), which receive direct input from the retina and belong to the earliest stages of visual processing. We trained mice of either sex to perform a visual decision task and recorded the activity of neurons in the SC using two-photon calcium imaging and high-density electrophysiological recordings. Responses to visual stimuli in around 20% of visually responsive neurons in the superficial SC were affected by reward delivered in the previous trial. Reward mostly increased visual responses independent from modulations due to pupil size changes. The modulation of visual responses by reward could not be explained by movements like licking. It was specific to responses to the following visual stimulus, independent of slow fluctuations in neural activity and independent of how often the stimulus was previously rewarded. Electrophysiological recordings confirmed these results and revealed that reward affected the early phase of the visual response around 80 ms after stimulus onset. Modulation of visual responses by reward, but not pupil size, significantly improved the performance of a population decoder to detect visual stimuli, indicating the relevance of reward modulation for the visual performance of the animal.SIGNIFICANCE STATEMENT To learn which actions lead to food, water, or safety, it is necessary to integrate the receiving of reward with sensory stimuli related to the reward. Cortical stages of sensory processing have been shown to represent stimulus-reward associations. Here, we show, however, that reward influences neurons at a much earlier stage of sensory processing, the superior colliculus (SC), receiving direct input from the retina. Visual responses were increased shortly after the animal received the water reward, which led to an improved stimulus signal in the population of these visual neurons. Reward modulation of early visual responses may thus improve perception of visual environments predictive of reward.

Signatures of task learning in neural representations

While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level.

How 'visual' is the visual cortex? The interactions between the visual cortex and other sensory, motivational and motor systems as enabling factors for visual perception

The definition of the visual cortex is primarily based on the evidence that lesions of this area impair visual perception. However, this does not exclude that the visual cortex may process more information than of retinal origin alone, or that other brain structures contribute to vision. Indeed, research across the past decades has shown that non-visual information, such as neural activity related to reward expectation and value, locomotion, working memory and other sensory modalities, can modulate primary visual cortical responses to retinal inputs. Nevertheless, the function of this non-visual information is poorly understood. Here we review recent evidence, coming primarily from studies in rodents, arguing that non-visual and motor effects in visual cortex play a role in visual processing itself, for instance disentangling direct auditory effects on visual cortex from effects of sound-evoked orofacial movement. These findings are placed in a broader framework casting vision in terms of predictive processing under control of frontal, reward- and motor-related systems. In contrast to the prevalent notion that vision is exclusively constructed by the visual cortical system, we propose that visual percepts are generated by a larger network-the extended visual system-spanning other sensory cortices, supramodal areas and frontal systems. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

How universal is preference for visual curvature? A systematic review and meta-analysis

Evidence dating back a century shows that humans are sensitive to and exhibit a preference for visual curvature. This effect has been observed in different age groups, human cultures, and primate species, suggesting that a preference for curvature could be universal. At the same time, several studies have found that preference for curvature is modulated by contextual and individual factors, casting doubt on this hypothesis. To resolve these conflicting findings, we conducted a systematic meta-analysis of studies that have investigated the preference for visual curvature. Our meta-analysis included 61 studies which provided 106 independent samples and 309 effect sizes. The results of a three-level random effects model revealed a Hedges' g of 0.39-consistent with a medium effect size. Further analyses revealed that preference for curvature is moderated by four factors: presentation time, stimulus type, expertise, and task. Together, our results suggest that preference for visual curvature is a reliable but not universal phenomenon and is influenced by factors other than perceptual information.

A flexible Python-based touchscreen chamber for operant conditioning reveals improved visual perception of cardinal orientations in mice

Natural scenes are composed of a wide range of edge angles and spatial frequencies, with a strong overrepresentation of vertical and horizontal edges. Correspondingly, many mammalian species are much better at discriminating these cardinal orientations compared to obliques. A potential reason for this increased performance could be an increased number of neurons in the visual cortex that are tuned to cardinal orientations, which is likely to be an adaptation to the natural scene statistics. Such biased angular tuning has recently been shown in the mouse primary visual cortex. However, it is still unknown if mice also show a perceptual dominance of cardinal orientations. Here, we describe the design of a novel custom-built touchscreen chamber that allows testing natural scene perception and orientation discrimination performance by applying different task designs. Using this chamber, we applied an iterative convergence towards orientation discrimination thresholds for cardinal or oblique orientations in different cohorts of mice. Surprisingly, the expert discrimination performance was similar for both groups but showed large inter-individual differences in performance and training time. To study the discrimination of cardinal and oblique stimuli in the same mice, we, therefore, applied, a different training regime where mice learned to discriminate cardinal and oblique gratings in parallel. Parallel training revealed a higher task performance for cardinal orientations in an early phase of the training. The performance for both orientations became similar after prolonged training, suggesting that learning permits equally high perceptual tuning towards oblique stimuli. In summary, our custom-built touchscreen chamber offers a flexible tool to test natural visual perception in rodents and revealed a training-induced increase in the perception of oblique gratings. The touchscreen chamber is entirely open-source, easy to build, and freely available to the scientific community to conduct visual or multimodal behavioral studies. It is also based on the FAIR principles for data management and sharing and could therefore serve as a catalyst for testing the perception of complex and natural visual stimuli across behavioral labs.

Structured random receptive fields enable informative sensory encodings

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

Norepinephrine potentiates and serotonin depresses visual cortical responses by transforming eligibility traces

Reinforcement allows organisms to learn which stimuli predict subsequent biological relevance. Hebbian mechanisms of synaptic plasticity are insufficient to account for reinforced learning because neuromodulators signaling biological relevance are delayed with respect to the neural activity associated with the stimulus. A theoretical solution is the concept of eligibility traces (eTraces), silent synaptic processes elicited by activity which upon arrival of a neuromodulator are converted into a lasting change in synaptic strength. Previously we demonstrated in visual cortical slices the Hebbian induction of eTraces and their conversion into LTP and LTD by the retroactive action of norepinephrine and serotonin Here we show in vivo in mouse V1 that the induction of eTraces and their conversion to LTP/D by norepinephrine and serotonin respectively potentiates and depresses visual responses. We also show that the integrity of this process is crucial for ocular dominance plasticity, a canonical model of experience-dependent plasticity.

Dynamic Distortion of Orientation Representation after Learning in the Mouse Primary Visual Cortex

Learning is an essential cognitive mechanism allowing behavioral adaptation through adjustments in neuronal processing. It is associated with changes in the activity of sensory cortical neurons evoked by task-relevant stimuli. However, the exact nature of those modifications and the computational advantages they may confer are still debated. Here, we investigated how learning an orientation discrimination task alters the neuronal representations of the cues orientations in the primary visual cortex (V1) of male and female mice. When comparing the activity evoked by the task stimuli in naive mice and the mice performing the task, we found that the representations of the orientation of the rewarded and nonrewarded cues were more accurate and stable in trained mice. This better cue representation in trained mice was associated with a distortion of the orientation representation space such that stimuli flanking the task-relevant orientations were represented as the task stimuli themselves, suggesting that those stimuli were generalized as the task cues. This distortion was context dependent as it was absent in trained mice passively viewing the task cues and enhanced in the behavioral sessions where mice performed best. Those modifications of the V1 population orientation representation in performing mice were supported by a suppression of the activity of neurons tuned for orientations neighboring the orientations of the task cues. Thus, visual processing in V1 is dynamically adapted to enhance the reliability of the representation of the learned cues and favor generalization in the task-relevant computational space.SIGNIFICANCE STATEMENT Performance improvement in a task often requires facilitating the extraction of the information necessary to its execution. Here, we demonstrate the existence of a suppression mechanism that improves the representation of the orientations of the task stimuli in the V1 of mice performing an orientation discrimination task. We also show that this mechanism distorts the V1 orientation representation space, leading stimuli flanking the task stimuli orientations to be generalized as the task stimuli themselves.

Computational mechanisms of distributed value representations and mixed learning strategies

Learning appropriate representations of the reward environment is challenging in the real world where there are many options, each with multiple attributes or features. Despite existence of alternative solutions for this challenge, neural mechanisms underlying emergence and adoption of value representations and learning strategies remain unknown. To address this, we measure learning and choice during a multi-dimensional probabilistic learning task in humans and trained recurrent neural networks (RNNs) to capture our experimental observations. We find that human participants estimate stimulus-outcome associations by learning and combining estimates of reward probabilities associated with the informative feature followed by those of informative conjunctions. Through analyzing representations, connectivity, and lesioning of the RNNs, we demonstrate this mixed learning strategy relies on a distributed neural code and opponency between excitatory and inhibitory neurons through value-dependent disinhibition. Together, our results suggest computational and neural mechanisms underlying emergence of complex learning strategies in naturalistic settings.

Task-dependent mixed selectivity in the subiculum

CA1 and subiculum (SUB) connect the hippocampus to numerous output regions. Cells in both areas have place-specific firing fields, although they are more dispersed in SUB. Weak responses to head direction and running speed have been reported in both regions. However, how such information is encoded in CA1 and SUB and the resulting impact on downstream targets are poorly understood. Here, we estimate the tuning of simultaneously recorded CA1 and SUB cells to position, head direction, and speed. Individual neurons respond conjunctively to these covariates in both regions, but the degree of mixed representation is stronger in SUB, and more so during goal-directed spatial navigation than free foraging. Each navigational variable could be decoded with higher precision, from a similar number of neurons, in SUB than CA1. The findings point to a possible contribution of mixed-selective coding in SUB to efficient transmission of hippocampal representations to widespread brain regions.

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CA1 and subiculum neurons respond conjunctively to position, head direction, and speed

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The degree of conjunctive coding (“mixed selectivity”) is stronger in the subiculum

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Mixed selectivity is stronger during goal-directed navigation than in free foraging

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Decoding of each navigational covariate is more accurate with mixed selectivity

Stability in the Face of Change: Lifelong Experience-Dependent Plasticity in the Sensory Cortex

Plasticity is a fundamental property of the nervous system that enables its adaptations to the ever-changing environment. Heightened plasticity typical for developing circuits facilitates their robust experience-dependent functional maturation. This plasticity wanes during adolescence to permit the stabilization of mature brain function, but abundant evidence supports that adult circuits exhibit both transient and long-term experience-induced plasticity. Cortical plasticity has been extensively studied throughout the life span in sensory systems and the main distinction between development and adulthood arising from these studies is the concept that passive exposure to relevant information is sufficient to drive robust plasticity early in life, while higher-order attentional mechanisms are necessary to drive plastic changes in adults. Recent work in the primary visual and auditory cortices began to define the circuit mechanisms that govern these processes and enable continuous adaptation to the environment, with transient circuit disinhibition emerging as a common prerequisite for both developmental and adult plasticity. Drawing from studies in visual and auditory systems, this review article summarizes recent reports on the circuit and cellular mechanisms of experience-driven plasticity in the developing and adult brains and emphasizes the similarities and differences between them. The benefits of distinct plasticity mechanisms used at different ages are discussed in the context of sensory learning, as well as their relationship to maladaptive plasticity and neurodevelopmental brain disorders. Knowledge gaps and avenues for future work are highlighted, and these will hopefully motivate future research in these areas, particularly those about the learning of complex skills during development.

Reward Association Enhances Stimulus-Specific Representations in Primary Visual Cortex

The potential for neuronal representations of external stimuli to be modified by previous experience is critical for efficient sensory processing and improved behavioral outcomes. To investigate how repeated exposure to a visual stimulus affects its representation in mouse primary visual cortex (V1), we performed two-photon calcium imaging of layer 2/3 neurons and assessed responses before, during, and after the presentation of a repetitive stimulus over 5 consecutive days. We found a stimulus-specific enhancement of the neuronal representation of the repetitively presented stimulus when it was associated with a reward. This was observed both after mice actively learned a rewarded task and when the reward was randomly received. Stimulus-specific enhanced representation resulted both from neurons gaining selectivity and from increased response reliability in previously selective neurons. In the absence of reward, there was either no change in stimulus representation or a decreased representation when the stimulus was viewed at a fixed temporal frequency. Pairing a second stimulus with a reward led to a similar enhanced representation and increased discriminability between the equally rewarded stimuli. Single-neuron responses showed that separate subpopulations discriminated between the two rewarded stimuli depending on whether the stimuli were displayed in a virtual environment or viewed on a single screen. We suggest that reward-associated responses enable the generalization of enhanced stimulus representation across these V1 subpopulations. We propose that this dynamic regulation of visual processing based on the behavioral relevance of sensory input ultimately enhances and stabilizes the representation of task-relevant features while suppressing responses to non-relevant stimuli.

Reward Timing and Its Expression by Inhibitory Interneurons in the Mouse Primary Visual Cortex

The primary sensory cortex has historically been studied as a low-level feature detector, but has more recently been implicated in many higher-level cognitive functions. For instance, after an animal learns that a light predicts water at a fixed delay, neurons in the primary visual cortex (V1) can produce "reward timing activity" (i.e., spike modulation of various forms that relate the interval between the visual stimulus and expected reward). Local manipulations to V1 implicate it as a site of learning reward timing activity (as opposed to simply reporting timing information from another region via feedback input). However, the manner by which V1 then produces these representations is unknown. Here, we combine behavior, in vivo electrophysiology, and optogenetics to investigate the characteristics of and circuit mechanisms underlying V1 reward timing in the head-fixed mouse. We find that reward timing activity is present in mouse V1, that inhibitory interneurons participate in reward timing, and that these representations are consistent with a theorized network architecture. Together, these results deepen our understanding of V1 reward timing and the manner by which it is produced.

Inhibitory microcircuits for top-down plasticity of sensory representations

Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation.

Rewards can improve stimulus processing in early sensory areas but the underlying neural circuit mechanisms are unknown. Here, the authors build a computational model of layer 2/3 primary visual cortex and suggest that plastic inhibitory circuits change first and then increase excitatory representations beyond the presence of rewards.

Learning, memory and consolidation mechanisms for behavioral control in hierarchically organized cortico-basal ganglia systems

This article aims to provide a synthesis on the question how brain structures cooperate to accomplish hierarchically organized behaviors, characterized by low‐level, habitual routines nested in larger sequences of planned, goal‐directed behavior. The functioning of a connected set of brain structures—prefrontal cortex, hippocampus, striatum, and dopaminergic mesencephalon—is reviewed in relation to two important distinctions: (a) goal‐directed as opposed to habitual behavior and (b) model‐based and model‐free learning. Recent evidence indicates that the orbitomedial prefrontal cortices not only subserve goal‐directed behavior and model‐based learning, but also code the “landscape” (task space) of behaviorally relevant variables. While the hippocampus stands out for its role in coding and memorizing world state representations, it is argued to function in model‐based learning but is not required for coding of action–outcome contingencies, illustrating that goal‐directed behavior is not congruent with model‐based learning. While the dorsolateral and dorsomedial striatum largely conform to the dichotomy between habitual versus goal‐directed behavior, ventral striatal functions go beyond this distinction. Next, we contextualize findings on coding of reward‐prediction errors by ventral tegmental dopamine neurons to suggest a broader role of mesencephalic dopamine cells, viz. in behavioral reactivity and signaling unexpected sensory changes. We hypothesize that goal‐directed behavior is hierarchically organized in interconnected cortico‐basal ganglia loops, where a limbic‐affective prefrontal‐ventral striatal loop controls action selection in a dorsomedial prefrontal–striatal loop, which in turn regulates activity in sensorimotor‐dorsolateral striatal circuits. This structure for behavioral organization requires alignment with mechanisms for memory formation and consolidation. We propose that frontal corticothalamic circuits form a high‐level loop for memory processing that initiates and temporally organizes nested activities in lower‐level loops, including the hippocampus and the ripple‐associated replay it generates. The evidence on hierarchically organized behavior converges with that on consolidation mechanisms in suggesting a frontal‐to‐caudal directionality in processing control.

The Visual Cortex in Context

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

Cortical layer-specific critical dynamics triggering perception

Perceptual experiences may arise from neuronal activity patterns in mammalian neocortex. We probed mouse neocortex during visual discrimination using a red-shifted channelrhodopsin (ChRmine, discovered through structure-guided genome mining) alongside multiplexed multiphoton-holography (MultiSLM), achieving control of individually specified neurons spanning large cortical volumes with millisecond precision. Stimulating a critical number of stimulus-orientation-selective neurons drove widespread recruitment of functionally related neurons, a process enhanced by (but not requiring) orientation-discrimination task learning. Optogenetic targeting of orientation-selective ensembles elicited correct behavioral discrimination. Cortical layer-specific dynamics were apparent, as emergent neuronal activity asymmetrically propagated from layer 2/3 to layer 5, and smaller layer 5 ensembles were as effective as larger layer 2/3 ensembles in eliciting orientation discrimination behavior. Population dynamics emerging after optogenetic stimulation both correctly predicted behavior and resembled natural internal representations of visual stimuli at cellular resolution over volumes of cortex.

Towards a Unified View on Pathways and Functions of Neural Recurrent Processing

There are three neural feedback pathways to the primary visual cortex (V1): corticocortical, pulvinocortical, and cholinergic. What are the respective functions of these three projections? Possible functions range from contextual modulation of stimulus processing and feedback of high-level information to predictive processing (PP). How are these functions subserved by different pathways and can they be integrated into an overarching theoretical framework? We propose that corticocortical and pulvinocortical connections are involved in all three functions, whereas the role of cholinergic projections is limited by their slow response to stimuli. PP provides a broad explanatory framework under which stimulus-context modulation and high-level processing are subsumed, involving multiple feedback pathways that provide mechanisms for inferring and interpreting what sensory inputs are about.