Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

YOLOv11-Based quantification and temporal analysis of repetitive behaviors in deer mice

Detailed temporal dynamics of deer mouse (Peromyscus maniculatus bairdii) behavior remain poorly characterized. This study presents an integrated automated system combining YOLOv11 deep learning for direct behavior classification, post-processing for bout reconstruction, and a comprehensive temporal analysis suite tailored for deer mice. YOLOv11 performs frame-by-frame classification of key whole-body behaviors (e.g., Exploration, Grooming, Rearing types) using bounding boxes, bypassing initial kinematic feature engineering. Post-processing then reconstructs continuous behavioral bouts. This methodology facilitates objective, high-throughput quantification of behavior frequency, duration, and complex temporal organization, including transition patterns and sequential structure revealed through analyses like transition probabilities, behavior sequence mining, and lead-follower behavior relationships. In addition to describing a new methodology, this study provides initial baseline temporal data for deer mice, a powerful suite of analyses for future Peromyscus studies evaluating natural variation and experimental manipulations, or for use in other movement disorder models. In conclusion, the described YOLOv11-based system provides an efficient, reliable, and accessible methodology for both detailing behavioral activity and enhancing investigations of temporal dynamics in deer mice and other animal models.

Rapid emergence of latent knowledge in the sensory cortex drives learning

Rapid learning confers significant advantages on animals in ecological environments. Despite the need for speed, animals appear to only slowly learn to associate rewarded actions with predictive cues1-4. This slow learning is thought to be supported by gradual changes to cue representation in the sensory cortex2,5. However, evidence is growing that animals learn more rapidly than classical performance measures suggest6,7, challenging the prevailing model of sensory cortical plasticity. Here we investigated the relationship between learning and sensory cortical representations. We trained mice on an auditory go/no-go task that dissociated the rapid acquisition of task contingencies (learning) from its slower expression (performance)7. Optogenetic silencing demonstrated that the auditory cortex drives both rapid learning and slower performance gains but becomes dispensable once mice achieve 'expert' performance. Instead of enhanced cue representations8, two-photon calcium imaging of auditory cortical neurons throughout learning revealed two higher-order signals that were causal to learning and performance. A reward-prediction signal emerged rapidly within tens of trials, was present after action-related errors early in training, and faded in expert mice. Silencing at the time of this signal impaired rapid learning, suggesting that it serves an associative role. A distinct cell ensemble encoded and controlled licking suppression that drove slower performance improvements. These ensembles were spatially clustered but uncoupled from sensory representations, indicating higher-order functional segregation within auditory cortex. Our results reveal that the sensory cortex manifests higher-order computations that separably drive rapid learning and slower performance improvements, reshaping our understanding of the fundamental role of the sensory cortex.

Distinct Roles of Somatostatin and Parvalbumin Interneurons in Regulating Predictive Actions and Emotional Responses During Trace Eyeblink Conditioning

Learning involves evaluating multiple dimensions of information and generating appropriate actions, yet how the brain assigns value to this information remains unclear. In this study, we show that two types of interneurons (INs) in the primary somatosensory cortex-somatostatin-expressing (SST-INs) and parvalbumin-expressing (PV-INs) neurons-differentially contribute to information evaluation during trace eyeblink conditioning (TEC). An air puff (unconditioned stimulus, US) delivered after a whisker stimulus (conditioned stimulus, CS) elicited both reflexive eye closure and stress-related locomotion. However, only self-initiated, anticipatory eye closure during the CS window, measured via electromyography (EMG), was directly relevant to learning performance. We found that SST-IN activity changes aligned with the learning induced changes of the anticipatory eye blinks during the CS period, correlated with the EMG changes across learning. In contrast, PV-IN activity was positively correlated with stress-related locomotion following the US and showed no learning related changes, suggesting a role in processing the emotional or aversive component of the task. Furthermore, cholinergic signaling via nicotinic receptors modulated both SST- and PV-IN activities, in a manner consistent with their distinctive roles, linking these interneurons to the regulation of learning-related actions and emotional responses, respectively. These findings demonstrate that distinct interneuron populations evaluate different dimensions of information-SST-INs for predictive, adaptive actions and PV-INs for stress-related emotional responses-to guide learning and behavior.

Visual experience orthogonalizes visual cortical stimulus responses via population code transformation

Sensory and behavioral experience can alter visual cortical stimulus coding, but the precise form of this plasticity is unclear. We measured orientation tuning in 4,000-neuron populations of mouse V1 before and after training on a visuomotor task. Changes to single-cell tuning curves appeared complex, including development of asymmetries and of multiple peaks. Nevertheless, these complex tuning curve transformations can be explained by a simple equation: a convex transformation suppressing responses to task stimuli specifically in cells responding at intermediate levels. The strength of the transformation varies across trials, suggesting a dynamic circuit mechanism rather than static synaptic plasticity. The transformation results in sparsening and orthogonalization of population codes for task stimuli. It cannot improve the performance of an optimal stimulus decoder, which is already perfect even for naive codes, but it improves the performance of a suboptimal decoder model with inductive bias as might be found in downstream readout circuits.

Moculus: an immersive virtual reality system for mice incorporating stereo vision

Due to technical roadblocks, it is unclear how visual circuits represent multiple features or how behaviorally relevant representations are selected for long-term memory. Here we developed Moculus, a head-mounted virtual reality platform for mice that covers the entire visual field, and allows binocular depth perception and full visual immersion. This controllable environment, with three-dimensional (3D) corridors and 3D objects, in combination with 3D acousto-optical imaging, affords rapid visual learning and the uncovering of circuit substrates in one measurement session. Both the control and reinforcement-associated visual cue coding neuronal assemblies are transiently expanded by reinforcement feedback to near-saturation levels. This increases computational capability and allows competition among assemblies that encode behaviorally relevant information. The coding assemblies form partially orthogonal and overlapping clusters centered around hub cells with higher and earlier ramp-like responses, as well as locally increased functional connectivity.

Neocortical somatostatin neuron diversity in cognition and learning

Somatostatin-expressing (SST) neurons are a major class of electrophysiologically and morphologically distinct inhibitory cells in the mammalian neocortex. Transcriptomic data suggest that this class can be divided into multiple subtypes that are correlated with morpho-electric properties. At the same time, availability of transgenic tools to identify and record from SST neurons in awake, behaving mice has stimulated insights about their response properties and computational function. Neocortical SST neurons are regulated by sleep and arousal, attention, and novelty detection, and show marked response plasticity during learning. Recent studies suggest that subtype-specific analysis of SST neurons may be critical for understanding their complex roles in cortical function. In this review, we discuss and synthesize recent advances in understanding the diversity, circuit integration, and functional properties of this important group of GABAergic neurons.

Prey capture learning drives critical period-specific plasticity in mouse binocular visual cortex

Critical periods are developmental windows of high experience-dependent plasticity essential for the correct refinement of neuronal circuitry and function. While the consequences for the visual system of sensory deprivation during the critical period have been well-characterized, far less is known about the effects of enhanced sensory experience. Here, we use prey capture learning to assess structural and functional plasticity mediating visual learning in the primary visual cortex of critical period mice. We show that prey capture learning improves temporal frequency discrimination and drives a profound remodeling of visual circuitry through an increase in excitatory connectivity and spine turnover. This global and persistent rewiring is not observed in adult hunters and is mediated by TNFα-dependent mechanisms. Our findings demonstrate that enhanced visual experience in a naturalistic paradigm during the critical period can drive structural plasticity to improve visual function, and promotes a long-lasting increase in spine dynamics that could enhance subsequent plasticity.

Stability of cross-sensory input to primary somatosensory cortex across experience

Merging information across sensory modalities is key to forming robust percepts, yet how the brain achieves this feat remains unclear. Recent studies report cross-modal influences in the primary sensory cortex, suggesting possible multisensory integration in the early stages of cortical processing. We test several hypotheses about the function of auditory influences on mouse primary somatosensory cortex (S1) using in vivo two-photon calcium imaging. We found sound-evoked spiking activity in an extremely small fraction of cells, and this sparse activity did not encode auditory stimulus identity. Moreover, S1 did not encode information about specific audio-tactile feature conjunctions. Auditory and audio-tactile stimulus encoding remained unchanged after both passive experience and reinforcement. These results suggest that while primary sensory cortex is plastic within its own modality, the influence of other modalities is remarkably stable and stimulus nonspecific.

Discretized representations in V1 predict suboptimal orientation discrimination

Neuronal population activity in sensory cortices is the substrate for perceptual decisions. Yet, we still do not understand how neuronal information content in sensory cortices relates to behavioral reports. To reconcile neurometric and psychometric performance, we recorded the activity of V1 neurons in mice performing a Go/NoGo orientation discrimination task. We found that, around the discrimination threshold, V1 does not represent the orientation of the stimuli as canonically expected. Instead, it forms categorical representations characterized by a relocation of activity at task-relevant domains of the orientation representational space. The relative neuronal activity at those discrete domains accurately predicted the probabilities of the animals' decisions. Our results thus suggest that the categorical integration of discretized feature representations from sensory cortices explains perceptual decisions.

Experience-driven development of decision-related representations in the auditory cortex

Associating sensory stimuli with behavioral significance induces substantial changes in stimulus representations. Recent studies suggest that primary sensory cortices not only adjust representations of task-relevant stimuli, but actively participate in encoding features of the decision-making process. We sought to determine whether this trait is innate in sensory cortices or if choice representation develops with time and experience. To trace choice representation development, we perform chronic two-photon calcium imaging in the primary auditory cortex of head-fixed mice while they gain experience in a tone detection task with a delayed decision window. Our results reveal a progressive increase in choice-dependent activity within a specific subpopulation of neurons, aligning with growing task familiarity and adapting to changing task rules. Furthermore, task experience correlates with heightened synchronized activity in these populations and the ability to differentiate between different types of behavioral decisions. Notably, the activity of this subpopulation accurately decodes the same action at different task phases. Our findings establish a dynamic restructuring of population activity in the auditory cortex to encode features of the decision-making process that develop over time and refines with experience.

Learning enhances behaviorally relevant representations in apical dendrites

Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.

Adolescent-like Processing of Behaviorally Salient Cues in Sensory and Prefrontal Cortices of Adult Preterm-Born Mice

Preterm birth is a leading risk factor for atypicalities in cognitive and sensory processing, but it is unclear how prematurity impacts circuits that support these functions. To address this, we trained adult mice born a day early (preterm mice) on a visual discrimination task and found that they commit more errors and fail to achieve high levels of performance. Using in vivo electrophysiology, we found that the neurons in the primary visual cortex (V1) and the V1-projecting prefrontal anterior cingulate cortex (ACC) are hyper-responsive to the reward, reminiscent of cue processing in adolescence. Moreover, the non-rewarded cue fails to robustly activate the V1 and V1-projecting ACC neurons during error trials, in contrast to prefrontal fast-spiking (FS) interneurons which show elevated error-related activity, suggesting that preterm birth impairs the function of prefrontal circuits for error monitoring. Finally, environmental enrichment, a well-established paradigm that promotes sensory maturation, failed to improve the performance of preterm mice, suggesting limited capacity of early interventions for reducing the risk of cognitive deficits after preterm birth. Altogether, our study for the first time identifies potential circuit mechanisms of cognitive atypicalities in the preterm population and highlights the vulnerability of prefrontal circuits to advanced onset of extrauterine experience.

Lateral entorhinal cortex afferents reconfigure the activity in piriform cortex circuits

Odors are key signals for guiding spatial behaviors such as foraging and navigation in rodents. Recent findings reveal that odor representations in the piriform cortex (PCx) also encode spatial context information. However, the brain origins of this information and its integration into PCx microcircuitry remain unclear. This study investigates the lateral entorhinal cortex (LEC) as a potential source of spatial contextual information affecting the PCx microcircuit and its olfactory responses. Using mice brain slices, we performed patch-clamp recordings on superficial (SP) and deep (DP) pyramidal neurons, as well as parvalbumin (PV) and somatostatin (SOM) inhibitory interneurons. Concurrently, we optogenetically stimulated excitatory LEC projections to observe their impact on PCx activity. Results show that LEC inputs are heterogeneously distributed in the PCx microcircuit, evoking larger excitatory currents in SP and PV neurons due to higher monosynaptic connectivity. LEC inputs also differentially affect inhibitory circuits, activating PV while suppressing SOM interneurons. Studying the interaction between LEC inputs and sensory signals from the lateral olfactory tract (LOT) revealed that simultaneous LEC and LOT activation increases spiking in SP and DP neurons, with DP neurons showing a sharpened response due to LEC-induced inhibition that suppresses delayed LOT-evoked spikes. This suggests a regulatory mechanism where LEC inputs inhibit recurrent activity by activating PV interneurons. Our findings demonstrate that LEC afferents reconfigure PCx activity, aiding the understanding of how odor objects form within the PCx by integrating olfactory and nonolfactory information.

Adolescent-like Processing of Behaviorally Salient Cues in Sensory and Prefrontal Cortices of Adult Preterm-Born Mice

Preterm birth is a leading risk factor for atypicalities in cognitive and sensory processing, but it is unclear how prematurity impacts circuits that support these functions. To address this, we trained adult mice born a day early (preterm mice) on a visual discrimination task and found that they commit more errors and fail to achieve high levels of performance. Using in vivo electrophysiology , we found that the neurons in the primary visual cortex (V1) and the V1-projecting prefrontal anterior cingulate cortex (ACC) are hyper-responsive to the reward, reminiscent of cue processing in adolescence. Moreover, the non-rewarded cue fails to robustly activate the V1 and V1-projecting ACC neurons during error trials, in contrast to prefrontal fast-spiking (FS) interneurons which show elevated error-related activity, suggesting that preterm birth impairs the function of prefrontal circuits for error monitoring. Finally, environmental enrichment, a well-established paradigm that promotes sensory maturation, failed to improve the performance of preterm mice, suggesting limited capacity of early interventions for reducing the risk of cognitive deficits after preterm birth. Altogether, our study for the first time identifies potential circuit mechanisms of cognitive atypicalities in the preterm population and highlights the vulnerability of prefrontal circuits to advanced onset of extrauterine experience.

Vagus nerve stimulation recruits the central cholinergic system to enhance perceptual learning

Perception can be refined by experience, up to certain limits. It is unclear whether perceptual limits are absolute or could be partially overcome via enhanced neuromodulation and/or plasticity. Recent studies suggest that peripheral nerve stimulation, specifically vagus nerve stimulation (VNS), can alter neural activity and augment experience-dependent plasticity, although little is known about central mechanisms recruited by VNS. Here we developed an auditory discrimination task for mice implanted with a VNS electrode. VNS applied during behavior gradually improved discrimination abilities beyond the level achieved by training alone. Two-photon imaging revealed VNS induced changes to auditory cortical responses and activated cortically projecting cholinergic axons. Anatomical and optogenetic experiments indicated that VNS can enhance task performance through activation of the central cholinergic system. These results highlight the importance of cholinergic modulation for the efficacy of VNS and may contribute to further refinement of VNS methodology for clinical conditions.

Modulation of cortical representations of sensory and contextual information underlies aversive associative learning

Cortical neurons encode both sensory and contextual information, yet it remains unclear how experiences modulate these cortical representations. Here, we demonstrate that trace eyeblink conditioning (TEC), an aversive associative-learning paradigm linking conditioned (CS) with unconditioned stimuli (US), finely tunes cortical coding at both population and single-neuron levels. Initially, we show that the primary somatosensory cortex (S1) is necessary for TEC acquisition, as evidenced by local muscimol administration. At the population level, TEC enhances activity in a small subset (∼20%) of CS- or US-responsive primary neurons (rPNs) while diminishing activity in non-rPNs, including locomotion-tuned or unresponsive PNs. Crucially, TEC learning modulates the encoding of sensory versus contextual information in single rPNs: CS-responsive neurons become less responsive, while US-responsive neurons gain responses to CS. Moreover, we find that the cholinergic pathway, via nicotinic receptors, underlies TEC-induced modulations. These findings suggest that experiences dynamically tune cortical representations through cholinergic pathways.

Cooperative thalamocortical circuit mechanism for sensory prediction errors

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 events1-10. 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.

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Amygdala and Cortex Relationships during Learning of a Sensory Discrimination Task

During learning of a sensory discrimination task, the cortical and subcortical regions display complex spatiotemporal dynamics. During learning, both the amygdala and cortex link stimulus information to its appropriate association, for example, a reward. In addition, both structures are also related to nonsensory parameters such as body movements and licking during the reward period. However, the emergence of the cortico-amygdala relationships during learning is largely unknown. To study this, we combined wide-field cortical imaging with fiber photometry to simultaneously record cortico-amygdala population dynamics as male mice learn a whisker-dependent go/no-go task. We were able to simultaneously record neuronal populations from the posterior cortex and either the basolateral amygdala (BLA) or central/medial amygdala (CEM). Prior to learning, the somatosensory and associative cortex responded during sensation, while amygdala areas did not show significant responses. As mice became experts, amygdala responses emerged early during the sensation period, increasing in the CEM, while decreasing in the BLA. Interestingly, amygdala and cortical responses were associated with task-related body movement, displaying significant responses ∼200 ms before movement initiation which led to licking for the reward. A correlation analysis between the cortex and amygdala revealed negative and positive correlation with the BLA and CEM, respectively, only in the expert case. These results imply that learning induces an involvement of the cortex and amygdala which may aid to link sensory stimuli with appropriate associations.

Prediction-error signals in anterior cingulate cortex drive task-switching

Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states.

Stability of cross-sensory input to primary somatosensory cortex across experience

Merging information from across sensory modalities is key to forming robust, disambiguated percepts of the world, yet how the brain achieves this feat remains unclear. Recent observations of cross-modal influences in primary sensory cortical areas have suggested that multisensory integration may occur in the earliest stages of cortical processing, but the role of these responses is still poorly understood. We address these questions by testing several hypotheses about the possible functions served by auditory influences on the barrel field of mouse primary somatosensory cortex (S1) using in vivo 2-photon calcium imaging. We observed sound-evoked spiking activity in a small fraction of cells overall, and moreover that this sparse activity was insufficient to encode auditory stimulus identity; few cells responded preferentially to one sound or another, and a linear classifier trained to decode auditory stimuli from population activity performed barely above chance. Moreover S1 did not encode information about specific audio-tactile feature conjunctions that we tested. Our ability to decode auditory audio-tactile stimuli from neural activity remained unchanged after both passive experience and reinforcement. Collectively, these results suggest that while a primary sensory cortex is highly plastic with regard to its own modality, the influence of other modalities are remarkably stable and play a largely stimulus-non-specific role.

A neural circuit architecture for rapid learning in goal-directed navigation

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

Desegregation of neuronal predictive processing

Neural circuits construct internal 'world-models' to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. We find that robust predictive processing arises in a network with loose excitatory/inhibitory balance. Contrary to previous proposals of functionally specialized cell-types, the network exhibits desegregation of stimulus and prediction-error representations. We confirmed these model predictions by experimentally probing predictive-coding circuits using a rich stimulus-set to violate learned expectations. When constrained by data, our model further reveals and makes concrete testable experimental predictions for the distinct functional roles of excitatory and inhibitory neurons, and of neurons in different layers along a laminar hierarchy, in computing multi-modal predictions. These results together imply that in natural conditions, neural representations of internal models are highly distributed, yet structured to allow flexible readout of behaviorally-relevant information. The generality of our model advances the understanding of computation of internal models across species, by incorporating different types of predictive computations into a unified framework.

Goal-directed learning is multidimensional and accompanied by diverse and widespread changes in neocortical signaling

New tasks are often learned in stages with each stage reflecting a different learning challenge. Accordingly, each learning stage is likely mediated by distinct neuronal processes. And yet, most rodent studies of the neuronal correlates of goal-directed learning focus on individual outcome measures and individual brain regions. Here, we longitudinally studied mice from naïve to expert performance in a head-fixed, operant conditioning whisker discrimination task. In addition to tracking the primary behavioral outcome of stimulus discrimination, we tracked and compared an array of object-based and temporal-based behavioral measures. These behavioral analyses identify multiple, partially overlapping learning stages in this task, consistent with initial response implementation, early stimulus-response generalization, and late response inhibition. To begin to understand the neuronal foundations of these learning processes, we performed widefield Ca2+ imaging of dorsal neocortex throughout learning and correlated behavioral measures with neuronal activity. We found distinct and widespread correlations between neocortical activation patterns and various behavioral measures. For example, improvements in sensory discrimination correlated with target stimulus evoked activations of response-related cortices along with distractor stimulus evoked global cortical suppression. Our study reveals multidimensional learning for a simple goal-directed learning task and generates hypotheses for the neuronal modulations underlying these various learning processes.

Distinctive Neurophysiological Signatures of Analgesia after Inflammatory Pain in the ACC of Freely Moving Mice

Preclinical assessments of pain have often relied upon behavioral measurements and anesthetized neurophysiological recordings. Current technologies enabling large-scale neural recordings, however, have the potential to unveil quantifiable pain signals in conscious animals for preclinical studies. Although pain processing is distributed across many brain regions, the anterior cingulate cortex (ACC) is of particular interest in isolating these signals given its suggested role in the affective ("unpleasant") component of pain. Here, we explored the utility of the ACC toward preclinical pain research using head-mounted miniaturized microscopes to record calcium transients in freely moving male mice expressing genetically encoded calcium indicator 6f (GCaMP6f) under the Thy1 promoter. We verified the expression of GCaMP6f in excitatory neurons and found no intrinsic behavioral differences in this model. Using a multimodal stimulation paradigm across naive, pain, and analgesic conditions, we found that while ACC population activity roughly scaled with stimulus intensity, single-cell representations were highly flexible. We found only low-magnitude increases in population activity after complete Freund's adjuvant (CFA) and insufficient evidence for the existence of a robust nociceptive ensemble in the ACC. However, we found a temporal sharpening of response durations and generalized increases in pairwise neural correlations in the presence of the mechanistically distinct analgesics gabapentin or ibuprofen after (but not before) CFA-induced inflammatory pain. This increase was not explainable by changes in locomotion alone. Taken together, these results highlight challenges in isolating distinct pain signals among flexible representations in the ACC but suggest a neurophysiological hallmark of analgesia after pain that generalizes to at least two analgesics.

Anterior cingulate cortex provides the neural substrates for feedback-driven iteration of decision and value representation

Adjusting decision-making under uncertain and dynamic situations is the hallmark of intelligence. It requires a system capable of converting feedback information to renew the internal value. The anterior cingulate cortex (ACC) involves in error and reward events that prompt switching or maintenance of current decision strategies. However, it is unclear whether and how the changes of stimulus-action mapping during behavioral adaptation are encoded, nor how such computation drives decision adaptation. Here, we tracked ACC activity in male mice performing go/no-go auditory discrimination tasks with manipulated stimulus-reward contingencies. Individual ACC neurons integrate the outcome information to the value representation in the next-run trials. Dynamic recruitment of them determines the learning rate of error-guided value iteration and decision adaptation, forming a non-linear feedback-driven updating system to secure the appropriate decision switch. Optogenetically suppressing ACC significantly slowed down feedback-driven decision switching without interfering with the execution of the established strategy.

A mathematical theory of relational generalization in transitive inference

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

Acquisition of non-olfactory encoding improves odour discrimination in olfactory cortex

Olfaction is influenced by contextual factors, past experiences, and the animal's internal state. Whether this information is integrated at the initial stages of cortical odour processing is not known, nor how these signals may influence odour encoding. Here we revealed multiple and diverse non-olfactory responses in the primary olfactory (piriform) cortex (PCx), which dynamically enhance PCx odour discrimination according to behavioural demands. We performed recordings of PCx neurons from mice trained in a virtual reality task to associate odours with visual contexts to obtain a reward. We found that learning shifts PCx activity from encoding solely odours to a regime in which positional, contextual, and associative responses emerge on odour-responsive neurons that become mixed-selective. The modulation of PCx activity by these non-olfactory signals was dynamic, improving odour decoding during task engagement and in rewarded contexts. This improvement relied on the acquired mixed-selectivity, demonstrating how integrating extra-sensory inputs in sensory cortices can enhance sensory processing while encoding the behavioural relevance of stimuli.

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Rapid emergence of latent knowledge in the sensory cortex drives learning

Rapid learning confers significant advantages to animals in ecological environments. Despite the need for speed, animals appear to only slowly learn to associate rewarded actions with predictive cues1-4. This slow learning is thought to be supported by a gradual expansion of predictive cue representation in the sensory cortex2,5. However, evidence is growing that animals learn more rapidly than classical performance measures suggest6-8, challenging the prevailing model of sensory cortical plasticity. Here, we investigated the relationship between learning and sensory cortical representations. We trained mice on an auditory go/no-go task that dissociated the rapid acquisition of task contingencies (learning) from its slower expression (performance)7. Optogenetic silencing demonstrated that the auditory cortex (AC) drives both rapid learning and slower performance gains but becomes dispensable at expert. Rather than enhancement or expansion of cue representations9, two-photon calcium imaging of AC excitatory neurons throughout learning revealed two higher-order signals that were causal to learning and performance. First, a reward prediction (RP) signal emerged rapidly within tens of trials, was present after action-related errors only early in training, and faded at expert levels. Strikingly, silencing at the time of the RP signal impaired rapid learning, suggesting it serves an associative and teaching role. Second, a distinct cell ensemble encoded and controlled licking suppression that drove the slower performance improvements. These two ensembles were spatially clustered but uncoupled from underlying sensory representations, indicating a higher-order functional segregation within AC. Our results reveal that the sensory cortex manifests higher-order computations that separably drive rapid learning and slower performance improvements, reshaping our understanding of the fundamental role of the sensory cortex.

Dimensionality reduction beyond neural subspaces with slice tensor component analysis

Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.

Transient enhancement of stimulus-evoked activity in neocortex during sensory learning

Synaptic potentiation has been linked to learning in sensory cortex, but the connection between this potentiation and increased sensory-evoked neural activity is not clear. Here, we used longitudinal in vivo Ca2+ imaging in the barrel cortex of awake mice to test the hypothesis that increased excitatory synaptic strength during the learning of a whisker-dependent sensory-association task would be correlated with enhanced stimulus-evoked firing. To isolate stimulus-evoked responses from dynamic, task-related activity, imaging was performed outside of the training context. Although prior studies indicate that multiwhisker stimuli drive robust subthreshold activity, we observed sparse activation of L2/3 pyramidal (Pyr) neurons in both control and trained mice. Despite evidence for excitatory synaptic strengthening at thalamocortical and intracortical synapses in this brain area at the onset of learning-indeed, under our imaging conditions thalamocortical axons were robustly activated-we observed that L2/3 Pyr neurons in somatosensory (barrel) cortex displayed only modest increases in stimulus-evoked activity that were concentrated at the onset of training. Activity renormalized over longer training periods. In contrast, when stimuli and rewards were uncoupled in a pseudotraining paradigm, stimulus-evoked activity in L2/3 Pyr neurons was significantly suppressed. These findings indicate that sensory-association training but not sensory stimulation without coupled rewards may briefly enhance sensory-evoked activity, a phenomenon that might help link sensory input to behavioral outcomes at the onset of learning.

Reach-to-Grasp and tactile discrimination task: A new task for the study of sensory-motor learning

Although active touch in rodents arises from the forepaws as well as whiskers, most research on active touch only focuses on whiskers. This results in a paucity of tasks designed to assess the process of active touch with a forepaw. We develop a new experimental task, the Reach-to-Grasp and Tactile Discrimination task (RGTD task), to examine active touch with a forepaw in rodents, particularly changes in processes of active touch during motor skill learning. In the RGTD task, animals are required to (1) extend their forelimb to an object, (2) grasp the object, and (3) manipulate the grasped object with the forelimb. The animals must determine the direction of the manipulation based on active touch sensations arising during the period of the grasping. In experiment 1 of the present study, we showed that rats can learn the RGTD task. In experiment 2, we confirmed that the rats are capable of reversal learning of the RGTD task. The RGTD task shared most of the reaching movements involved with conventional forelimb reaching tasks. From the standpoint of a discrimination task, the RGTD task enables rigorous experimental control, for example by removing bias in the stimulus-response correspondence, and makes it possible to utilize diverse experimental procedures that have been difficult in prior tasks.

Differential representation of sensory information and behavioral choice across layers of the mouse auditory cortex

The activity of neurons in sensory areas sometimes covaries with upcoming choices in decision-making tasks. However, the prevalence, causal origin, and functional role of choice-related activity remain controversial. Understanding the circuit-logic of decision signals in sensory areas will require understanding their laminar specificity, but simultaneous recordings of neural activity across the cortical layers in forced-choice discrimination tasks have not yet been performed. Here, we describe neural activity from such recordings in the auditory cortex of mice during a frequency discrimination task with delayed report, which, as we show, requires the auditory cortex. Stimulus-related information was widely distributed across layers but disappeared very quickly after stimulus offset. Choice selectivity emerged toward the end of the delay period-suggesting a top-down origin-but only in the deep layers. Early stimulus-selective and late choice-selective deep neural ensembles were correlated, suggesting that the choice-selective signal fed back to the auditory cortex is not just action specific but develops as a consequence of the sensory-motor contingency imposed by the task.

Learning to learn: Single session acquisition of new rules by freely moving mice

Learning from examples and adapting to new circumstances are fundamental attributes of human cognition. However, it is unclear what conditions allow for fast and successful learning, especially in nonhuman subjects. To determine how rapidly freely moving mice can learn a new discrimination criterion (DC), we design a two-alternative forced-choice visual discrimination paradigm in which the DCs governing the task can change between sessions. We find that experienced animals can learn a new DC after being exposed to only five training and three testing trials. The propensity for single session learning improves over time and is accurately predicted based on animal experience and criterion difficulty. After establishing the procedural learning of a paradigm, mice continuously improve their performance in new circumstances. Thus, mice learn to learn.

Learning leaves a memory trace in motor cortex

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.

A mathematical theory of relational generalization in transitive inference

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A > B and B > C) and generalize it to new combinations of items (A > C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and has been found to improve generalization, unexpectedly show poor generalization and anomalous behavior. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

What does the mean mean? A simple test for neuroscience

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions.Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context.

Rationality, preferences, and emotions with biological constraints: it all starts from our senses

Is the role of our sensory systems to represent the physical world as accurately as possible? If so, are our preferences and emotions, often deemed irrational, decoupled from these 'ground-truth' sensory experiences? We show why the answer to both questions is 'no'. Brain function is metabolically costly, and the brain loses some fraction of the information that it encodes and transmits. Therefore, if brains maximize objective functions that increase the fitness of their species, they should adapt to the objective-maximizing rules of the environment at the earliest stages of sensory processing. Consequently, observed 'irrationalities', preferences, and emotions stem from the necessity for our early sensory systems to adapt and process information while considering the metabolic costs and internal states of the organism.

Disinhibition by VIP interneurons is orthogonal to cross-modal attentional modulation in primary visual cortex

Attentional modulation of sensory processing is a key feature of cognition; however, its neural circuit basis is poorly understood. A candidate mechanism is the disinhibition of pyramidal cells through vasoactive intestinal peptide (VIP) and somatostatin (SOM)-positive interneurons. However, the interaction of attentional modulation and VIP-SOM disinhibition has never been directly tested. We used all-optical methods to bi-directionally manipulate VIP interneuron activity as mice performed a cross-modal attention-switching task. We measured the activities of VIP, SOM, and parvalbumin (PV)-positive interneurons and pyramidal neurons identified in the same tissue and found that although activity in all cell classes was modulated by both attention and VIP manipulation, their effects were orthogonal. Attention and VIP-SOM disinhibition relied on distinct patterns of changes in activity and reorganization of interactions between inhibitory and excitatory cells. Circuit modeling revealed a precise network architecture consistent with multiplexing strong yet non-interacting modulations in the same neural population.

A consistent map in the medial entorhinal cortex supports spatial memory

The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.

Simultaneous interneuron labeling reveals population-level interactions among parvalbumin, somatostatin, and pyramidal neurons in cortex

Cortical interneurons shape network activity in cell type-specific ways, and are also influenced by interactions with other cell types. These specific cell-type interactions are understudied, as transgenic labeling methods typically restrict labeling to one neuron type at a time. Although recent methods have enabled post-hoc identification of cell types, these are not available to many labs. Here, we present a method to distinguish between two red fluorophores in vivo, which allowed imaging of activity in somatostatin (SOM), parvalbumin (PV), and putative pyramidal neurons (PYR) in mouse association cortex. We compared population events of elevated activity and observed that the PYR network state corresponded to the ratio between mean SOM and PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. These results extend previous findings in sensory cortex, as activity became sparser and less correlated when the ratio between SOM and PV activity was high.

Learning what matters: Synaptic plasticity with invariance to second-order input correlations

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. To learn efficient population codes, synaptic plasticity mechanisms must differentiate relevant latent features from spurious input correlations, which are omnipresent in cortical networks. Here, we develop a theory for sparse coding and synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, our learning objective explains the functional form of observed excitatory plasticity mechanisms, showing how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations so that receptive fields become aligned with features hidden in higher-order statistics. Invariance to second-order correlations enhances the versatility of biologically realistic learning models, supporting optimal decoding from noisy inputs and sparse population coding from spatially correlated stimuli. In a spiking model with triplet spike-timing-dependent plasticity (STDP), we show that individual neurons can learn localized oriented receptive fields, circumventing the need for input preprocessing, such as whitening, or population-level lateral inhibition. The theory advances our understanding of local unsupervised learning in cortical circuits, offers new interpretations of the Bienenstock-Cooper-Munro and triplet STDP models, and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.

Inferring neural activity before plasticity as a foundation for learning beyond backpropagation

For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. Here, we set out a fundamentally different principle on credit assignment called 'prospective configuration'. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms and (3) reproduces surprising patterns of neural activity and behavior observed in diverse human and rat learning experiments.

Mice Recognise Mice in Neighbouring Rearing Cages and Change Their Social Behaviour

Mice are social animals that change their behaviour primarily in response to visual, olfactory, and auditory information from conspecifics. Rearing conditions such as cage size and colour are important factors influencing mouse behaviour. In recent years, transparent plastic cages have become standard breeding cages. The advantage of using a transparent cage is that the experimenter can observe the mouse from outside the cage without touching the cage. However, mice may recognise the environment outside the cage and change their behaviour. We speculated that mice housed in transparent cages might recognise mice in neighbouring cages. We used only male mice in this experiment. C57BL/6 mice were kept in transparent rearing cages with open lids, and the cage positions were maintained for 3 weeks. Subsequently, we examined how mice behaved toward cagemate mice, mice from neighbouring cages, and mice from distant cages. We compared the level of interest in mice using a social preference test. Similar to previous reports, subject mice showed a high degree of interest in unfamiliar mice from distant cages. By contrast, subject mice reacted to mice from neighbouring cages as familiar mice, similar to cagemate mice. This suggests that mice housed in transparent cages with open lids perceive the external environment and identify mice in neighbouring cages. Researchers should pay attention to the environment outside the mouse cage, especially for the social preference test.

Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

Experimental work across species has demonstrated that spontaneously generated behaviors are robustly coupled to variations in neural activity within the cerebral cortex. Functional magnetic resonance imaging data suggest that temporal correlations in cortical networks vary across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these data generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior observed in awake animals. Here, we used wide-field mesoscopic calcium imaging to monitor cortical dynamics in awake mice and developed an approach to quantify rapidly time-varying functional connectivity. We show that spontaneous behaviors are represented by fast changes in both the magnitude and correlational structure of cortical network activity. Combining mesoscopic imaging with simultaneous cellular-resolution two-photon microscopy demonstrated that correlations among neighboring neurons and between local and large-scale networks also encode behavior. Finally, the dynamic functional connectivity of mesoscale signals revealed subnetworks not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide new insights into how behavioral information is represented across the neocortex and demonstrate an analytical framework for investigating time-varying functional connectivity in neural networks.

Visual experience induces 4-8 Hz synchrony between V1 and higher-order visual areas

Visual perceptual experience induces persistent 4-8 Hz oscillations in the mouse primary visual cortex (V1), encoding visual familiarity. Recent studies suggest that higher-order visual areas (HVAs) are functionally specialized and segregated into information streams processing distinct visual features. However, whether visual memories are processed and stored within the distinct streams is not understood. We report here that V1 and lateromedial (LM), but not V1 and anterolateral, become more phase synchronized in 4-8 Hz after the entrainment of visual stimulus that maximally induces responses in LM. Directed information analysis reveals changes in the top-down functional connectivity between V1 and HVAs. Optogenetic inactivation of LM reduces post-stimulus oscillation peaks in V1 and impairs visual discrimination behavior. Our results demonstrate that 4-8 Hz familiarity-evoked oscillations are specific for the distinct visual features and are present in the corresponding HVAs, where they may be used for the inter-areal communication with V1 during memory-related behaviors.

A direction-selective cortico-brainstem pathway adaptively modulates innate behaviors

Sensory cortices modulate innate behaviors through corticofugal projections targeting phylogenetically-old brainstem nuclei. However, the principles behind the functional connectivity of these projections remain poorly understood. Here, we show that in mice visual cortical neurons projecting to the optic-tract and dorsal-terminal nuclei (NOT-DTN) possess distinct response properties and anatomical connectivity, supporting the adaption of an essential innate eye movement, the optokinetic reflex (OKR). We find that these corticofugal neurons are enriched in specific visual areas, and they prefer temporo-nasal visual motion, matching the direction bias of downstream NOT-DTN neurons. Remarkably, continuous OKR stimulation selectively enhances the activity of these temporo-nasally biased cortical neurons, which can efficiently promote OKR plasticity. Lastly, we demonstrate that silencing downstream NOT-DTN neurons, which project specifically to the inferior olive-a key structure in oculomotor plasticity, impairs the cortical modulation of OKR and OKR plasticity. Our results unveil a direction-selective cortico-brainstem pathway that adaptively modulates innate behaviors.

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.