Distinct Waking States for Strong Evoked Responses in Primary Visual Cortex and Optimal Visual Detection Performance

Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

The processing of sensory information, even at early stages, is influenced by the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural activity to a single "level" of arousal, defined by a behavioral indicator such as pupil size. In this study, we expand the understanding of arousal-related modulations in sensory systems by uncovering multiple timescales of pupil dynamics and their relationship to neural activity. Specifically, we observed a robust coupling between spiking activity in the mouse dorsolateral geniculate nucleus (dLGN) of the thalamus and pupil dynamics across timescales spanning a few seconds to several minutes. Throughout all these timescales, 2 distinct spiking modes-individual tonic spikes and tightly clustered bursts of spikes-preferred opposite phases of pupil dynamics. This multi-scale coupling reveals modulations distinct from those captured by pupil size per se, locomotion, and eye movements. Furthermore, coupling persisted even during viewing of a naturalistic movie, where it contributed to differences in the encoding of visual information. We conclude that dLGN spiking activity is under the simultaneous influence of multiple arousal-related processes associated with pupil dynamics occurring over a broad range of timescales.

Modulation of metastable ensemble dynamics explains optimal coding at moderate arousal in auditory cortex

Performance during perceptual decision-making exhibits an inverted-U relationship with arousal, but the underlying network mechanisms remain unclear. Here, we recorded from auditory cortex (A1) of behaving mice during passive tone presentation, while tracking arousal via pupillometry. We found that tone discriminability in A1 ensembles was optimal at intermediate arousal, revealing a population-level neural correlate of the inverted-U relationship. We explained this arousal-dependent coding using a spiking network model with a clustered architecture. Specifically, we show that optimal stimulus discriminability is achieved near a transition between a multi-attractor phase with metastable cluster dynamics (low arousal) and a single-attractor phase (high arousal). Additional signatures of this transition include arousal-induced reductions of overall neural variability and the extent of stimulus-induced variability quenching, which we observed in the empirical data. Altogether, this study elucidates computational principles underlying interactions between pupil-linked arousal, sensory processing, and neural variability, and suggests a role for phase transitions in explaining nonlinear modulations of cortical computations.

Decision-making dynamics are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, using computational modeling of visual and auditory task performance data from mice, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we find that animals fluctuate between minutes-long optimal, sub-optimal, and disengaged performance states. Optimal state epochs are predicted by intermediate levels, and reduced variability, of pupil diameter and movement. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states and suggest that mice regulate their arousal during optimal performance.

A machine learning approach for real-time cortical state estimation

Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.

A disinhibitory circuit mechanism explains a general principle of peak performance during mid-level arousal

Perceptual decision-making is highly dependent on the momentary arousal state of the brain, which fluctuates over time on a scale of hours, minutes, and even seconds. The textbook relationship between momentary arousal and task performance is captured by an inverted U-shape, as put forward in the Yerkes-Dodson law. This law suggests optimal performance at moderate levels of arousal and impaired performance at low or high arousal levels. However, despite its popularity, the evidence for this relationship in humans is mixed at best. Here, we use pupil-indexed arousal and performance data from various perceptual decision-making tasks to provide converging evidence for the inverted U-shaped relationship between spontaneous arousal fluctuations and performance across different decision types (discrimination, detection) and sensory modalities (visual, auditory). To further understand this relationship, we built a neurobiologically plausible mechanistic model and show that it is possible to reproduce our findings by incorporating two types of interneurons that are both modulated by an arousal signal. The model architecture produces two dynamical regimes under the influence of arousal: one regime in which performance increases with arousal and another regime in which performance decreases with arousal, together forming an inverted U-shaped arousal-performance relationship. We conclude that the inverted U-shaped arousal-performance relationship is a general and robust property of sensory processing. It might be brought about by the influence of arousal on two types of interneurons that together act as a disinhibitory pathway for the neural populations that encode the available sensory evidence used for the decision.

Transthalamic input to higher-order cortex selectively conveys state information

Communication among different neocortical areas is largely thought to be mediated by long-range synaptic interactions between cortical neurons, with the thalamus providing only an initial relay of information from the sensory periphery. Higher-order thalamic nuclei receive strong synaptic inputs from the cortex and send robust projections back to other cortical areas, providing a distinct and potentially critical route for cortico-cortical communication. However, the relative contributions of corticocortical and thalamocortical inputs to higher-order cortical function remain unclear. Using imaging of cortical neurons and projection axon terminals in combination with optogenetic manipulations, we find that the higher-order visual thalamus of mice conveys a specialized stream of information to higher-order visual cortex. Whereas corticocortical projections from lower cortical areas convey robust visual information, higher-order thalamocortical projections convey strong behavioral state information. Together, these findings suggest a key role for higher-order thalamus in providing contextual signals that flexibly modulate sensory processing in higher-order cortex.

Endogenous fluctuations in cortical state selectively enhance different modes of sensory processing in human temporal lobe

The degree of synchronized fluctuations in neocortical network activity can vary widely during alertness. One influential idea that has emerged over the past few decades is that perceptual decisions are more accurate when the state of population activity is desynchronized. This suggests that optimal task performance may occur during a particular cortical state - the desynchronized state. Here we show that, contrary to this view, cortical state can both facilitate and suppress perceptual performance in a task-dependent manner. We performed electrical recordings from surface-implanted grid electrodes in the temporal lobe while human subjects completed two perceptual tasks. We found that when local population activity is in a synchronized state, network and perceptual performance are enhanced in a detection task and impaired in a discrimination task, but these modulatory effects are reversed when population activity is desynchronized. These findings indicate that the brain has adapted to take advantage of endogenous fluctuations in the state of neural populations in temporal cortex to selectively enhance different modes of sensory processing during perception in a state-dependent manner.

Motor state changes escape behavior of crickets

Animals change their behavior depending on external circumstances, internal factors, and their interactions. Locomotion state is a crucial internal factor that profoundly affects sensory perception and behavior. However, studying the behavioral impacts of locomotion state in free-moving animals has been challenging due to difficulty in reproducing quantitatively identical stimuli in freely moving animals. We utilized a closed-loop controlled servosphere treadmill system, enabling unrestricted confinement and orientation of small animals, and investigated wind-induced escape behavior in freely moving crickets. When stimulated during locomotion, the crickets quickly stopped before initiating escape behavior. Moving crickets exhibited a higher probability of escape response compared to stationary crickets. The threshold for pausing response in moving crickets was also much lower than the escape response threshold. Moving crickets had delayed reaction times for escape and greater variance in movement direction compared to stationary crickets. The locomotion-related response delay may be compensated by an elevated sensitivity to airflow.

The h-current controls cortical recurrent network activity through modulation of dendrosomatic communication

A fundamental feature of the cerebral cortex is the ability to rapidly turn on and off maintained activity within ensembles of neurons through recurrent excitation balanced by inhibition. Here we demonstrate that reduction of the h-current, which is especially prominent in pyramidal cell dendrites, strongly increases the ability of local cortical networks to generate maintained recurrent activity. Reduction of the h-current resulted in hyperpolarization and increase in input resistance of both the somata and apical dendrites of layer 5 pyramidal cells, while strongly increasing the dendrosomatic transfer of low (<20 Hz) frequencies, causing an increased responsiveness to dynamic clamp-induced recurrent network-like activity injected into the dendrites and substantially increasing the duration of spontaneous Up states. We propose that modulation of the h-current may strongly control the ability of cortical networks to generate recurrent persistent activity and the formation and dissolution of neuronal ensembles.

Spontaneous variations in arousal modulate subsequent visual processing and local field potential dynamics in the ferret during quiet wakefulness

Behavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in the primary visual cortex and posterior parietal cortex in awake ferrets, using pupil diameter as an indicator of arousal. We observed that the power of stimuli-induced alpha (8-12 Hz) decreases when the arousal level increases. The peak of alpha power shifts depending on arousal. High arousal increases inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area posterior parietal cortex and supragranular layers in V1. During high arousal, neurons in V1 displayed higher firing rates at their preferred orientations. Broad-spiking cells in V1 are entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons are phase-locked to low- (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.

Decision-making dynamics are predicted by arousal and uninstructed movements

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize that these decision-making dynamics can be predicted by externally observable measures, such as uninstructed movements and changes in arousal. Here, combining behavioral experiments in mice with computational modeling, we uncovered lawful relationships between transitions in strategic task performance states and an animal's arousal and uninstructed movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, we found that animals fluctuate between minutes-long optimal, sub-optimal and disengaged performance states. Optimal state epochs were predicted by intermediate levels, and reduced variability, of pupil diameter, along with reduced variability in face movements and locomotion. Our results demonstrate that externally observable uninstructed behaviors can predict optimal performance states, and suggest mice regulate their arousal during optimal performance.

Brainstem serotonin neurons selectively gate retinal information flow to thalamus

Retinal ganglion cell (RGC) types relay parallel streams of visual feature information. We hypothesized that neuromodulators might efficiently control which visual information streams reach the cortex by selectively gating transmission from specific RGC axons in the thalamus. Using fiber photometry recordings, we found that optogenetic stimulation of serotonergic axons in primary visual thalamus of awake mice suppressed ongoing and visually evoked calcium activity and glutamate release from RGC boutons. Two-photon calcium imaging revealed that serotonin axon stimulation suppressed RGC boutons that responded strongly to global changes in luminance more than those responding only to local visual stimuli, while the converse was true for suppression induced by increases in arousal. Converging evidence suggests that differential expression of the 5-HT1B receptor on RGC presynaptic terminals, but not differential density of nearby serotonin axons, may contribute to the selective serotonergic gating of specific visual information streams before they can activate thalamocortical neurons.

Ongoing movement controls sensory integration in the dorsolateral striatum

The dorsolateral striatum (DLS) receives excitatory inputs from both sensory and motor cortical regions. In the neocortex, sensory responses are affected by motor activity, however, it is not known whether such sensorimotor interactions occur in the striatum and how they are shaped by dopamine. To determine the impact of motor activity on striatal sensory processing, we performed in vivo whole-cell recordings in the DLS of awake mice during the presentation of tactile stimuli. Striatal medium spiny neurons (MSNs) were activated by both whisker stimulation and spontaneous whisking, however, their responses to whisker deflection during ongoing whisking were attenuated. Dopamine depletion reduced the representation of whisking in direct-pathway MSNs, but not in those of the indirect-pathway. Furthermore, dopamine depletion impaired the discrimination between ipsilateral and contralateral sensory stimulation in both direct and indirect pathway MSNs. Our results show that whisking affects sensory responses in DLS and that striatal representation of both processes is dopamine- and cell type-dependent.

Walking humans and running mice: perception and neural encoding of optic flow during self-motion

Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Independent response modulation of visual cortical neurons by attentional and behavioral states

Sensory processing is influenced by cognitive and behavioral states, but how these states interact to modulate responses of individual neurons is unknown. We trained mice in a visual discrimination task wherein they attended to different locations within a hemifield while running or sitting still, enabling us to examine how visual responses are modulated by spatial attention and running behavior. We found that spatial attention improved discrimination performance and strengthened visual responses of excitatory neurons in the primary visual cortex whose receptive fields overlapped with the attended location. Although individual neurons were modulated by both spatial attention and running, the magnitudes of these influences were not correlated. While running-dependent modulation was stable across days, attentional modulation was dynamic, influencing individual neurons to different degrees after repeated changes in attentional states. Thus, despite similar effects on neural responses, spatial attention and running act independently with different dynamics, implying separable mechanisms for their implementation.

Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity

Variation in an animal's behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent circuit dynamics may reflect neuromodulatory influences such as that of acetylcholine (ACh). Although early literature suggested that ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. In addition, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. Here, we perform simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that higher arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic modulation and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

Behavioral impulsivity is associated with pupillary alterations and hyperactivity in CDKL5 mutant mice

Cyclin-dependent kinase-like 5 (Cdkl5) deficiency disorder (CDD) is a severe neurodevelopmental condition caused by mutations in the X-linked Cdkl5 gene. CDD is characterized by early-onset seizures in the first month of life, intellectual disability, motor and social impairment. No effective treatment is currently available and medical management is only symptomatic and supportive. Recently, mouse models of Cdkl5 disorder have demonstrated that mice lacking Cdkl5 exhibit autism-like phenotypes, hyperactivity and dysregulations of the arousal system, suggesting the possibility to use these features as translational biomarkers. In this study, we tested Cdkl5 male and female mutant mice in an appetitive operant conditioning chamber to assess cognitive and motor abilities, and performed pupillometry to assess the integrity of the arousal system. Then, we evaluated the performance of artificial intelligence models to classify the genotype of the animals from the behavioral and physiological phenotype. The behavioral results show that CDD mice display impulsivity, together with low levels of cognitive flexibility and perseverative behaviors. We assessed arousal levels by simultaneously recording pupil size and locomotor activity. Pupillometry reveals in CDD mice a smaller pupil size and an impaired response to unexpected stimuli associated with hyperlocomotion, demonstrating a global defect in arousal modulation. Finally, machine learning reveals that both behavioral and pupillometry parameters can be considered good predictors of CDD. Since early diagnosis is essential to evaluate treatment outcomes and pupillary measures can be performed easily, we proposed the monitoring of pupil size as a promising biomarker for CDD.

Response Time Distributions and the Accumulation of Visual Evidence in Freely Moving Mice

Response time (RT) distributions are histograms of the observed RTs for discriminative choices, comprising a rich source of empirical information to study perceptual processes. The drift-diffusion model (DDM), a mathematical formulation predicting decision tasks, reproduces the RT distributions, contributing to our understanding of these processes from a theoretical perspective. Notably, although the mouse is a popular model system for studying brain function and behavior, little is known about mouse perceptual RT distributions, and their description from an information-accumulation perspective. We combined an automated visual discrimination task with pharmacological micro-infusions of targeted brain regions to acquire thousands of responses from freely-moving adult mice. Both choices and escape latencies showed a strong dependency on stimulus discriminability. By applying a DDM fit to our experimental data, we found that the rate of incoming evidence (drift rate) increased with stimulus contrast but was reversibly impaired when inactivating the primary visual cortex (V1). Other brain regions involved in the decision-making process, the posterior parietal cortex (PPC) and the frontal orienting fields (FOF), also influenced relevant parameters from the DDM. The large number of empirical observations that we collected for this study allowed us to achieve accurate convergence for the model fit. Therefore, changes in the experimental conditions were mirrored by changes in model parameters, suggesting the participation of relevant brain areas in the decision-making process. This approach could help interpret future studies involving attention, discrimination, and learning in adult mice.

Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model

Arousal levels strongly affect task performance. Yet, what arousal level is optimal for a task depends on its difficulty. Easy task performance peaks at higher arousal levels, whereas performance on difficult tasks displays an inverted U-shape relationship with arousal, peaking at medium arousal levels, an observation first made by Yerkes and Dodson in 1908. It is commonly proposed that the noradrenergic locus coeruleus system regulates these effects on performance through a widespread release of noradrenaline resulting in changes of cortical gain. This account, however, does not explain why performance decays with high arousal levels only in difficult, but not in simple tasks. Here, we present a mechanistic model that revisits the Yerkes-Dodson effect from a sensory perspective: a deep convolutional neural network augmented with a global gain mechanism reproduced the same interaction between arousal state and task difficulty in its performance. Investigating this model revealed that global gain states differentially modulated sensory information encoding across the processing hierarchy, which explained their differential effects on performance on simple versus difficult tasks. These findings offer a novel hierarchical sensory processing account of how, and why, arousal state affects task performance.

Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation

Understanding cortical microcircuits requires thorough measurement of physiological properties of synaptic connections formed within and between diverse subclasses of neurons. Towards this goal, we combined spatially precise optogenetic stimulation with multicellular recording to deeply characterize intralaminar and translaminar monosynaptic connections to supragranular (L2/3) neurons in the mouse visual cortex. The reliability and specificity of multiphoton optogenetic stimulation were measured across multiple Cre lines, and measurements of connectivity were verified by comparison to paired recordings and targeted patching of optically identified presynaptic cells. With a focus on translaminar pathways, excitatory and inhibitory synaptic connections from genetically defined presynaptic populations were characterized by their relative abundance, spatial profiles, strength, and short-term dynamics. Consistent with the canonical cortical microcircuit, layer 4 excitatory neurons and interneurons within L2/3 represented the most common sources of input to L2/3 pyramidal cells. More surprisingly, we also observed strong excitatory connections from layer 5 intratelencephalic neurons and potent translaminar inhibition from multiple interneuron subclasses. The hybrid approach revealed convergence to and divergence from excitatory and inhibitory neurons within and across cortical layers. Divergent excitatory connections often spanned hundreds of microns of horizontal space. In contrast, divergent inhibitory connections were more frequently measured from postsynaptic targets near each other.

Visual thalamocortical mechanisms of waking state-dependent activity and alpha oscillations

The brain exhibits distinct patterns of recurrent activity closely related to behavioral state. The neural mechanisms that underlie state-dependent activity in the awake animal are incompletely understood. Here, we demonstrate that two types of state-dependent activity, rapid arousal/movement-related signals and a 3-5 Hz alpha-like rhythm, in the primary visual cortex (V1) of mice strongly correlate with activity in the visual thalamus. Inactivation of V1 does not interrupt arousal/movement signals in most visual thalamic neurons, but it abolishes the 3-5 Hz oscillation. Silencing of the visual thalamus similarly eradicates the alpha-like rhythm and perturbs arousal/movement-related activation in V1. Intracellular recordings in thalamic neurons reveal the 3-5 Hz oscillation to be associated with rhythmic low-threshold Ca²⁺ spikes. Our results indicate that thalamocortical interactions through ionotropic signaling, together with cell-intrinsic properties of thalamocortical cells, play a crucial role in shaping state-dependent activity in V1 of the awake animal.

Probing mechanisms of visual spatial attention in mice

The role of spatial attention for visual perception has been thoroughly studied in primates, but less so in mice. Several behavioral tasks in mice reveal spatial attentional effects, with similarities to observations in primates. Pairing these tasks with large-scale, cell-type-specific techniques could enable deeper access to underlying mechanisms, and help define the utility and limitations of resolving attentional effects on visual perception and neural activity in mice. In this Review, we evaluate behavioral and neural evidence for visual spatial attention in mice; assess how specializations of the mouse visual system and behavioral repertoire impact interpretation of spatial attentional effects; and outline how several measurement and manipulation techniques in mice could precisely test and refine models of attentional modulation across scales.

Self-generation and sound intensity interactively modulate perceptual bias, but not perceptual sensitivity

The ability to distinguish self-generated stimuli from those caused by external sources is critical for all behaving organisms. Although many studies point to a sensory attenuation of self-generated stimuli, recent evidence suggests that motor actions can result in either attenuated or enhanced perceptual processing depending on the environmental context (i.e., stimulus intensity). The present study employed 2-AFC sound detection and loudness discrimination tasks to test whether sound source (self- or externally-generated) and stimulus intensity (supra- or near-threshold) interactively modulate detection ability and loudness perception. Self-generation did not affect detection and discrimination sensitivity (i.e., detection thresholds and Just Noticeable Difference, respectively). However, in the discrimination task, we observed a significant interaction between self-generation and intensity on perceptual bias (i.e. Point of Subjective Equality). Supra-threshold self-generated sounds were perceived softer than externally-generated ones, while at near-threshold intensities self-generated sounds were perceived louder than externally-generated ones. Our findings provide empirical support to recent theories on how predictions and signal intensity modulate perceptual processing, pointing to interactive effects of intensity and self-generation that seem to be driven by a biased estimate of perceived loudness, rather by changes in detection and discrimination sensitivity.

NDNF interneurons in layer 1 gain-modulate whole cortical columns according to an animal's behavioral state

Processing of sensory information in neural circuits is modulated by an animal's behavioral state, but the underlying cellular mechanisms are not well understood. Focusing on the mouse visual cortex, here we analyze the role of GABAergic interneurons that are located in layer 1 and express Ndnf (L1 NDNF INs) in the state-dependent control over sensory processing. We find that the ongoing and sensory-evoked activity of L1 NDNF INs is strongly enhanced when an animal is aroused and that L1 NDNF INs gain-modulate local excitatory neurons selectively during high-arousal states by inhibiting their apical dendrites while disinhibiting their somata via Parvalbumin-expressing interneurons. Because active NDNF INs are evenly spread in L1 and can affect excitatory neurons across all cortical layers, this indicates that the state-dependent activation of L1 NDNF INs and the subsequent shift of inhibition in excitatory neurons toward their apical dendrites gain-modulate sensory processing in whole cortical columns.

Diminished Cortical Excitation and Elevated Inhibition During Perceptual Impairments in a Mouse Model of Autism

Sensory impairments are a core feature of autism spectrum disorder (ASD). These impairments affect visual perception and have been hypothesized to arise from imbalances in cortical excitatory and inhibitory activity. There is conflicting evidence for this hypothesis from several recent studies of transgenic mouse models of ASD; crucially, none have measured activity from identified excitatory and inhibitory neurons during simultaneous impairments of sensory perception. Here, we directly recorded putative excitatory and inhibitory population spiking in primary visual cortex (V1) while simultaneously measuring visual perceptual behavior in CNTNAP2-/- knockout (KO) mice. We observed quantitative impairments in the speed, accuracy, and contrast sensitivity of visual perception in KO mice. During these perceptual impairments, stimuli evoked more firing of inhibitory neurons and less firing of excitatory neurons, with reduced neural sensitivity to contrast. In addition, pervasive 3-10 Hz oscillations in superficial cortical layers 2/3 (L2/3) of KO mice degraded predictions of behavioral performance from neural activity. Our findings show that perceptual deficits relevant to ASD may be associated with elevated cortical inhibitory activity along with diminished and aberrant excitatory population activity in L2/3, a major source of feedforward projections to higher cortical regions.

Evaluating Visual Cues Modulates Their Representation in Mouse Visual and Cingulate Cortex

Choosing an action in response to visual cues relies on cognitive processes, such as perception, evaluation, and prediction, which can modulate visual representations even at early processing stages. In the mouse, it is challenging to isolate cognitive modulations of sensory signals because concurrent overt behavior patterns, such as locomotion, can also have brainwide influences. To address this challenge, we designed a task, in which head-fixed mice had to evaluate one of two visual cues. While their global shape signaled the opportunity to earn reward, the cues provided equivalent local stimulation to receptive fields of neurons in primary visual (V1) and anterior cingulate cortex (ACC). We found that mice evaluated these cues within few hundred milliseconds. During this period, ∼30% of V1 neurons became cue-selective, with preferences for either cue being balanced across the recorded population. This selectivity emerged in response to the behavioral demands because the same neurons could not discriminate the cues in sensory control measurements. In ACC, cue evaluation affected a similar fraction of neurons; emerging selectivity, however, was stronger than in V1, and preferences in the recorded population were biased toward the cue promising reward. Such a biased selectivity regime might allow the mouse to infer the promise of reward simply by the overall level of activity. Together, these experiments isolate the impact of task demands on neural responses in mouse cerebral cortex, and document distinct neural signatures of cue evaluation in V1 and ACC.SIGNIFICANCE STATEMENT Performing a cognitive task, such as evaluating visual cues, not only recruits frontal and parietal brain regions, but also modulates sensory processing stages. We trained mice to evaluate two visual cues, and show that, during this task, ∼30% of neurons recorded in V1 became selective for either cue, although they provided equivalent visual stimulation. We also show that, during cue evaluation, mice frequently move their eyes, even under head fixation, and that ignoring systematic differences in eye position can substantially obscure the modulations seen in V1 neurons. Finally, we document that modulations are stronger in ACC, and biased toward the reward-predicting cue, suggesting a transition in the neural representation of task-relevant information across processing stages in mouse cerebral cortex.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Interstitial ions: A key regulator of state-dependent neural activity?

Throughout the nervous system, ion gradients drive fundamental processes. Yet, the roles of interstitial ions in brain functioning is largely forgotten. Emerging literature is now revitalizing this area of neuroscience by showing that interstitial cations (K+, Ca2+ and Mg2+) are not static quantities but change dynamically across states such as sleep and locomotion. In turn, these state-dependent changes are capable of sculpting neuronal activity; for example, changing the local interstitial ion composition in the cortex is sufficient for modulating the prevalence of slow-frequency neuronal oscillations, or potentiating the gain of visually evoked responses. Disturbances in interstitial ionic homeostasis may also play a central role in the pathogenesis of central nervous system diseases. For example, impairments in K+ buffering occur in a number of neurodegenerative diseases, and abnormalities in neuronal activity in disease models disappear when interstitial K+ is normalized. Here we provide an overview of the roles of interstitial ions in physiology and pathology. We propose the brain uses interstitial ion signaling as a global mechanism to coordinate its complex activity patterns, and ion homeostasis failure contributes to central nervous system diseases affecting cognitive functions and behavior.

Pupillary Dilations of Mice Performing a Vibrotactile Discrimination Task Reflect Task Engagement and Response Confidence

Pupillometry, the measure of pupil size and reactivity, has been widely used to assess cognitive processes. Changes in pupil size have been shown to correlate with various behavioral states, both externally and internally induced such as locomotion, arousal, cortical state, and decision-making processes. Besides, these pupillary responses have also been linked to the activity of neuromodulatory systems that modulate attention and perception such as the noradrenergic and cholinergic systems. Due to the extent of processes the pupil reflects, we aimed at further resolving pupillary responses in the context of behavioral state and task performance while recording pupillary transients of mice performing a vibrotactile two-alternative forced-choice task (2-AFC). We show that before the presentation of task-relevant information, pre-stimulus, pupil size differentiates between states of disengagement from task performance vs. engagement. Also, when subjects have to attend to task stimuli to attain a reward, post-stimulus, pupillary dilations exhibit a difference between correct and error responses with this difference reflecting an internal decision variable. We hypothesize that this internal decision variable relates to response confidence, the internal perception of the confidence the subject has in its choice. As opposed to this, we show that in a condition of passive performance, when the stimulus has no more task relevance due to reward being provided automatically, pupillary dilations reflect the occurrence of stimulation and reward provision but not decisional variables as under active performance. Our results provide evidence that in addition to reflecting attentiveness under task performance rather than arousal per se, pupil dilations also reflect the confidence of the subject in his ensuing response. This confidence coding is overlaid within a more pronounced pupil dilation that reflects post-decision components that are related to the response itself but not to the decision. We also provide evidence as to how different behavioral states, imposed by task demands, modulate what the pupil is reflecting, presumably showing what the underlying cognitive network is coding for.

Neuromodulation of Brain State and Behavior

Neural activity and behavior are both notoriously variable, with responses differing widely between repeated presentation of identical stimuli or trials. Recent results in humans and animals reveal that these variations are not random in their nature, but may in fact be due in large part to rapid shifts in neural, cognitive, and behavioral states. Here we review recent advances in the understanding of rapid variations in the waking state, how variations are generated, and how they modulate neural and behavioral responses in both mice and humans. We propose that the brain has an identifiable set of states through which it wanders continuously in a nonrandom fashion, owing to the activity of both ascending modulatory and fast-acting corticocortical and subcortical-cortical neural pathways. These state variations provide the backdrop upon which the brain operates, and understanding them is critical to making progress in revealing the neural mechanisms underlying cognition and behavior.

Mechanisms underlying gain modulation in the cortex

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

Local cortical desynchronization and pupil-linked arousal differentially shape brain states for optimal sensory performance

Instantaneous brain states have consequences for our sensation, perception, and behaviour. Fluctuations in arousal and neural desynchronization likely pose perceptually relevant states. However, their relationship and their relative impact on perception is unclear. We here show that, at the single-trial level in humans, local desynchronization in sensory cortex (expressed as time-series entropy) versus pupil-linked arousal differentially impact perceptual processing. While we recorded electroencephalography (EEG) and pupillometry data, stimuli of a demanding auditory discrimination task were presented into states of high or low desynchronization of auditory cortex via a real-time closed-loop setup. Desynchronization and arousal distinctly influenced stimulus-evoked activity and shaped behaviour displaying an inverted u-shaped relationship: States of intermediate desynchronization elicited minimal response bias and fastest responses, while states of intermediate arousal gave rise to highest response sensitivity. Our results speak to a model in which independent states of local desynchronization and global arousal jointly optimise sensory processing and performance.