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The brain’s connectome provides the scaffold for canonical neural computations. However, a comparison of connectivity studies in the mouse primary visual cortex (V1) reveals that the average number and strength of connections between specific neuron types can vary. Can variability in V1 connectivity measurements coexist with canonical neural computations? We developed a theory-driven approach to deduce V1 network connectivity from visual responses in mouse V1 and visual thalamus (dLGN). Our method revealed that the same recorded visual responses were captured by multiple connectivity configurations. Remarkably, the magnitude and selectivity of connectivity weights followed a specific order across most of the inferred connectivity configurations. We argue that this order stems from the specific shapes of the recorded contrast response functions and contrast invariance of orientation tuning. Remarkably, despite variability across connectivity studies, connectivity weights computed from individual published connectivity reports followed the order we identified with our method, suggesting that the relations between the weights, rather than their magnitudes, represent a connectivity motif supporting canonical V1 computations.

Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.

In this issue of Neuron and in Cell Reports, Fratzl et al. (2021) and Salay and Huberman (2021) identify the ventral lateral geniculate nucleus (vLGN) of the thalamus as a key regulator for adjusting defensive behaviors according to the level of perceived visual threat.

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 STATEMENTPerforming 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.

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the “stand-alone” system identification model, it also produced more biologically plausible filters, meaning that they more closely resembled neural representation in early visual systems. We found these results applied to retinal responses to different artificial stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. The benefit of natural scene statistics became marginal, however, for predicting the responses to natural movies. In summary, our results indicate that efficiently encoding environmental inputs can improve system identification models, at least for noise stimuli, and point to the benefit of probing the visual system with naturalistic stimuli.

Across vertebrates, eye movements serve the dual purpose of image stabilization during head or body movement, and gaze relocation. A new study has measured head and bilateral eye movements in freely moving mice, providing a detailed characterization of dynamic gaze behavior.

Visual behavior is based on the concerted activity of neurons in visual areas, where sensory signals are integrated with top-down information. In the past decade, the advent of new tools, such as functional imaging of populations of identified single neurons, high-density electrophysiology, virus-assisted circuit mapping, and precisely timed, cell-type specific manipulations, has advanced our understanding of the neuronal microcircuits underlying visual behavior. Studies in head-fixed mice, where such tools can routinely be applied, begin to provide new insights into the neural code of primary visual cortex (V1) underlying visual perception, and the micro-circuits of attention, predictive processing, and learning.

SummaryThe way in which sensory systems encode information, even at early processing stages, is modulated according to the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural variables to a single “level” of arousal, defined using a behavioral variable such as locomotion or pupil size. Here, we extend the notion of arousal-related modulation by showing that multifaceted aspects of pupil size dynamics can predict changes in neural activity. Specifically, we show that the phases of pupil size fluctuations occurring over multiple timescales modulate firing mode in the dorsal lateral geniculate nucleus (dLGN) of mice. Increased firing rates, driven by a preponderance of tonic spiking, occurred during pupil dilations, while burst spikes preferentially occurred during contractions. We further find that these activity modulations could not be explained solely by pupil size per se, or by transitions between overt locomotion and quiescence, and extend to periods of patterned stimulus viewing. We conclude that dLGN spiking activity is modulated by pupil-indexed arousal processes on various timescales, with implications for the determination of arousal-related cortical rhythms, and for the transfer of sensory information to the cortex.

Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal's specific visual environment by capturing natural scenes, characterizing their statistical regularities, and using them to probe visual computations. Mice, a prominent visual system model, have salient visual specializations, being dichromatic with enhanced sensitivity to green and UV in the dorsal and ventral retina, respectively. However, the characteristics of their visual environment that likely have driven these adaptations are rarely considered. Here, we built a UV-green-sensitive camera to record footage from mouse habitats. This footage is publicly available as a resource for mouse vision research. We found chromatic contrast to greatly diverge in the upper, but not the lower, visual field. Moreover, training a convolutional autoencoder on upper, but not lower, visual field scenes was sufficient for the emergence of color-opponent filters, suggesting that this environmental difference might have driven superior chromatic opponency in the ventral mouse retina, supporting color discrimination in the upper visual field. Furthermore, the upper visual field was biased toward dark UV contrasts, paralleled by more light-offset-sensitive ganglion cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution.

Pupil size is a commonly used proxy for waking brain states such as arousal, and has been related to activity modulations in cortical sensory areas. Here, we asked whether the dorsolateral geniculate nucleus (dLGN), which provides sensory input to the visual cortex, is modulated by pupil-indexed arousal. Observing that the pupil oscillates at multiple timescales, we developed a method to show that the spiking mode of the dLGN is predicted by pupil size oscillations over several of these timescales. Overall, we found that tonic spikes preferentially occurred during pupil dilation, while bursts occurred during contraction. These preferences could not be explained solely by pupil size per se or by the locomotion of the animal, and were also present during periods of stimulus viewing. We conclude that dLGN spiking activity is modulated by pupil-indexed arousal processes on various timescales, influencing the mode in which sensory signals are passed on to the cortex.

En route from the retina to the cortex, visual information passes through the dorsolateral geniculate nucleus (dLGN) of the thalamus, where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here, we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large RFs, show little surround suppression and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in the dLGN, likely via recruitment of neurons in the visTRN.

En route from retina to cortex, visual information passes through the dorsolateral geniculate nucleus of the thalamus (dLGN), where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large receptive fields, show little surround suppression, and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in dLGN, likely via recruitment of neurons in visTRN.

Zusammenfassung Die Verarbeitung sensorischer Information kann sowohl im Kortex als auch im Thalamus durch den Verhaltenskontext, z. B. durch aktive Fortbewegung, moduliert werden. Solch aktives Verhalten verbessert die Kodierung sensorischer Reize und die Wahrnehmung, besonders während Aktivitäten von moderater Intensität. Der Modulation sensorischer Verarbeitung durch Fortbewegung scheint eine Kombination von Mechanismen zugrunde zu liegen, unter anderem neuromodulatorische Einflüsse, die Aktivität spezifischer, inhibitorischer Interneurone, sowie top-down- oder motorische Rückprojektionen. Neue experimentelle Ansätze, die es Mäusen erlauben, sich trotz Kopffixation auf Laufbällen oder –bändern fortzubewegen, ermöglichte es in den letzten Jahren, die neuronalen Schaltkreise und zellulären Elemente, die der Modulation durch Verhaltenskontext zugrunde liegen, eingehend zu untersuchen. Dieser Übersichtsartikel fasst den momentanen Stand dieser Studien zusammen und beleuchtet wichtige offenen Fragen.

Processing of sensory information can be modulated in both cortex and thalamus by behavioral context, such as locomotion. During active behaviors, coding of sensory stimuli and perception are improved, in particular during physical activity of moderate intensity. These locomotion-related modulations seem to arise from a combination of mechanisms, including neuromodulation, the recruitment of inhibitory interneurons, and specific top-down or motor-related inputs. The application of new experimental methods in mice during walking under head-fixation on treadmills made it possible to study the circuit and cellular basis underlying modulations by behavioral context with unprecedented detail. This article reviews the current state of these studies and highlights some important open questions.

Neurons in the dorsolateral geniculate nucleus (dLGN) of the thalamus receive a substantial proportion of modulatory inputs from corticothalamic (CT) feedback and brain stem nuclei. Hypothesizing that these modulatory influences might be differentially engaged depending on the visual stimulus and behavioral state, we performed in vivo extracellular recordings from mouse dLGN while optogenetically suppressing CT feedback and monitoring behavioral state by locomotion and pupil dilation. For naturalistic movie clips, we found CT feedback to consistently increase dLGN response gain and promote tonic firing. In contrast, for gratings, CT feedback effects on firing rates were mixed. For both stimulus types, the neural signatures of CT feedback closely resembled those of behavioral state, yet effects of behavioral state on responses to movies persisted even when CT feedback was suppressed. We conclude that CT feedback modulates visual information on its way to cortex in a stimulus-dependent manner, but largely independently of behavioral state.

Visual processing along the primate ventral stream takes place in a hierarchy of areas, characterized by an increase in both complexity of neuronal preferences and invariance to changes of low-level stimulus attributes. A basic type of invariance is form-cue invariance, where neurons have similar preferences in response to first-order stimuli, defined by changes in luminance, and global features of second-order stimuli, defined by changes in texture or contrast. Whether in mice, a now popular model system for early visual processing, visual perception can be guided by second-order stimuli is currently unknown. Here, we probed mouse visual perception and neural responses in areas V1 and LM using various types of second-order, contrast-modulated gratings with static noise carriers. These gratings differ in their spatial frequency composition and thus in their ability to invoke first-order mechanisms exploiting local luminance features. We show that mice can transfer learning of a coarse orientation discrimination task involving first-order, luminance-modulated gratings to the contrast-modulated gratings, albeit with markedly reduced discrimination performance. Consistent with these behavioral results, we demonstrate that neurons in area V1 and LM are less responsive and less selective to contrast-modulated than to luminance-modulated gratings, but respond with broadly similar preferred orientations. We conclude that mice can, at least in a rudimentary form, use second-order stimuli to guide visual perception.SIGNIFICANCE STATEMENTTo extract object boundaries in natural scenes, the primate visual system does not only rely on differences in local luminance but can also take into account differences in texture or contrast. Whether the mouse, which has a much simpler visual system, can use such second-order information to guide visual perception is unknown. Here we tested mouse perception of second-order, contrast-defined stimuli and measured their neural representations in two areas of visual cortex. We find that mice can use contrast-defined stimuli to guide visual perception, although behavioral performance and neural representations were less robust than for luminance-defined stimuli. These findings shed light on basic steps of feature extraction along the mouse visual cortical hierarchy, which may ultimately lead to object recognition.

Aims The aim of this study was to assess the clinical value of biomarkers to identify TAVI patients at high risk for adverse outcome, to assess whether these biomarkers provide prognostic information beyond that of established clinical risk scores, and to assess whether a combined multi-marker strategy can improve clinical decision making. Methods and results In 683 TAVI patients, biomarkers reflecting various pathophysiologic systems were measured before TAVI. The primary endpoint was one-year all-cause mortality. Other outcomes were recorded according to the VARC-2 criteria. Thirty-day and one-year mortality was 2.9% and 12.0%, respectively. Non-survivors at one year had higher risk scores and increased median biomarker levels. Logistic EuroSCORE in combination with hs-CRP had the highest predictive value for 30-day (AUC 0.740 [95% CI: 0.667-0.812], p=0.1117) and one-year mortality (AUC 0.631 [95% CI: 0.569-0.693], p=0.0403). In multivariate regression analysis, logistic EuroSCORE in combination with hs-CRP showed the strongest association with one-year mortality. Combinations of increasing medians of logistic EuroSCORE results and hs-CRP levels allowed the stratification of the TAVI patients into subgroups with one-year mortality rates ranging from 6.6% up to 18.2%. Conclusions hs-CRP alongside the logistic EuroSCORE was an independent predictor of one-year all-cause mortality in TAVI patients. A combination of both might help to predict procedural risk and outcome better.

SUMMARYIn the mouse, the parallel output of more than 30 functional types of retinal ganglion cells (RGCs) serves as the basis for all further visual processing. Little is known about how the representation of visual information changes between the retina and the dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay station between the retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate the dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that the responses of dLGN neurons could be predicted as a linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives input from a diverse population of RGCs with limited functional convergence.

To reduce statistical redundancy of natural inputs and increase the sparseness of coding, neurons in primary visual cortex (V1) show tuning for stimulus size and surround suppression. This integration of spatial information is a fundamental, context-dependent neural operation involving extensive neural circuits that span across all cortical layers of a V1 column, and reflects both feedforward and feedback processing. However, how spatial integration is dynamically coordinated across cortical layers remains poorly understood. We recorded single- and multiunit activity and local field potentials across V1 layers of awake mice (both sexes) while they viewed stimuli of varying size and used dynamic Bayesian model comparisons to identify when laminar activity and interlaminar functional interactions showed surround suppression, the hallmark of spatial integration. We found that surround suppression is strongest in layer 3 (L3) and L4 activity, where suppression is established within ∼10 ms after response onset, and receptive fields dynamically sharpen while suppression strength increases. Importantly, we also found that specific directed functional connections were strongest for intermediate stimulus sizes and suppressed for larger ones, particularly for connections from L3 targeting L5 and L1. Together, the results shed light on the different functional roles of cortical layers in spatial integration and on how L3 dynamically coordinates activity across a cortical column depending on spatial context.SIGNIFICANCE STATEMENTNeurons in primary visual cortex (V1) show tuning for stimulus size, where responses to stimuli exceeding the receptive field can be suppressed (surround suppression). We demonstrate that functional connectivity between V1 layers can also have a surround-suppressed profile. A particularly prominent role seems to have layer 3, the functional connections to layers 5 and 1 of which are strongest for stimuli of optimal size and decreased for large stimuli. Our results therefore point toward a key role of layer 3 in coordinating activity across the cortical column according to spatial context.

Working memory is influenced by past experiences. An area of the rat brain has now been identified that represents recent history — silencing this area can remove biases from working memory and decision-making. Working memory is influenced by past experiences. An area of the rat brain has now been identified that represents recent history — silencing this area can remove biases from working memory and decision-making.

In the mouse, the parallel output of more than 30 functional types of retinal ganglion cells (RGCs) serves as the basis for all further visual processing. Little is known about how the representation of visual information changes between the retina and the dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay station between the retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate the dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that the responses of dLGN neurons could be predicted as a linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives input from a diverse population of RGCs with limited functional convergence.

In the past years, the mouse has taken centre stage as a model system for studying the neural circuits of visual processing and perception. This chapter provides background information on the mouse visual system, including the retina, superior colliculus, visual thalamus, primary visual cortex and extrastriate areas. Where relevant, circuits and mechanisms discovered in the mouse are compared to findings in larger mammals. Finally, this chapter introduces key findings regarding the role of visual cortex in perception. It concludes by exploring the interareal networks and mechanisms, by which top-down signals shape sensory processing in primary visual cortex and behaviour.

Mouse vision is based on the parallel output of more than 30 functional types of retinal ganglion cells (RGCs). Little is known about how representations of visual information change between retina and dorsolateral geniculate nucleus (dLGN) of the thalamus, the main relay between retina and cortex. Here, we functionally characterized responses of retrogradely labeled dLGN-projecting RGCs and dLGN neurons to the same set of visual stimuli. We found that many of the previously identified functional RGC types innervate dLGN, which maintained a high degree of functional diversity. Using a linear model to assess functional connectivity between RGC types and dLGN neurons, we found that responses of dLGN neurons could be predicted as linear combination of inputs from on average five RGC types, but only two of those had the strongest functional impact. Thus, mouse dLGN receives functional input from a diverse population of RGC types with limited convergence.

A fundamental property of visual cortex is to enhance the representation of those stimuli that are relevant for behavior, but it remains poorly understood how such enhanced representations arise during learning. Using classical conditioning in adult mice of either sex, we show that orientation discrimination is learned in a sequence of distinct behavioral stages, in which animals first rely on stimulus appearance before exploiting its orientation to guide behavior. After confirming that orientation discrimination under classical conditioning requires primary visual cortex (V1), we measured, during learning, response properties of V1 neurons. Learning improved neural discriminability, sharpened orientation tuning, and led to higher contrast sensitivity. Remarkably, these learning-related improvements in the V1 representation were fully expressed before successful orientation discrimination was evident in the animals' behavior. We propose that V1 plays a key role early in discrimination learning to enhance behaviorally relevant sensory information. SIGNIFICANCE STATEMENT Decades of research have documented that responses of neurons in visual cortex can reflect the behavioral relevance of visual information. The behavioral relevance of any stimulus needs to be learned, though, and little is known how visual sensory processing changes, as the significance of a stimulus becomes clear. Here, we trained mice to discriminate two visual stimuli, precisely quantified when learning happened, and measured, during learning, the neural representation of these stimuli in V1. We observed learning-related improvements in V1 processing, which were fully expressed before discrimination was evident in the animals' behavior. These findings indicate that sensory and behavioral improvements can follow different time courses and point toward a key role of V1 at early stages in discrimination learning.

SummaryPrimary visual cortex (V1) exhibits two types of gamma rhythm: broadband activity in the 30–90 Hz range, and a narrowband oscillation seen in mice at frequencies close to 60 Hz. We investigated the sources of the narrowband gamma oscillation, the factors modulating its strength, and its relationship to broadband gamma activity. Narrowband and broadband gamma power were uncorrelated. Increasing visual contrast had opposite effects on the two rhythms: it increased broadband activity, but suppressed the narrowband oscillation. The narrowband oscillation was strongest in layer 4, and was mediated primarily by excitatory currents entrained by the synchronous, rhythmic firing of neurons in the lateral geniculate nucleus (LGN). The power and peak frequency of the narrowband gamma oscillation increased with light intensity. Silencing the cortex optogenetically did not affect narrowband oscillation in either LGN firing or cortical excitatory currents, suggesting that this oscillation reflects unidirectional flow of signals from thalamus to cortex.Highlights•Local field potential in mouse primary visual cortex exhibits a pronounced narrowband gamma oscillation close to 60 Hz.•Narrowband gamma is highest in the thalamorecipient layer 4•Narrowband gamma increases with light intensity and arousal state, and is suppressed by visual contrast.•Lateral geniculate nucleus neurons fire synchronously at the narrowband gamma frequency, independent of V1 activity.

Summary Primary visual cortex exhibits two types of gamma rhythm: broadband activity in the 30–90 Hz range and a narrowband oscillation seen in mice at frequencies close to 60 Hz. We investigated the sources of the narrowband gamma oscillation, the factors modulating its strength, and its relationship to broadband gamma activity. Narrowband and broadband gamma power were uncorrelated. Increasing visual contrast had opposite effects on the two rhythms: it increased broadband activity, but suppressed the narrowband oscillation. The narrowband oscillation was strongest in layer 4 and was mediated primarily by excitatory currents entrained by the synchronous, rhythmic firing of neurons in the lateral geniculate nucleus (LGN). The power and peak frequency of the narrowband gamma oscillation increased with light intensity. Silencing the cortex optogenetically did not abolish the narrowband oscillation in either LGN firing or cortical excitatory currents, suggesting that this oscillation reflects unidirectional flow of signals from thalamus to cortex., Highlights • Mouse V1 exhibits a pronounced narrowband gamma oscillation close to 60 Hz • This oscillation is strongest in layer 4 and specific to excitatory currents • It increases with arousal and light intensity and decreases with visual contrast • It is seen in lateral geniculate neurons, regardless of V1 activity, Saleem et al. discover that the narrowband gamma oscillation close to 60 Hz prevalent in the mouse visual cortex is inherited from the visual thalamus. The oscillation is enhanced by arousal and light intensity, and suppressed by visual contrast.

The mouse is becoming a key species for research on the neural circuits of the early visual system. To relate such circuits to perception, one must measure visually guided behavior and ask how it depends on fundamental stimulus attributes such as visual contrast. Using operant conditioning, we trained mice to detect visual contrast in a two-alternative forced-choice task. After 3–4 weeks of training, mice performed hundreds of trials in each session. Numerous sessions yielded high-quality psychometric curves from which we inferred measures of contrast sensitivity. In multiple sessions, however, choices were influenced not only by contrast, but also by estimates of reward value and by irrelevant factors such as recent failures and rewards. This behavior was captured by a generalized linear model involving not only the visual responses to the current stimulus but also a bias term and history terms depending on the outcome of the previous trial. We compared the behavioral performance of the mice to predictions of a simple decoder applied to neural responses measured in primary visual cortex of awake mice during passive viewing. The decoder performed better than the animal, suggesting that mice might not use optimally the information contained in the activity of visual cortex.

GABAAinhibition is thought to play multiple roles in sensory cortex, such as controlling responsiveness and sensitivity, sharpening selectivity, and mediating competitive interactions. To test these proposals, we recorded in cat primary visual cortex (V1) after local iontophoresis of gabazine, the selective GABAAantagonist. Gabazine increased responsiveness by as much as 300%. It slightly decreased selectivity for stimulus orientation and direction, often by raising responses to all orientations. Strikingly, gabazine affected neither contrast sensitivity nor cross-orientation suppression, the competition seen when stimuli of different orientation are superimposed. These results were captured by a simple model in which GABAAinhibition has the same selectivity as excitation and keeps responses to unwanted stimuli below threshold. We conclude that GABAAinhibition in V1 helps enhance stimulus selectivity but is not responsible for competition among superimposed stimuli. It controls the sensitivity of V1 neurons by adjusting their response gain, without affecting their input gain.

In a recent ERP study of inhibitory control using the Stop-Signal Task [Pliszka, S., Liotti, M., Woldorff, M. (2000). Inhibitory control in children with attention-deficit/hyperactivity disorder: Event-related potentials identify the processing component and timing of an impaired right-frontal response-inhibition mechanism. Biological Psychiatry, 48, 238-246], we showed that in normal children (age 10-12 years) the Stop Signals elicited a robust, right-frontal-maximal N200 (latency approximately 200 ms) that was strongly reduced in children with ADHD. To further investigate the mechanisms of response inhibition, this paradigm was applied to 11 healthy young adults. To better distinguish response-inhibition-related activity from early attentional effects, a "Stop-Signal-Irrelevant" condition was added, in which subjects performed the task while ignoring the Stop Signals. In the Stop-Signal-Relevant condition, the right frontal N200 to the Stop Signals was larger for Successful inhibition (SI) than for Failed inhibition (FI) trials. The timing and distribution of this effect was strikingly similar to that of the right-frontal ADHD deficit reported in Pliszka et al. (2000), supporting this activity being related to successful normal inhibitory control processes. In contrast, a posterior N200 was larger for Stop-Relevant than for Stop-Irrelevant trials, likely reflecting enhanced early sensory attention to the Stop Signals when relevant. Two longer-latency failure-specific ERP effects were also observed: a greater frontopolar negative wave (370-450 ms) to Failed than Successful inhibitions, and a greater parietal positive slow wave (450-650 ms) for Failed inhibitions than ignore-stop trials, likely reflecting differential recruitment of error detection and correction mechanisms following Failed attempts to inhibit a response.

Attending to a stimulus is known to enhance the neural responses to that stimulus. Recent experiments on visual attention have shown that this modulation can have object-based characteristics, such that, when certain parts of a visual object are attended, other parts automatically also receive enhanced processing. Here, we investigated whether visual attention can modulate neural responses to other components of a multisensory object defined by synchronous, but spatially disparate, auditory and visual stimuli. The audiovisual integration of such multisensory stimuli typically leads to mislocalization of the sound toward the visual stimulus (ventriloquism illusion). Using event-related potentials and functional MRI, we found that the brain's response to task-irrelevant sounds occurring synchronously with a visual stimulus from a different location was larger when that accompanying visual stimulus was attended versus unattended. The event-related potential effect consisted of sustained, frontally distributed, brain activity that emerged relatively late in processing, an effect resembling attention-related enhancements seen at earlier latencies during intramodal auditory attention. Moreover, the functional MRI data confirmed that the effect included specific enhancement of activity in auditory cortex. These findings indicate that attention to one sensory modality can spread to encompass simultaneous signals from another modality, even when they are task-irrelevant and from a different location. This cross-modal attentional spread appears to reflect an object-based, late selection process wherein spatially discrepant auditory stimulation is grouped with synchronous attended visual input into a multisensory object, resulting in the auditory information being pulled into the attentional spotlight and bestowed with enhanced processing.

A major difficulty in fast-rate event-related fMRI experiments is the extensive overlap from adjacent trials in the stimulus sequence. One approach to address this problem is to include "no-stim" or "null" events as a trial type. These are randomized as if they were true stimulus events but no stimulus actually occurs. Assuming that no response is elicited by the null events, their time-locked average reflects only the averaged overlap. Thus, contrasting the averages for the other trial types versus the null-event average subtracts out the overlap, enabling the extraction of the response functions for these other trial types. ERP studies, however, have indicated that an endogenous brain response, the omitted stimulus response (OSR), can be evoked by a missing event in a stream of regularly occurring stimuli. To the extent that this response is elicited by null events in an event-related fMRI experiment, the null-event subtraction or contrast would falsely introduce the inverse of the OSR into the averaged responses to the other trial types. Using high-density ERP recordings, we investigated the effect of different percentages of omitted stimuli (11, 22, and 33%) on the auditory OSR at stimulus rates of one event per second or one event per 2 s. Significant OSRs were found for each percentage in the 1-s condition as well as in the 11% 2-s condition. The responses consisted of an early posterior negative wave (180-280 ms) followed by a larger anterior positive wave. These results have important implications for fast-rate fMRI designs, while also providing new data on the brain's response to omitted stimuli.

The representation of visual objects in short-term memory has been shown to be related to increased gamma-band activity in the electroencephalogram. Using a similar paradigm, we investigated oscillatory magnetoencephalographic activity in human subjects during a delayed matching-to-sample task requiring working memory of auditory spatial information. The memory task involved same-different judgments about the lateralization angle of pairs of filtered noise stimuli (S1 and S2) separated by 800 msec delays of background noise. This was compared with a control condition requiring the detection of a possible change in the background noise volume appearing instead of S2 (volume task). Statistical probability mapping revealed increased spectral activity at 59 Hz over left parietal cortex during the delay phase of the memory condition. In addition, 59 Hz coherence was enhanced between left parietal and right frontal sensors. During the end of the delay and during the presentation of S2, enhanced gamma-band activity at 67 Hz was observed over right frontal and later over midline parietal areas. In contrast, the volume task was characterized by increased left inferior frontotemporal 59 Hz spectral amplitude after S1. Apparently representation of the spatial position of a sound source is associated both with synchronization of networks in parietal areas involved in the auditory dorsal stream and with increased coupling between networks serving representation of audiospatial information and frontal executive systems. The comparison with S2 seemed to activate frontal and parietal neuronal ensembles. Gamma-band activity during the volume task may reflect auditory pattern encoding in auditory ventral stream areas.

Numerous studies have revealed traveling waves of activity in sensory cortex, both following sensory stimulation and during ongoing activity. We contributed to this body of work by measuring the spike-triggered average of the local field potential (stLFP) at multiple concurrent locations (Nauhaus et al., 2009) in the visual cortex of anesthetized cats and macaques. We found the stLFP to be progressively delayed at increasing distances from the site of the triggering spikes, and interpreted this as a traveling wave of depolarization originating from that site. Our results were criticized, however, on two grounds. First, a study using the same recording techniques in the visual cortex of awake macaques reported an apparent lack of traveling waves, and proposed that traveling waves could arise artifactually from excessive filtering of the field potentials (Ray and Maunsell, 2011). Second, the interpretability of the stLFP was questioned (Kenneth Miller, personal communication), as the stLFP must reflect not only interactions between spike trains and field potentials, but also correlations within and across the spike trains. Here, we show that our data and interpretation are not imperiled by these criticisms. We reanalyzed our field potentials to remove any possible artifact due to filtering and to discount the effects of correlations within and across the triggering spike trains. In both cases, we found that the traveling waves were still present. In fact, closer inspection of Ray and Maunsell's (2011) data from awake cortex shows that they do agree with ours, as they contain clear evidence for traveling waves.

Dynamically shifting attention between behaviorally relevant stimuli in the environment is a key condition for successful adaptive behavior. Here, we investigated how exogenous (reflexive) and endogenous (voluntary) shifts of visual spatial attention interact to modulate activity of single neurons in extrastriate area MT. We used a double-cueing paradigm, in which the first cue instructed two macaque monkeys to covertly attend to one of three moving random dot patterns until a second cue, whose unpredictable onset exogenously captured attention, either signaled to shift or maintain the current focus of attention. The neuronal activity revealed correlates of both exogenous and endogenous attention, which could be well distinguished by their characteristic temporal dynamics. The earliest effect was a transient interruption of the focus of endogenous attention by the onset of the second cue. The neuronal signature of this exogenous capture of attention was a short-latency decrease of responses to the stimulus attended so far. About 70 ms later, the influence of exogenous attention leveled off, which was reflected in two concurrent processes: responses to the newly cued stimulus continuously increased because of allocation of endogenous attention, while, surprisingly, there was also a gradual rebound of attentional enhancement of the previously relevant stimulus. Only after an additional 110 ms did endogenous disengagement of attention from this previously relevant stimulus become evident. These patterns of attentional modulation can be most parsimoniously explained by assuming two distinct attentional mechanisms drawing on the same capacity-limited system, with exogenous attention having a much faster time course than endogenous attention.

Neurons in visual cortex are linked by an extensive network of lateral connections. To study the effect of these connections on neural responses, we recorded spikes and local field potentials (LFPs) from multi-electrode arrays that were implanted in monkey and cat primary visual cortex. Spikes at each location generated outward traveling LFP waves. When the visual stimulus was absent or had low contrast, these LFP waves had large amplitudes and traveled over long distances. Their effect was strong: LFP traces at any site could be predicted by the superposition of waves that were evoked by spiking in a approximately 1.5-mm radius. As stimulus contrast increased, both the magnitude and the distance traveled by the waves progressively decreased. We conclude that the relative weight of feedforward and lateral inputs in visual cortex is not fixed, but rather depends on stimulus contrast. Lateral connections dominate at low contrast, when spatial integration of signals is perhaps most beneficial.

The temporal dynamics of the effects of lateralized visual selective attention within the lower visual field were studied with the combined application of event-related potentials (ERPs) and positron emission tomography (15O PET). Bilateral stimuli were rapidly presented to the lower visual field while subjects either passively viewed them or covertly attended to a designated side to detect occasional targets. Lateralized attention resulted in strongly enhanced PET activity in contralateral dorsal occipital cortex, while ERPs showed an enhanced positivity (P1 effect, 80-160 ms) for all stimuli (both non-targets and targets) over contralateral occipital scalp. Dipole modeling seeded by the dorsal occipital PET foci yielded an excellent fit for the peak P1 attention effect. However, more detailed ERP modeling throughout the P1 latency window (90-160 ms) suggested a spatial-temporal movement of the attention-related enhancement that roughly paralleled the shape of the dorsal occipital PET attention-related activations-likely reflecting the sequential attention-related enhancement of early visual cortical areas. Lateralized spatial attention also resulted in a longer-latency contralateral enhanced negativity (N2 effect, 230-280 ms) with a highly similar distribution to the earlier P1 effect. Dipole modeling seeded by the same dorsal occipital PET foci also yielded an excellent fit. This pattern of results provides evidence for re-entrance of attention-enhanced activation to the same retinotopically organized region of dorsal extrastriate cortex. Finally, target stimuli in the attended location elicited an additional prolonged enhancement of the longer-latency negativity over contralateral occipital cortex. The combination of PET activation and dipole modeling suggested contribution from a ventral-occipital generator to this target-related activity.

In sensory neurophysiology, reverse correlation analyses have advanced our understanding of the spatio-temporal structure of receptive fields (RFs) and the tuning properties of individual neurons. Here, we used a psychophysical variant of the motion reverse correlation technique to investigate how visual selective attention influences human perceptual tuning curves for direction of motion. Direction tuning functions were computed by reverse correlating speeded target-present responses of human observers with a random sequence of brief, fully coherent motion impulses. We found that attention enhanced the amplitude of perceptual tuning curves for direction of motion, while tuning width remained unaffected. Furthermore, the full direction tuning profile across time could be well fitted by a separable model of direction and temporal tuning. Attention enhanced both the direction tuning and its temporal profile, without shifts or changes in shape. Thus, attention exerts a multiplicative effect on human perceptual tuning curves for direction of motion. An analysis of second-order correlations revealed a boost in the likelihood of responses to the target direction when it was followed by a motion impulse in the opposite direction. This perceptual effect might be mediated by biphasic neurons that are preferentially activated by a rapid succession of opposite motion directions.

In four variants of a speeded target detection task, we investigated the processing of color and motion signals in the human visual system. Participants were required to attend to both a particular color and direction of motion in moving random dot patterns (RDPs) and to report the appearance of the designated targets. Throughout, reaction times (RTs) to simultaneous presentations of color and direction targets were too fast to be reconciled with models proposing separate and independent processing of such stimulus dimensions. Thus, the data provide behavioral evidence for an integration of color and motion signals. This integration occurred even across superimposed surfaces in a transparent motion stimulus and also across spatial locations, arguing against object- and location-based accounts of attentional selection in such a task. Overall, the pattern of results can be best explained by feature-based mechanisms of visual attention.