Contrast-dependent orientation discrimination in the mouse

Neural ensembles in the murine medial prefrontal cortex process distinct information during visual perceptual learning

BACKGROUND

Perceptual learning refers to an augmentation of an organism's ability to respond to external stimuli, which has been described in most sensory modalities. Visual perceptual learning (VPL) is a manifestation of plasticity in visual information processing that occurs in the adult brain, and can be used to ameliorate the ability of patients with visual defects mainly based on an improvement of detection or discrimination of features in visual tasks. While some brain regions such as the primary visual cortex have been described to participate in VPL, the way more general high-level cognitive brain areas are involved in this process remains unclear. Here, we showed that the medial prefrontal cortex (mPFC) was essential for both the training and maintenance processes of VPL in mouse models.

RESULTS

We built a new VPL model in a custom-designed training chamber to enable the utilization of miniScopes when mice freely executed the VPL task. We found that pyramidal neurons in the mPFC participate in both the training process and maintenance of VPL. By recording the calcium activity of mPFC pyramidal neurons while mice freely executed the task, distinct ON and OFF neural ensembles tuned to different behaviors were identified, which might encode different cognitive information. Decoding analysis showed that mouse behaviors could be well predicted using the activity of each ON ensemble. Furthermore, VPL recruited more reward-related components in the mPFC.

CONCLUSION

We revealed the neural mechanism underlying vision improvement following VPL and identify distinct ON and OFF neural ensembles in the mPFC that tuned to different information during visual perceptual training. These results uncover an important role of the mPFC in VPL, with more reward-related components being also involved, and pave the way for future clarification of the reward signal coding rules in VPL.

Reliable, Fast and Stable Contrast Response Function Estimation

A study was conducted to determine stable cortical contrast response functions (CRFs) accurately and repeatedly in the shortest possible experimentation time. The method consisted of searching for experimental temporal aspects (number and duration of trials and number and distribution of contrasts used) with a model based on inhomogeneous Poisson spike trains to varying contrast levels. The set of values providing both short experimental duration and maximizing fit of the CRFs were saved, and then tested on cats' visual cortical neurons. Our analysis revealed that 4 sets of parameters with less or equal to 6 experimental visual contrasts satisfied our premise of obtaining good CRFs' performance in a short recording period, in which the number of trials seems to be the experimental condition that stabilizes the fit.

Emergence of probabilistic representation in the neural network of primary visual cortex

During the early development of the mammalian visual system, the distribution of neuronal preferred orientations in the primary visual cortex (V1) gradually shifts to match major orientation features of the environment, achieving its optimal representation. By combining computational modeling and electrophysiological recording, we provide a circuit plasticity mechanism that underlies the developmental emergence of such matched representation in the visual cortical network. Specifically, in a canonical circuit of densely-interconnected pyramidal cells and inhibitory parvalbumin-expressing (PV+) fast-spiking interneurons in V1 layer 2/3, our model successfully simulates the experimental observations and further reveals that the nonuniform inhibition plays a key role in shaping the network representation through spike timing-dependent plasticity. The experimental results suggest that PV + interneurons in V1 are capable of providing nonuniform inhibition shortly after vision onset. Our study elucidates a circuit mechanism for acquisition of prior knowledge of environment for optimal inference in sensory neural systems

•

Computational and experimental methods are combined to representation in mice V1

•

Nonuniform inhibition plays a key role in shaping the network representation

•

PV + interneurons provide nonuniform inhibition shortly after vision onset

Dynamics of Visual Perceptual Decision-Making in Freely Behaving Mice

The temporal dynamics of perceptual decisions offer a key window into the cognitive processes contributing to decision-making. Investigating perceptual dynamics in a genetically tractable animal model can facilitate the subsequent unpacking of the underlying neural mechanisms. Here, we investigated the time course as well as fundamental psychophysical constants governing visual perceptual decision-making in freely behaving mice. We did so by analyzing response accuracy against reaction time (RT), i.e., conditional accuracy, in a series of two-alternative forced choice (2-AFC) orientation discrimination tasks in which we varied target size, luminance, duration, and presence of a foil. Our results quantified two distinct stages in the time course of mouse visual decision-making: a "sensory encoding" stage in which conditional accuracy exhibits a classic trade-off with response speed, and a subsequent "short-term memory (STM)-dependent" stage in which conditional accuracy exhibits a classic asymptotic decay following stimulus offset. We estimated the duration of visual sensory encoding as 200-320 ms across tasks, the lower bound of the duration of STM as ∼1700 ms, and the briefest duration of visual stimulus input that is informative as ≤50 ms. Separately, by varying stimulus onset delay, we demonstrated that the conditional accuracy function (CAF) and RT distribution can be independently modulated, and found that the duration for which mice naturally withhold from responding is a quantitative metric of impulsivity. Taken together, our results establish a quantitative foundation for investigating the neural circuit bases of visual decision dynamics in mice.

Dynamics of Visual Perceptual Decision-Making in Freely Behaving Mice

The temporal dynamics of perceptual decisions offer a key window into the cognitive processes contributing to decision-making. Investigating perceptual dynamics in a genetically tractable animal model can facilitate the subsequent unpacking of the underlying neural mechanisms. Here, we investigated the time course as well as fundamental psychophysical constants governing visual perceptual decision-making in freely behaving mice. We did so by analyzing response accuracy against reaction time (RT), i.e., conditional accuracy, in a series of two-alternative forced choice (2-AFC) orientation discrimination tasks in which we varied target size, luminance, duration, and presence of a foil. Our results quantified two distinct stages in the time course of mouse visual decision-making: a “sensory encoding” stage in which conditional accuracy exhibits a classic trade-off with response speed, and a subsequent “short-term memory (STM)-dependent” stage in which conditional accuracy exhibits a classic asymptotic decay following stimulus offset. We estimated the duration of visual sensory encoding as 200–320 ms across tasks, the lower bound of the duration of STM as ∼1700 ms, and the briefest duration of visual stimulus input that is informative as ≤50 ms. Separately, by varying stimulus onset delay, we demonstrated that the conditional accuracy function (CAF) and RT distribution can be independently modulated, and found that the duration for which mice naturally withhold from responding is a quantitative metric of impulsivity. Taken together, our results establish a quantitative foundation for investigating the neural circuit bases of visual decision dynamics in mice.

Different Forms of Variability Could Explain a Difference Between Human and Rat Decision Making

When observers make rapid, difficult perceptual decisions, their response time is highly variable from trial to trial. In a visual motion discrimination task, it has been reported that human accuracy declines with increasing response time, whereas rat accuracy increases with response time. This is of interest because different mathematical theories of decision-making differ in their predictions regarding the correlation of accuracy with response time. On the premise that perceptual decision-making mechanisms are likely to be conserved among mammals, we seek to unify the rodent and primate results in a common theoretical framework. We show that a bounded drift diffusion model (DDM) can explain both effects with variable parameters: trial-to-trial variability in the starting point of the diffusion process produces the pattern typically observed in rats, whereas variability in the drift rate produces the pattern typically observed in humans. We further show that the same effects can be produced by deterministic biases, even in the absence of parameter stochasticity or parameter change within a trial.

The contribution of low contrast-preferring neurons to information representation in the primary visual cortex after learning

Animals exhibit improved perception of lower-contrast visual objects after training. We explored this neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination. We found that the firing rates of a subset of neurons increased by reducing luminance contrast, being at least above basal activities at low contrast. These low contrast–preferring neurons were rare during passive viewing without training or anesthesia after training. They fired more frequently in correct-choice than incorrect-choice trials. At single-neuron and population levels, they efficiently represented low-contrast orientations. Following training, in addition to generally enhanced excitation, the phase synchronization of spikes to beta oscillations at high contrast was stronger in putative inhibitory than excitatory neurons. The change in excitation-inhibition balance might contribute to low-contrast preference. Thus, low-contrast preference in V1 activity is strengthened in an experience-dependent manner, which may contribute to low-contrast visual discrimination.

Probabilistic discrimination of relative stimulus features in mice

During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.

A Sparse Probabilistic Code Underlies the Limits of Behavioral Discrimination

The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior.

Control of adaptive action selection by secondary motor cortex during flexible visual categorization

Adaptive action selection during stimulus categorization is an important feature of flexible behavior. To examine neural mechanism underlying this process, we trained mice to categorize the spatial frequencies of visual stimuli according to a boundary that changed between blocks of trials in a session. Using a model with a dynamic decision criterion, we found that sensory history was important for adaptive action selection after the switch of boundary. Bilateral inactivation of the secondary motor cortex (M2) impaired adaptive action selection by reducing the behavioral influence of sensory history. Electrophysiological recordings showed that M2 neurons carried more information about upcoming choice and previous sensory stimuli when sensorimotor association was being remapped than when it was stable. Thus, M2 causally contributes to flexible action selection during stimulus categorization, with the representations of upcoming choice and sensory history regulated by the demand to remap stimulus-action association.

CMOS-based bio-image sensor spatially resolves neural activity-dependent proton dynamics in the living brain

Recent studies have shown that protons can function as neurotransmitters in cultured neurons. To further investigate regional and neural activity-dependent proton dynamics in the brain, the development of a device with both wide-area detectability and high spatial-ltemporal resolution is necessary. Therefore, we develop an image sensor with a high spatial-temporal resolution specifically designed for measuring protons in vivo. Here, we demonstrate that spatially deferent neural stimulation by visual stimulation induced distinct patterns of proton changes in the visual cortex. This result indicates that our biosensor can detect micrometer and millisecond scale changes of protons across a wide area. Our study demonstrates that a CMOS-based proton image sensor with high spatial and temporal precision can be used to detect pH changes associated with biological events. We believe that our sensor may have broad applicability in future biological studies.

Protons have been discovered to play a role in neuronal signaling, but current methods to measure pH in the brain of animal models are limited. Here the authors develop a miniaturized proton image sensor that fits into a living mouse brain and can measure pH changes at micrometer and millisecond resolution scales.

Neuronal Adaptation Reveals a Suboptimal Decoding of Orientation Tuned Populations in the Mouse Visual Cortex

Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust visual adaptation in mouse primary visual cortex (V1). We first designed a stimulus paradigm in which we could vary the degree of neuronal adaptation measured in V1 during an orientation discrimination task. We then determined how adaptation affects task performance for mice of both sexes and tested which neuronal computations are most consistent with the behavioral results given the adapted population responses in V1. Despite increasing the reliability of the population representation of orientation among neurons, and improving the ability of a variety of optimal decoders to discriminate target from distractor orientations, adaptation increases animals' behavioral thresholds. Decoding the animals' choice from neuronal activity revealed that this unexpected effect on behavior could be explained by an overreliance of the perceptual choice circuit on target preferring neurons and a failure to appropriately discount the activity of neurons that prefer the distractor. Consistent with this all-positive computation, we find that animals' task performance is susceptible to subtle perturbations of distractor orientation and optogenetic suppression of neuronal activity in V1. This suggests that to solve this task the circuit has adopted a suboptimal and task-specific computation that discards important task-related information.SIGNIFICANCE STATEMENT A major goal in systems neuroscience is to understand how sensory signals are used to guide behavior. This requires determining what information in sensory cortical areas is used, and how it is combined, by downstream perceptual choice circuits. Here we demonstrate that when performing a go/no-go orientation discrimination task, mice suboptimally integrate signals from orientation tuned visual cortical neurons. While they appropriately positively weight target-preferring neurons, they fail to negatively weight distractor-preferring neurons. We propose that this all-positive computation may be adopted because of its simple learning rules and faster processing, and may be a common approach to perceptual decision-making when task conditions allow.

Contribution of Sensory Encoding to Measured Bias

Signal detection theory (SDT) is a widely used theoretical framework that describes how variable sensory signals are integrated with a decision criterion to support perceptual decision-making. SDT provides two key measurements: sensitivity (d') and bias (c), which reflect the separability of decision variable distributions (signal and noise) and the position of the decision criterion relative to optimal, respectively. Although changes in the subject's decision criterion can be reflected in changes in bias, decision criterion placement is not the sole contributor to measured bias. Indeed, neuronal representations of bias have been observed in sensory areas, suggesting that some changes in bias are because of effects on sensory encoding. To directly test whether the sensory encoding process can influence bias, we optogenetically manipulated neuronal excitability in primary visual cortex (V1) in mice of both sexes during either an orientation discrimination or a contrast detection task. Increasing excitability in V1 significantly decreased behavioral bias, whereas decreasing excitability had the opposite effect. To determine whether this change in bias is consistent with effects on sensory encoding, we made extracellular recordings from V1 neurons in passively viewing mice. Indeed, we found that optogenetic manipulation of excitability shifted the neuronal bias in the same direction as the behavioral bias. Moreover, manipulating the quality of V1 encoding by changing stimulus contrast or interstimulus interval also resulted in consistent changes in both behavioral and neuronal bias. Thus, changes in sensory encoding are sufficient to drive changes in bias measured using SDT.SIGNIFICANCE STATEMENT Perceptual decision-making involves sensory integration followed by application of a cognitive criterion. Using signal detection theory, one can extract features of the underlying decision variables and rule: sensitivity (d') and bias (c). Because bias is measured as the difference between the optimal and actual criterion, it is sensitive to both the sensory encoding processes and the placement of the decision criterion. Here, we use behavioral and electrophysiological approaches to demonstrate that measures of bias depend on sensory processes. Optogenetic manipulations of V1 in mice bidirectionally affect both behavioral and neuronal measures of bias with little effect on sensitivity. Thus, changes in sensory encoding influence bias, and the absence of changes in sensitivity do not preclude changes in sensory encoding.

Short-Term Influence of Recent Trial History on Perceptual Choice Changes with Stimulus Strength

Perceptual decisions, especially for difficult stimuli, can be influenced by choices and outcomes in previous trials. However, it is not well understood how stimulus strength modulates the temporal characteristics as well as the magnitude of trial history influence. We addressed this question using a contrast detection task in freely moving mice. We found that, at lower as compared to higher stimulus contrast, the current choice of the mice was more influenced by choices and outcomes in the past trials and the influence emerged from a longer history. To examine the neural basis of stimulus strength-dependent history influence, we recorded from the secondary motor cortex (M2), a prefrontal region that plays an important role in cue-guided actions and memory-guided behaviors. We found that more M2 neurons conveyed information about choices on the past two trials at lower than at higher contrast. Furthermore, history-trial activity in M2 was important for decoding upcoming choice at low contrast. Thus, trial history influence of perceptual choice is adaptive to the strength of sensory evidence, which may be important for action selection in a dynamic environment.

Internal Gain Modulations, But Not Changes in Stimulus Contrast, Preserve the Neural Code

Neurons in primary visual cortex are strongly modulated both by stimulus contrast and by fluctuations of internal inputs. An important question is whether the population code is preserved under these conditions. Changes in stimulus contrast are thought to leave the population code invariant, whereas the effect of internal gain modulations remains unknown. To address these questions we studied how the direction-of-motion of oriented gratings is encoded in layer 2/3 primary visual cortex of mouse (with C57BL/6 background, of either sex). We found that, because contrast gain responses across cells are heterogeneous, a change in contrast alters the information distribution profile across cells leading to a violation of contrast invariance. Remarkably, internal input fluctuations that cause commensurate firing rate modulations at the single-cell level result in more homogeneous gain responses, respecting population code invariance. These observations argue that the brain strives to maintain the stability of the neural code in the face of fluctuating internal inputs.SIGNIFICANCE STATEMENT Neuronal responses are modulated both by stimulus contrast and by the spontaneous fluctuation of internal inputs. It is not well understood how these different types of input impact the population code. Specifically, it is important to understand whether the neural code stays invariant in the face of significant internal input modulations. Here, we show that changes in stimulus contrast lead to different optimal population codes, whereas spontaneous internal input fluctuations leave the population code invariant. This is because spontaneous internal input fluctuations modulate the gain of neuronal responses more homogeneously across cells compared to changes in stimulus contrast.

Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

Recently, sophisticated optogenetic tools for mouse have enabled many detailed studies of the neuronal circuits of its primary visual cortex (V1), providing much more specific information than is available for cat or monkey. Among various other differences, they show a striking contrast dependency in orientation selectivity in mouse V1 rather than the well-known contrast invariance for cat and monkey. Constrained by the existing experiment data, we develop a comprehensive large-scale model of an effective input layer of mouse V1 that successfully reproduces the contrast-dependent phenomena and many other response properties. The model helps to probe different mechanisms based on excitation–inhibition balance that underlie both contrast dependencies and invariance, and it provides implications for future studies on these circuits.

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

Having More Choices Changes How Human Observers Weight Stable Sensory Evidence

Decision-making becomes slower when more choices are available. Existing models attribute this slowing to poor sensory processing, to attenuated rates of sensory evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). However, studies have not isolated the effects of having more choices on sensory and decision-related processes from changes in task difficulty and divided attention. Here, we controlled task difficulty while independently manipulating the distribution of attention and the number of choices available to male and female human observers. We used EEG to measure steady-state visually evoked potentials (SSVEPs) and a frontal late positive deflection (LPD), EEG markers of sensory and postsensory decision-related processes, respectively. We found that dividing attention decreased SSVEP and LPD amplitudes, consistent with dampened sensory responses and slower rates of evidence accumulation, respectively. In contrast, having more choices did not alter SSVEP amplitude and led to a larger LPD. These results suggest that having more options largely spares early sensory processing and slows down decision-making via a selective increase in decision thresholds.SIGNIFICANCE STATEMENT When more choices are available, decision-making becomes slower. We tested whether this phenomenon is due to poor sensory processing, to reduced rates of evidence accumulation, or to increases in the amount of evidence required before committing to a decision (a higher decision threshold). We measured choice modulations of sensory and decision-related neural responses using EEG. We also minimized potential confounds from changes in the distribution of attention and task difficulty, which often covary with having more choices. Dividing attention reduced the activity levels of both sensory and decision-related responses. However, having more choices did not change sensory processing and led to larger decision-related responses. These results suggest that having more choices spares sensory processing and selectively increases decision thresholds.

Phase-specific Surround suppression in Mouse Primary Visual Cortex Correlates with Figure Detection Behavior Based on Phase Discontinuity

In the primary visual cortex (V1), neuronal responses to stimuli within the receptive field (RF) are modulated by stimuli in the RF surround. A common effect of surround modulation is surround suppression, which is dependent on the feature difference between stimuli within and surround the RF and is suggested to be involved in the perceptual phenomenon of figure-ground segregation. In this study, we examined the relationship between feature-specific surround suppression of V1 neurons and figure detection behavior based on figure-ground feature difference. We trained freely moving mice to perform a figure detection task using figure and ground gratings that differed in spatial phase. The performance of figure detection increased with the figure-ground phase difference, and was modulated by stimulus contrast. Electrophysiological recordings from V1 in head-fixed mice showed that the increase in phase difference between stimuli within and surround the RF caused a reduction in surround suppression, which was associated with an increase in V1 neural discrimination between stimuli with and without RF-surround phase difference. Consistent with the behavioral performance, the sensitivity of V1 neurons to RF-surround phase difference could be influenced by stimulus contrast. Furthermore, inhibiting V1 by optogenetically activating either parvalbumin (PV)- or somatostatin (SOM)-expressing inhibitory neurons both decreased the behavioral performance of figure detection. Thus, the phase-specific surround suppression in V1 represents a neural correlate of figure detection behavior based on figure-ground phase discontinuity.

Adaptation to stimulus orientation in mouse primary visual cortex

Information processing in the visual system is shaped by recent stimulus history, such that prolonged viewing of an adapting stimulus can alter the perception of subsequently presented test stimuli. In the tilt-after-effect, the perceived orientation of a grating is often repelled away from the orientation of a previously viewed adapting grating. A possible neural correlate for the tilt-after-effect has been described in cat and macaque primary visual cortex (V1), where adaptation produces repulsive shifts in the orientation tuning curves of V1 neurons. We investigated adaptation to stimulus orientation in mouse V1 to determine whether known species differences in orientation processing, notably V1 functional architecture and proportion of tightly tuned cells, are important for these repulsive shifts. Unlike the consistent repulsion reported in other species, we found that repulsion was only about twice as common as attraction in our mouse data. Furthermore, adapted responses were attenuated across all orientations. A simple model that captured key physiological findings reported in cats and mice indicated that the greater proportion of broadly tuned neurons in mice may explain the observed species differences in adaptation.

Mice use robust and common strategies to discriminate natural scenes

Mice use vision to navigate and avoid predators in natural environments. However, their visual systems are compact compared to other mammals, and it is unclear how well mice can discriminate ethologically relevant scenes. Here, we examined natural scene discrimination in mice using an automated touch-screen system. We estimated the discrimination difficulty using the computational metric structural similarity (SSIM), and constructed psychometric curves. However, the performance of each mouse was better predicted by the mean performance of other mice than SSIM. This high inter-mouse agreement indicates that mice use common and robust strategies to discriminate natural scenes. We tested several other image metrics to find an alternative to SSIM for predicting discrimination performance. We found that a simple, primary visual cortex (V1)-inspired model predicted mouse performance with fidelity approaching the inter-mouse agreement. The model involved convolving the images with Gabor filters, and its performance varied with the orientation of the Gabor filter. This orientation dependence was driven by the stimuli, rather than an innate biological feature. Together, these results indicate that mice are adept at discriminating natural scenes, and their performance is well predicted by simple models of V1 processing.

Changes in GABAergic markers accompany degradation of neuronal function in the primary visual cortex of senescent rats

Numerous studies have reported age-dependent degradation of neuronal function in the visual cortex and have attributed this functional decline to weakened intracortical inhibition, especially GABAergic inhibition. However, whether this type of functional decline is linked to compromised GABAergic inhibition has not been fully confirmed. Here, we compared the neuronal response properties and markers of GABAergic inhibition in the primary visual cortex (V1) of young adult and senescent rats. Compared with those of young adult rats, old rats’ V1 neurons exhibited significantly increased visually evoked responses and spontaneous activity, a decreased signal-to-noise ratio and reduced response selectivity for the stimulus orientation and motion direction. Additionally, the ratio of GABA-positive neurons to total cortical neurons in old rats was significantly decreased compared with that in young rats. Expression of the key GABA-synthesizing enzyme GAD67 was significantly lower in old rats than in young rats, although GAD65 expression showed a marginal difference between the two age groups. Further, expression of an important GABA_A receptor subunit, GABA_AR α₁, was significantly attenuated in old rats relative to young ones. These results demonstrate that ageing may result in decreased GABAergic inhibition in the visual cortex and that this decrease in GABAergic inhibition accompanies neuronal function degradation.

High-Yield Methods for Accurate Two-Alternative Visual Psychophysics in Head-Fixed Mice

Research in neuroscience increasingly relies on the mouse, a mammalian species that affords unparalleled genetic tractability and brain atlases. Here, we introduce high-yield methods for probing mouse visual decisions. Mice are head-fixed, facilitating repeatable visual stimulation, eye tracking, and brain access. They turn a steering wheel to make two alternative choices, forced or unforced. Learning is rapid thanks to intuitive coupling of stimuli to wheel position. The mouse decisions deliver high-quality psychometric curves for detection and discrimination and conform to the predictions of a simple probabilistic observer model. The task is readily paired with two-photon imaging of cortical activity. Optogenetic inactivation reveals that the task requires mice to use their visual cortex. Mice are motivated to perform the task by fluid reward or optogenetic stimulation of dopamine neurons. This stimulation elicits a larger number of trials and faster learning. These methods provide a platform to accurately probe mouse vision and its neural basis.

AhR-deficiency as a cause of demyelinating disease and inflammation

The Aryl hydrocarbon Receptor(AhR) is among the most important receptors which bind pollutants; however it also regulates signaling pathways independently of such exposure. We previously demonstrated that AhR is expressed during development of the central nervous system(CNS) and that its deletion leads to the occurrence of a congenital nystagmus. Objectives of the present study are to decipher the origin of these deficits, and to identify the role of the AhR in the development of the CNS. We show that the AhR-knockout phenotype develops during early infancy together with deficits in visual-information-processing which are associated with an altered optic nerve myelin sheath, which exhibits modifications in its lipid composition and in the expression of myelin-associated-glycoprotein(MAG), a cell adhesion molecule involved in myelin-maintenance and glia-axon interaction. In addition, we show that the expression of pro-inflammatory cytokines is increased in the impaired optic nerve and confirm that inflammation is causally related with an AhR-dependent decreased expression of MAG. Overall, our findings demonstrate the role of the AhR as a physiological regulator of myelination and inflammatory processes in the developing CNS. It identifies a mechanism by which environmental pollutants might influence CNS myelination and suggest AhR as a relevant drug target for demyelinating diseases.

Altered visual cortical processing in a mouse model of MECP2 duplication syndrome

As an epigenetic modulator of gene expression, Methyl-CpG binding protein 2 (MeCP2) is essential for normal neurological function. Dysfunction of MeCP2 is associated with a variety of neurological disorders. MECP2 gene duplication in human causes neuropsychiatric symptoms such as mental retardation and autism. MeCP2 overexpression in mice results in neurobehavioural disorders, dendritic abnormalities, and synaptic defects. However, how gain of MeCP2 function influences cortical processing of sensory information remains unclear. In this study, we examined visual processing in a mouse model of MECP2 duplication syndrome (MECP2 Tg1 mouse) at 8 and 14 weeks, which were before and after the onset of behavioural symptoms, respectively. In vivo extracellular recordings from primary visual cortex (V1) showed that neurons in Tg1 mice at both adult ages preferred higher spatial frequencies (SFs) than those in wild-type (WT) littermate controls, and the semi-saturation contrasts of neurons were lower in Tg1 mice at 8 weeks but not at 14 weeks. Behavioural experiments showed that the performance for visual detection at high SFs and low contrasts was higher in MECP2 Tg1 mice. Thus, MeCP2 gain-of-function in mice leads to higher visual acuity and contrast sensitivity, both at the levels of cortical response and behavioural performance.

Learning Enhances Sensory Processing in Mouse V1 before Improving Behavior

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.