Mechanisms of neuronal computation in mammalian visual cortex

Neuronal and behavioral responses to naturalistic texture images in macaque monkeys

The visual world is richly adorned with texture, which can serve to delineate important elements of natural scenes. In anesthetized macaque monkeys, selectivity for the statistical features of natural texture is weak in V1, but substantial in V2, suggesting that neuronal activity in V2 might directly support texture perception. To test this, we investigated the relation between single cell activity in macaque V1 and V2 and simultaneously measured behavioral judgments of texture. We generated stimuli along a continuum between naturalistic texture and phase-randomized noise and trained two macaque monkeys to judge whether a sample texture more closely resembled one or the other extreme. Analysis of responses revealed that individual V1 and V2 neurons carried much less information about texture naturalness than behavioral reports. However, the sensitivity of V2 neurons, especially those preferring naturalistic textures, was significantly closer to that of behavior compared with V1. The firing of both V1 and V2 neurons predicted perceptual choices in response to repeated presentations of the same ambiguous stimulus in one monkey, despite low individual neural sensitivity. However, neither population predicted choice in the second monkey. We conclude that neural responses supporting texture perception likely continue to develop downstream of V2. Further, combined with neural data recorded while the same two monkeys performed an orientation discrimination task, our results demonstrate that choice-correlated neural activity in early sensory cortex is unstable across observers and tasks, untethered from neuronal sensitivity, and thus unlikely to reflect a critical aspect of the formation of perceptual decisions.

Significance statement

As visual signals propagate along the cortical hierarchy, they encode increasingly complex aspects of the sensory environment and likely have a more direct relationship with perceptual experience. We replicate and extend previous results from anesthetized monkeys differentiating the selectivity of neurons along the first step in cortical vision from area V1 to V2. However, our results further complicate efforts to establish neural signatures that reveal the relationship between perception and the neuronal activity of sensory populations. We find that choice-correlated activity in V1 and V2 is unstable across different observers and tasks, and also untethered from neuronal sensitivity and other features of nonsensory response modulation.

A role for ocular dominance in binocular integration

Neurons in the primate primary visual cortex (V1) combine left- and right-eye information to form a binocular output. Controversy surrounds whether ocular dominance, the preference of these neurons for one eye over the other, is functionally relevant. Here, we demonstrate that ocular dominance impacts gain control during binocular combination. We recorded V1 spiking activity while monkeys passively viewed grating stimuli. Gratings were either presented to one eye (monocular), both eyes with the same contrasts (binocular balanced), or both eyes with different contrasts (binocular imbalanced). We found that contrast placed in a neuron's dominant eye was weighted more strongly than contrast placed in a neuron's non-dominant eye. This asymmetry covaried with neurons' ocular dominance. We then tested whether accounting for ocular dominance within divisive normalization improves the fit to neural data. We found that ocular dominance significantly improved model performance, with interocular normalization providing the best fits. These findings suggest that V1 ocular dominance is relevant for response normalization during binocular stimulation.

Widespread and Multifaceted Binocular Integration in the Mouse Primary Visual Cortex

The brain combines two-dimensional images received from the two eyes to form a percept of three-dimensional surroundings. This process of binocular integration in the primary visual cortex (V1) serves as a useful model for studying how neural circuits generate emergent properties from multiple input signals. Here, we perform a thorough characterization of binocular integration using electrophysiological recordings in the V1 of awake adult male and female mice by systematically varying the orientation and phase disparity of monocular and binocular stimuli. We reveal widespread binocular integration in mouse V1 and demonstrate that the three commonly studied binocular properties-ocular dominance, interocular matching, and disparity selectivity-are independent of each other. For individual neurons, the responses to monocular stimulation can predict the average amplitude of binocular response but not its selectivity. Finally, the extensive and independent binocular integration of monocular inputs is seen across cortical layers in both regular-spiking and fast-spiking neurons, regardless of stimulus design. Our data indicate that the current model of simple feedforward convergence is inadequate to account for binocular integration in mouse V1, thus suggesting an indispensable role played by intracortical circuits in binocular computation.SIGNIFICANCE STATEMENT Binocular integration is an important step of visual processing that takes place in the visual cortex. Studying the process by which V1 neurons become selective for certain binocular disparities is informative about how neural circuits integrate multiple information streams at a more general level. Here, we systematically characterize binocular integration in mice. Our data demonstrate more widespread and complex binocular integration in mouse V1 than previously reported. Binocular responses cannot be explained by a simple convergence of monocular responses, contrary to the prevailing model of binocular integration. These findings thus indicate that intracortical circuits must be involved in the exquisite computation of binocular disparity, which would endow brain circuits with the plasticity needed for binocular development and processing.

Stimulus edges induce orientation tuning in superior colliculus

Orientation columns exist in the primary visual cortex (V1) of cat and primates but not mouse. Intriguingly, some recent studies reported the presence of orientation and direction columns in the mouse superficial superior colliculus (sSC), while others reported a lack of columnar organization therein. Using in vivo calcium imaging of sSC in the awake mouse brain, we found that the presence of columns is highly stimulus dependent. Specifically, we observed orientation and direction columns formed by sSC neurons retinotopically mapped to the edge of grating stimuli. For both excitatory and inhibitory neurons in sSC, orientation selectivity can be induced by the edge with their preferred orientation perpendicular to the edge orientation. Furthermore, we found that this edge-induced orientation selectivity is associated with saliency encoding. These findings indicate that the tuning properties of sSC neurons are not fixed by circuit architecture but rather dependent on the spatiotemporal properties of the stimulus.

A unifying perspective on neural manifolds and circuits for cognition

Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.

Sanford’s L dissected: A partial replication and extension of Cai et al. (2017)

Partial replications of experiments reported by Cai et al. (Attention, Perception, & Psychophysics, 79(4), 1217-1226, 2017) on the so-called Horizontal-vertical illusion confirmed that dissecting L-figures into two separate lines yields greater overestimation of (near-)verticals than do intact Ls. However, contrary to Cai et al.'s findings, which had been obtained with a staircase procedure, with the method of constant stimuli, the amount of illusion was much smaller. This divergence is explained by the self-reinforcing nature of adjustment procedures. Another finding, already reported by Cormack and Cormack (Perception & Psychophysics, 16(2), 208-212, 1974), that obtuse angles between an L's lines yield greater bias than acute angles, was also replicated in one experiment but tended to be reversed in another. Mixing dissected, upright and top-down inverted Ls and laterally oriented Ts, both with tilted lines, within one experiment confirmed that the bias for Ts is opposite to the one for Ls: For Ts, the effect of (virtual) bisection dominates, yielding an overestimation of the length of the undivided line, whereas for Ls, the horizontal-vertical anisotropy dominates, yielding an overestimation of the length of the vertical line. The differential gap effects can possibly be explained by interactions within the neural substrate between orientation-sensitive and end-inhibited neurons, and the method effects by perceptual learning.

Neural circuits for binocular vision: Ocular dominance, interocular matching, and disparity selectivity

The brain creates a single visual percept of the world with inputs from two eyes. This means that downstream structures must integrate information from the two eyes coherently. Not only does the brain meet this challenge effortlessly, it also uses small differences between the two eyes' inputs, i.e., binocular disparity, to construct depth information in a perceptual process called stereopsis. Recent studies have advanced our understanding of the neural circuits underlying stereoscopic vision and its development. Here, we review these advances in the context of three binocular properties that have been most commonly studied for visual cortical neurons: ocular dominance of response magnitude, interocular matching of orientation preference, and response selectivity for binocular disparity. By focusing mostly on mouse studies, as well as recent studies using ferrets and tree shrews, we highlight unresolved controversies and significant knowledge gaps regarding the neural circuits underlying binocular vision. We note that in most ocular dominance studies, only monocular stimulations are used, which could lead to a mischaracterization of binocularity. On the other hand, much remains unknown regarding the circuit basis of interocular matching and disparity selectivity and its development. We conclude by outlining opportunities for future studies on the neural circuits and functional development of binocular integration in the early visual system.

Deconfounded and mixed-symmetry versions of the Ponzo illusion figure

One of the original Ponzo illusion figures, which consists of two converging lines between which two parallel lines of similar length have been inserted orthogonal to the figure's axis of mirror symmetry, was itself mirror-reflected so that the overall shape of the figure became "< >" or "> <", and one line at a time was inserted into each half. The usual illusion - the overestimation of the length of a line that is nearer to a vertex than a farther-away comparison line - occurred. Experiments 1 and 2 used different distances of target and comparison lines to the vertices, but identical distances of these lines from the converging lines, and so, as a tandem, deconfounded the two variables. Experiments 3 and 4 changed the symmetries of the modified Ponzo figure by reducing opposing half-angles of the converging lines or by tilting target and comparison lines concordantly or discordantly. The first measure, which created unequal distances of the endpoints of the target and comparison lines from the converging lines, hardly affected the amount of illusion. The second measure often attenuated the illusion - equally so for concordant and discordant tilts - suggesting that global and local symmetries of the stimuli, and their accordance, were less important than the vertical versus oblique orientation of target and comparison lines. Descriptively, the main cause of the Ponzo illusion seems to be the size of the gap between target and converging lines. The neural substrate of the effect may be interactions between orientation-sensitive and end-inhibited neurons.

Motion detection and direction recognition in a photonic spiking neural network consisting of VCSELs-SA

Motion detection and direction recognition are two important fundamental visual functions among the many cognitive functions performed by the human visual system. The retina and visual cortex are indispensable for composing the visual nervous system. The retina is responsible for transmitting electrical signals converted from light signals to the visual cortex of the brain. We propose a photonic spiking neural network (SNN) based on vertical-cavity surface-emitting lasers with an embedding saturable absorber (VCSELs-SA) with temporal integration effects, and demonstrate that the motion detection and direction recognition tasks can be solved by mimicking the visual nervous system. Simulation results reveal that the proposed photonic SNN with a modified supervised algorithm combining the tempotron and the STDP rule can correctly detect the motion and recognize the direction angles, and is robust to time jitter and the current difference between VCSEL-SAs. The proposed approach adopts a low-power photonic neuromorphic system for real-time information processing, which provides theoretical support for the large-scale application of hardware photonic SNN in the future.

The Prospects of Utilizing Geometrical Visual Illusions as Tools for Neuroscience

Geometrical visual illusions have long been used as tools in neuroscience. Most commonly, researchers have taken illusions as a given and attempted to explain phenomenal impressions in terms of known neural mechanisms. In a psychophysical approach to this topic, it is customary to modify stimuli until conditions for which illusions are enhanced, attenuated, or annihilated have been found. Additionally, the focus is not exclusively on response bias but equally on sensitivity, because observers may fall prey to an illusion but at the same time be able to discriminate between stimuli perfectly. For the T-figure, the length of the undivided line is usually overestimated relative to the length of the divided line, and evidence has accrued that suggests that the illusion may be due to the processing of the figure as a coherent unit (a “T-schema”). Dissecting the T or tilting its lines influenced the amount of illusion, suggesting that interactions between orientation-sensitive and end-inhibited neurons are at work. Examples of cognate research with the Ponzo, Ebbinghaus, and Müller-Lyer illusions are also discussed.

Horizontal connectivity in V1: Prediction of coherence in contour and motion integration

This study demonstrates the functional importance of the Surround context relayed laterally in V1 by the horizontal connectivity, in controlling the latency and the gain of the cortical response to the feedforward visual drive. We report here four main findings: 1) a centripetal apparent motion sequence results in a shortening of the spiking latency of V1 cells, when the orientation of the local inducer and the global motion axis are both co-aligned with the RF orientation preference; 2) this contextual effects grows with visual flow speed, peaking at 150–250°/s when it matches the propagation speed of horizontal connectivity (0.15–0.25 mm/ms); 3) For this speed range, the axial sensitivity of V1 cells is tilted by 90° to become co-aligned with the orientation preference axis; 4) the strength of modulation by the surround context correlates with the spatiotemporal coherence of the apparent motion flow. Our results suggest an internally-generated binding process, linking local (orientation /position) and global (motion/direction) features as early as V1. This long-range diffusion process constitutes a plausible substrate in V1 of the human psychophysical bias in speed estimation for collinear motion. Since it is demonstrated in the anesthetized cat, this novel form of contextual control of the cortical gain and phase is a built-in property in V1, whose expression does not require behavioral attention and top-down control from higher cortical areas. We propose that horizontal connectivity participates in the propagation of an internal “prediction” wave, shaped by visual experience, which links contour co-alignment and global axial motion at an apparent speed in the range of saccade-like eye movements.

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

Origins of direction selectivity in the primate retina

From mouse to primate, there is a striking discontinuity in our current understanding of the neural coding of motion direction. In non-primate mammals, directionally selective cell types and circuits are a signature feature of the retina, situated at the earliest stage of the visual process. In primates, by contrast, direction selectivity is a hallmark of motion processing areas in visual cortex, but has not been found in the retina, despite significant effort. Here we combined functional recordings of light-evoked responses and connectomic reconstruction to identify diverse direction-selective cell types in the macaque monkey retina with distinctive physiological properties and synaptic motifs. This circuitry includes an ON-OFF ganglion cell type, a spiking, ON-OFF polyaxonal amacrine cell and the starburst amacrine cell, all of which show direction selectivity. Moreover, we discovered that macaque starburst cells possess a strong, non-GABAergic, antagonistic surround mediated by input from excitatory bipolar cells that is critical for the generation of radial motion sensitivity in these cells. Our findings open a door to investigation of a precortical circuitry that computes motion direction in the primate visual system.

Cortical layer-specific differences in stimulus selectivity revealed with high-field fMRI and single-vessel resolution optical imaging of the primary visual cortex

The mammalian neocortex exhibits a stereotypical laminar organization, with feedforward inputs arriving primarily into layer 4, local computations shaping response selectivity in layers 2/3, and outputs to other brain areas emanating via layers 2/3, 5 and 6. It cannot be assumed a priori that these signatures of laminar differences in neuronal circuitry are reflected in hemodynamic signals that form the basis of functional magnetic resonance imaging (fMRI). Indeed, optical imaging of single-vessel functional responses has highlighted the potential limits of using vascular signals as surrogates for mapping the selectivity of neural responses. Therefore, before fMRI can be employed as an effective tool for studying critical aspects of laminar processing, validation with single-vessel resolution is needed. The primary visual cortex (V1) in cats, with its precise neuronal functional micro-architecture, offers an ideal model system to examine laminar differences in stimulus selectivity across imaging modalities. Here we used cerebral blood volume weighted (wCBV) fMRI to examine if layer-specific orientation-selective responses could be detected in cat V1. We found orientation preference maps organized tangential to the cortical surface that typically extended across depth in a columnar fashion. We then examined arterial dilation and blood velocity responses to identical visual stimuli by using two- and three- photon optical imaging at single-vessel resolution-which provides a measure of the hemodynamic signals with the highest spatial resolution. Both fMRI and optical imaging revealed a consistent laminar response pattern in which orientation selectivity in cortical layer 4 was significantly lower compared to layer 2/3. This systematic change in selectivity across cortical layers has a clear underpinning in neural circuitry, particularly when comparing layer 4 to other cortical layers.

Saturating Nonlinearities of Contrast Response in Human Visual Cortex

Response nonlinearities are ubiquitous throughout the brain, especially within sensory cortices where changes in stimulus intensity typically produce compressed responses. Although this relationship is well established in electrophysiological measurements, it remains controversial whether the same nonlinearities hold for population-based measurements obtained with human fMRI. We propose that these purported disparities are not contingent on measurement type and are instead largely dependent on the visual system state at the time of interrogation. We show that deploying a contrast adaptation paradigm permits reliable measurements of saturating sigmoidal contrast response functions (10 participants, 7 female). When not controlling the adaptation state, our results coincide with previous fMRI studies, yielding nonsaturating, largely linear contrast responses. These findings highlight the important role of adaptation in manifesting measurable nonlinear responses within human visual cortex, reconciling discrepancies reported in vision neuroscience, re-establishing the qualitative relationship between stimulus intensity and response across different neural measures and the concerted study of cortical gain control.SIGNIFICANCE STATEMENT Nonlinear stimulus-response relationships govern many essential brain functions, ranging from the sensory to cognitive level. Certain core response properties previously shown to be nonlinear with nonhuman electrophysiology recordings have yet to be reliably measured with human neuroimaging, prompting uncertainty and reconsideration. The results of this study stand to reconcile these incongruencies in the vision neurosciences, demonstrating the profound impact adaptation can have on brain activation throughout the early visual cortex. Moving forward, these findings facilitate the study of modulatory influences on sensory processing (i.e., arousal and attention) and help establish a closer link between neural recordings in animals and hemodynamic measurements from human fMRI, resuming a concerted effort to understand operations in the mammalian cortex.

Mechanism underpinning the sharpening of orientation and spatial frequency selectivities in the tree shrew (Tupaia belangeri) primary visual cortex

Most neurons in the primary visual cortex (V1) of mammals show sharp orientation selectivity and band-pass spatial frequency tuning. Here, we examine whether sharpening of the broad tuning that exists subcortically, namely in the retina and the lateral geniculate nucleus (LGN), underlie the sharper tuning seen for both the above features in tree shrew V1. Since the transition from poor feature selectivity to sharp tuning occurs entirely within V1 in tree shrews, we examined the orientation selectivity and spatial frequency tuning of neurons within individual electrode penetrations. We found that most layer 4 and layer 2/3 neurons in the same cortical column preferred the same stimulus orientation. However, a subset of layer 3c neurons close to the layer 4 border preferred near orthogonal orientations, suggesting that layer 2/3 neurons may inherit the orientation preferences of their layer 4 input neurons and also receive cross-orientation inhibition from layer 3c neurons. We also found that layer 4 neurons showed sharper orientation selectivity at higher spatial frequencies, suggesting that attenuation of low spatial frequency responses by spatially broad inhibition acting on layer 4 inputs to layer 2/3 neurons can enhance both orientation and spatial frequency selectivities. However, in a proportion of layer 2/3 neurons, the sharper tuning of layer 2/3 neurons appeared to arise also or even mainly from inhibition specific to high spatial frequencies acting on the layer 4 inputs to layer 2/3. Overall, our results are consistent with the suggestion that in tree shrews, sharp feature selectivity in layer 2/3 can be established by intracortical mechanisms that sharpen biases observed in layer 4, which are in turn inherited presumably from thalamic afferents.

A binocular synaptic network supports interocular response alignment in visual cortical neurons

In visual cortex, signals from the two eyes merge to form a coherent binocular representation. Here we investigate the synaptic interactions underlying the binocular representation of stimulus orientation in ferret visual cortex with in vivo calcium imaging of layer 2/3 neurons and their dendritic spines. Individual neurons with aligned somatic responses received a mixture of monocular and binocular synaptic inputs. Surprisingly, monocular pathways alone could not account for somatic alignment because ipsilateral monocular inputs poorly matched somatic preference. Binocular inputs exhibited different degrees of interocular alignment, and those with a high degree of alignment (congruent) had greater selectivity and somatic specificity. While congruent inputs were similar to others in measures of strength, simulations show that the number of active congruent inputs predicts aligned somatic output. Our study suggests that coherent binocular responses derive from connectivity biases that support functional amplification of aligned signals within a heterogeneous binocular intracortical network.

Feedforward mechanisms of cross-orientation interactions in mouse V1

Sensory neurons are modulated by context. For example, in mouse primary visual cortex (V1), neuronal responses to the preferred orientation are modulated by the presence of superimposed orientations ("plaids"). The effects of this modulation are diverse; some neurons are suppressed, while others have larger responses to a plaid than its components. We investigated whether this diversity could be explained by a unified circuit mechanism. We report that this masking is maintained during suppression of cortical activity, arguing against cortical mechanisms. Instead, the heterogeneity of plaid responses is explained by an interaction between stimulus geometry and orientation tuning. Highly selective neurons are uniformly suppressed by plaids, whereas the effects in weakly selective neurons depend on the spatial configuration of the stimulus, transitioning systematically between suppression and facilitation. Thus, the diverse responses emerge as a consequence of the spatial structure of feedforward inputs, with no need to invoke cortical interactions.

Spatial modulation of dark versus bright stimulus responses in the mouse visual system

A fundamental task of the visual system is to respond to both increases and decreases of luminance with action potentials (ON and OFF responses1-4). OFF responses are stronger, faster, and more salient than ON responses in primary visual cortex (V1) of both cats5,6 and primates,7,8 but in ferrets9 and mice,10 ON responses can be stronger, weaker,11 or balanced12 in comparison to OFF responses. These discrepancies could arise from differences in species, experimental techniques, or stimulus properties, particularly retinotopic location in the visual field, as has been speculated;9 however, the role of retinotopy for ON/OFF dominance has not been systematically tested across multiple scales of neural activity within species. Here, we measured OFF versus ON responses across large portions of visual space with silicon probe and whole-cell patch-clamp recordings in mouse V1 and lateral geniculate nucleus (LGN). We found that OFF responses dominated in the central visual field, whereas ON and OFF responses were more balanced in the periphery. These findings were consistent across local field potential (LFP), spikes, and subthreshold membrane potential in V1, and were aligned with spatial biases in ON and OFF responses in LGN. Our findings reveal that retinotopy may provide a common organizing principle for spatial modulation of OFF versus ON processing in mammalian visual systems.

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.

The Synaptic Vesicle Priming Protein CAPS-1 Shapes the Adaptation of Sensory Evoked Responses in Mouse Visual Cortex

Short-term plasticity gates information transfer across neuronal synapses and is thought to be involved in fundamental brain processes, such as cortical gain control and sensory adaptation. Neurons employ synaptic vesicle priming proteins of the CAPS and Munc13 families to shape short-term plasticity in vitro, but the relevance of this phenomenon for information processing in the intact brain is unknown. By combining sensory stimulation with in vivo patch-clamp recordings in anesthetized mice, we show that genetic deletion of CAPS-1 in thalamic neurons results in more rapid adaptation of sensory-evoked subthreshold responses in layer 4 neurons of the primary visual cortex. Optogenetic experiments in acute brain slices further reveal that the enhanced adaptation is caused by more pronounced short-term synaptic depression. Our data indicate that neurons engage CAPS-family priming proteins to shape short-term plasticity for optimal sensory information transfer between thalamic and cortical neurons in the intact brain in vivo.

Supersaturation of VEP in Migraine without Aura Patients Treated with Topiramate: An Anatomo-Functional Biomarker of the Disease

Migraine is a primary headache with high prevalence among the general population, characterized by functional hypersensitivity to both exogenous and endogenous stimuli particularly affecting the nociceptive system. The hyperresponsivity of cortical neurons could be due to a disequilibrium in the excitatory/inhibitory signaling. This study aimed to investigate the anatomo-functional pathway from the retina to the primary visual cortex using visual evoked potentials (VEP). Contrast gain protocol was used in 15 patients diagnosed with migraine without aura (at baseline and after 3 months of topiramate therapy) and 13 controls. A saturation (S) index was assessed to monitor the response of VEP’s amplitude to contrast gain. Non-linear nor monotone growth of VEP (S < 0.95) was defined as supersaturation. A greater percentage of migraine patients (53%) relative to controls (7%) showed this characteristic. A strong inverse correlation was found between the S index and the number of days separating the registration of VEP from the next migraine attack. Moreover, allodynia measured through the Allodynia Symptoms Check-list (ASC-12) correlates with the S index both at baseline and after 3 months of topiramate treatment. Other clinical characteristics were not related to supersaturation. Topiramate therapy, although effective, did not influence electrophysiological parameters suggesting a non-intracortical nor retinal origin of the supersaturation (with possible involvement of relay cells from the lateral geniculate nucleus). In conclusion, the elaboration of visual stimuli and visual cortex activity is different in migraine patients compared to controls. More data are necessary to confirm the potential use of the S index as a biomarker for the migraine cycle (association with the pain-phase) and cortical sensitization (allodynia).

Central Vestibular Tuning Arises from Patterned Convergence of Otolith Afferents

As sensory information moves through the brain, higher-order areas exhibit more complex tuning than lower areas. Though models predict that complexity arises via convergent inputs from neurons with diverse response properties, in most vertebrate systems, convergence has only been inferred rather than tested directly. Here, we measure sensory computations in zebrafish vestibular neurons across multiple axes in vivo. We establish that whole-cell physiological recordings reveal tuning of individual vestibular afferent inputs and their postsynaptic targets. Strong, sparse synaptic inputs can be distinguished by their amplitudes, permitting analysis of afferent convergence in vivo. An independent approach, serial-section electron microscopy, supports the inferred connectivity. We find that afferents with similar or differing preferred directions converge on central vestibular neurons, conferring more simple or complex tuning, respectively. Together, these results provide a direct, quantifiable demonstration of feedforward input convergence in vivo.

Selectivity and robustness of sparse coding networks

We investigate how the population nonlinearities resulting from lateral inhibition and thresholding in sparse coding networks influence neural response selectivity and robustness. We show that when compared to pointwise nonlinear models, such population nonlinearities improve the selectivity to a preferred stimulus and protect against adversarial perturbations of the input. These findings are predicted from the geometry of the single-neuron iso-response surface, which provides new insight into the relationship between selectivity and adversarial robustness. Inhibitory lateral connections curve the iso-response surface outward in the direction of selectivity. Since adversarial perturbations are orthogonal to the iso-response surface, adversarial attacks tend to be aligned with directions of selectivity. Consequently, the network is less easily fooled by perceptually irrelevant perturbations to the input. Together, these findings point to benefits of integrating computational principles found in biological vision systems into artificial neural networks.

Variation of connectivity across exemplar sensory and associative thalamocortical loops in the mouse

The thalamus engages in sensation, action, and cognition, but the structure underlying these functions is poorly understood. Thalamic innervation of associative cortex targets several interneuron types, modulating dynamics and influencing plasticity. Is this structure-function relationship distinct from that of sensory thalamocortical systems? Here, we systematically compared function and structure across a sensory and an associative thalamocortical loop in the mouse. Enhancing excitability of mediodorsal thalamus, an associative structure, resulted in prefrontal activity dominated by inhibition. Equivalent enhancement of medial geniculate excitability robustly drove auditory cortical excitation. Structurally, geniculate axons innervated excitatory cortical targets in a preferential manner and with larger synaptic terminals, providing a putative explanation for functional divergence. The two thalamic circuits also had distinct input patterns, with mediodorsal thalamus receiving innervation from a diverse set of cortical areas. Altogether, our findings contribute to the emerging view of functional diversity across thalamic microcircuits and its structural basis.

Cortical synaptic architecture supports flexible sensory computations

Establishing the fundamental principles that underlie the integration of excitatory and inhibitory presynaptic input populations is crucial to understanding how individual cortical neurons transform signals from peripheral receptors. Here we review recent studies using novel tools to examine the functional properties of excitatory synaptic inputs and the tuning of excitation and inhibition onto individual neurons. New evidence challenges existing synaptic connectivity rules and suggests a more complex functional synaptic architecture that supports a broad range of operations, enabling single neurons to encode multiple sensory features and flexibly shape their computations in the face of diverse sensory input.

Frequency-separated principal component analysis of cortical population activity

A hallmark of neocortical activity is the presence of low-dimensional fluctuations in firing rate that are coordinated across neurons. However, the impact of these fluctuations on sensory processing remains unclear. Here, we examined fluctuations in populations of orientation-selective neurons from anesthetized macaque primary visual cortex (V1) during stimulus viewing as well as spontaneous activity. We introduce a novel approach termed frequency-separated principal component analysis (FS-PCA) to characterize these fluctuations. This method unveiled a distribution of components with a broad range of frequencies whose eigenvalues and variance followed an approximate power law. During stimulus viewing, subpopulations of V1 neurons correlated either positively or negatively with low-dimensional fluctuations. These two subpopulations displayed distinct activation properties and noise correlations in response to sensory input. Together, results suggest that slow, low-dimensional fluctuations in V1 population activity shape the response of individual neurons to oriented stimuli and may impact the transmission of sensory information to downstream regions of the primary visual system.NEW & NOTEWORTHY A method termed frequency-separated principal component analysis (FS-PCA) is introduced for analyzing populations of simultaneously recorded neurons. This framework extends standard principal component analysis by extracting components of activity delimited to specific frequency bands. FS-PCA revealed that circuits of the primary visual cortex generate a broad range of components dominated by low-frequency activity. Furthermore, low-dimensional fluctuations in population activity modulated the response of individual neurons to sensory input.

Laminar Subnetworks of Response Suppression in Macaque Primary Visual Cortex

Cortical inhibition plays an important role in information processing in the brain. However, the mechanisms by which inhibition and excitation are coordinated to generate functions in the six layers of the cortex remain unclear. Here, we measured laminar-specific responses to stimulus orientations in primary visual cortex (V1) of awake monkeys (male, Macaca mulatta). We distinguished inhibitory effects (suppression) from excitation, by taking advantage of the separability of excitation and inhibition in the orientation and time domains. We found two distinct types of suppression governing different layers. Fast suppression (FS) was strongest in input layers (4C and 6), and slow suppression (SS) was 3 times stronger in output layers (2/3 and 5). Interestingly, the two types of suppression were correlated with different functional properties measured with drifting gratings. FS was primarily correlated with orientation selectivity in input layers (r = −0.65, p < 10⁻⁹), whereas SS was primarily correlated with surround suppression in output layers (r = 0.61, p < 10⁻⁴). The earliest SS in layer 1 indicates the origin of cortical feedback for SS, in contrast to the feedforward/recurrent origin of FS. Our results reveal two V1 laminar subnetworks with different response suppression that may provide a general framework for laminar processing in other sensory cortices.

SIGNIFICANCE STATEMENT This study sought to understand inhibitory effects (suppression) and their relationships with functional properties in the six different layers of the cortex. We found that the diversity of neural responses across layers in primary visual cortex (V1) could be fully explained by one excitatory and two suppressive components (fast and slow suppression). The distinct laminar distributions, origins, and functional roles of the two types of suppression provided a simplified representation of the differences between two V1 subnetworks (input network and output network). These results not only help to elucidate computational principles in macaque V1, but also provide a framework for general computation of cortical laminae in other sensory cortices.

Synaptic and intrinsic mechanisms underlying development of cortical direction selectivity

Modifications of synaptic inputs and cell-intrinsic properties both contribute to neuronal plasticity and development. To better understand these mechanisms, we undertook an intracellular analysis of the development of direction selectivity in the ferret visual cortex, which occurs rapidly over a few days after eye opening. We found strong evidence of developmental changes in linear spatiotemporal receptive fields of simple cells, implying alterations in circuit inputs. Further, this receptive field plasticity was accompanied by increases in near-spike-threshold excitability and input-output gain that resulted in dramatically increased spiking responses in the experienced state. Increases in subthreshold membrane responses induced by the receptive field plasticity and the increased input-output spiking gain were both necessary to explain the elevated firing rates in experienced ferrets. These results demonstrate that cortical direction selectivity develops through a combination of plasticity in inputs and in cell-intrinsic properties.

Voltage-Gated Intrinsic Conductances Shape the Input-Output Relationship of Cortical Neurons in Behaving Primate V1

Neurons are input-output (I/O) devices-they receive synaptic inputs from other neurons, integrate those inputs with their intrinsic properties, and generate action potentials as outputs. To understand this fundamental process, we studied the interaction between synaptic inputs and intrinsic properties using whole-cell recordings from V1 neurons of awake, fixating macaque monkeys. Our measurements during spontaneous activity and visual stimulation reveal an intrinsic voltage-gated conductance that profoundly alters the integrative properties and visual responses of cortical neurons. This voltage-gated conductance increases neuronal gain and selectivity with subthreshold depolarization and linearizes the relationship between synaptic input and neural output. This intrinsic conductance is found in layer 2/3 V1 neurons of awake macaques, anesthetized mice, and acute brain slices. These results demonstrate that intrinsic conductances play an essential role in shaping the I/O relationship of cortical neurons and must be taken into account in future models of cortical computations.

Interaction between steady-state visually evoked potentials at nearby flicker frequencies

Steady-state visually evoked potential (SSVEP) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

Contrast versus identity encoding in the face image follow distinct orientation selectivity profiles

Orientation selectivity is a fundamental property of primary visual encoding. High-level processing stages also show some form of orientation dependence, with face identification preferentially relying on horizontally-oriented information. How high-level orientation tuning emerges from primary orientation biases is unclear. In the same group of participants, we derived the orientation selectivity profile at primary and high-level visual processing stages using a contrast detection and an identity matching task. To capture the orientation selectivity profile, we calculated the difference in performance between all tested orientations (0, 45, 90, and 135°) for each task and for upright and inverted faces, separately. Primary orientation selectivity was characterized by higher sensitivity to oblique as compared to cardinal orientations. The orientation profile of face identification showed superior horizontal sensitivity to face identity. In each task, performance with upright and inverted faces projected onto qualitatively similar a priori models of orientation selectivity. Yet the fact that the orientation selectivity profiles of contrast detection in upright and inverted faces correlated significantly while such correlation was absent for identification indicates a progressive dissociation of orientation selectivity profiles from primary to high-level stages of orientation encoding. Bayesian analyses further indicate a lack of correlation between the orientation selectivity profiles in the contrast detection and face identification tasks, for upright and inverted faces. From these findings, we conclude that orientation selectivity shows distinct profiles at primary and high-level stages of face processing and that a transformation must occur from general cardinal attenuation when processing basic properties of the face image to horizontal tuning when encoding more complex properties such as identity.

Reconstruction of track candidates at the LHC crossing rate using FPGAs

In 2021 the LHCb experiment will be upgraded, and the DAQ system will be based on full reconstruction of events, at the full LHC crossing rate. This requires an entirely new system, capable of reading out, building and reconstructing events at an average rate of 30 MHz. In facing this challenge, the system could take advantage of a fast pre-processing of data on dedicated FPGAs. The results of an R&D on these technologies, developed in the context of the LHCb Upgrade I, are presented in this document. In particular, the details and potential benefits of an approach based on producing in real-time sorted collections of hits in the VELO detector (pre-tracks) are discussed. These pre-processed data can then be used as seeds by the High Level Trigger (HLT) farm to find tracks for the Level 1 trigger with much lower computational effort than possible by starting from the raw detector data, thus freeing an important fraction of the power of the CPU farm for higher level processing tasks.

Diversity of Feature Selectivity in Macaque Visual Cortex Arising from a Limited Number of Broadly Tuned Input Channels

Spike (action potential) responses of most primary visual cortical cells in the macaque are sharply tuned for the orientation of a line or an edge, and neurons preferring similar orientations are clustered together in cortical columns. The preferred stimulus orientation of these columns span the full range of orientations, as observed in recordings of spikes and in classical optical imaging of intrinsic signals. However, when we imaged the putative thalamic input to striate cortical cells that can be seen in imaging of intrinsic signals when they are analyzed on a larger spatial scale, we found that the orientation domain map of the primary visual cortex did not show the same diversity of orientations. This map was dominated by just the one orientation that is most commonly preferred by neurons in the retina and the lateral geniculate nucleus. This supports cortical feature selectivity and columnar architecture being built upon feed-forward signals transmitted from the thalamus in a very limited number of broadly tuned input channels.

Dynamics and sources of response variability and its coordination in visual cortex

The trial-to-trial response variability in sensory cortices and the extent to which this variability can be coordinated among cortical units have strong implications for cortical signal processing. Yet, little is known about the relative contributions and dynamics of defined sources to the cortical response variability and their correlations across cortical units. To fill this knowledge gap, here we obtained and analyzed multisite local field potential (LFP) recordings from visual cortex of turtles in response to repeated naturalistic movie clips and decomposed cortical across-trial LFP response variability into three defined sources, namely, input, network, and local fluctuations. We found that input fluctuations dominate cortical response variability immediately following stimulus onset, whereas network fluctuations dominate the response variability in the steady state during continued visual stimulation. Concurrently, we found that the network fluctuations dominate the correlations of the variability during the ongoing and steady-state epochs, but not immediately following stimulus onset. Furthermore, simulations of various model networks indicated that (i) synaptic time constants, leading to oscillatory activity, and (ii) synaptic clustering and synaptic depression, leading to spatially constrained pockets of coherent activity, are both essential features of cortical circuits to mediate the observed relative contributions and dynamics of input, network, and local fluctuations to the cortical LFP response variability and their correlations across recording sites. In conclusion, these results show how a mélange of multiscale thalamocortical circuit features mediate a complex stimulus-modulated cortical activity that, when naively related to the visual stimulus alone, appears disguised as high and coordinated across-trial response variability.

Layer 2/3 Pyramidal Neurons Control the Gain of Cortical Output

Initial anatomical and physiological studies suggested that sensory information relayed from the periphery by the thalamus is serially processed in primary sensory cortical areas. It is thought to propagate from layer 4 (L4) up to L2/3 and down to L5, which constitutes the main output of the cortex. However, more recent experiments point toward the existence of a direct processing of thalamic input by L5 neurons. Therefore, the role of L2/3 neurons in the sensory processing operated by L5 neurons is now highly debated. Using cell type-specific and reversible optogenetic manipulations in the somatosensory cortex of both anesthetized and awake mice, we demonstrate that L2/3 pyramidal neurons play a major role in amplifying sensory-evoked responses in L5 neurons. The amplification effect scales with the velocity of the sensory stimulus, indicating that L2/3 pyramidal neurons implement gain control in deep-layer neurons.

Spatial Asymmetry and Short-Term Suppression Underlie Direction Selectivity of Synaptic Excitation in the Mouse Visual Cortex

Direction selectivity (DS) of neuronal responses is fundamental for motion detection. With in vivo whole-cell voltage-clamp recordings from layer (L)4 neurons in the mouse visual cortex, we observed a strong correlation between DS and spatial asymmetry in the distribution of excitatory input strengths. This raises an interesting possibility that the latter may contribute to DS. The preferred direction of excitatory input was found from the stronger to weaker side of its spatial receptive field. A simple linear summation of asymmetrically distributed excitatory responses to stationary flash stimuli however failed to predict the correct directionality: it at best resulted in weak DS with preferred direction opposite to what was observed experimentally. Further studies with sequential 2 flash-bar stimulation revealed a short-term suppression of excitatory input evoked by the late bar. More importantly, the level of the suppression positively correlated with the relative amplitude of the early-bar response. Implementing this amplitude-dependent suppressive interaction can successfully predict DS of excitatory input. Our results suggest that via nonlinear temporal interactions, the spatial asymmetry can be transformed into differential temporal integration of inputs under opposite directional movements. This mechanism may contribute to the DS of excitatory inputs to L4 neurons.

Elementary motion sequence detectors in whisker somatosensory cortex

How somatosensory cortex (S1) encodes complex patterns of touch, as occur during tactile exploration, is poorly understood. In mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to 2-whisker sequences involving the columnar whisker (CW) and one, specific surround whisker (SW), usually in SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW-CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, providing a basis for higher-order feature extraction.

Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or 'neural decoding', has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder's dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis?2.2The informational benefits of multivariate pattern analysis3Why the Decoder's Dictum Is False 3.1We don't know what information is decoded3.2The theoretical basis for the dictum3.3Undermining the theoretical basis4Objections and Replies 4.1Does anyone really believe the dictum?4.2Good decoding is not enough4.3Predicting behaviour is not enough5Moving beyond the Dictum6Conclusion.

Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus

Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay "population-level stimulus representations" (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.

High-dimensional representation of texture in somatosensory cortex of primates

The sense of touch affords a remarkable sensitivity to the microstructure of surfaces, affording us the ability to sense elements ranging in size from tens of nanometers to tens of millimeters. The hand sends signals about texture to the brain using three classes of nerve fibers through two neural codes: coarse features in spatial patterns of activation and fine features in precise temporal spiking patterns. In this study, we show that these nerve signals culminate in a complex, high-dimensional representation of texture in somatosensory cortex, whose structure can account for the structure of texture perception. This complexity arises from the neurons that act as idiosyncratic detectors of spatial and/or temporal motifs in the afferent input.

In the somatosensory nerves, the tactile perception of texture is driven by spatial and temporal patterns of activation distributed across three populations of afferents. These disparate streams of information must then be integrated centrally to achieve a unified percept of texture. To investigate the representation of texture in somatosensory cortex, we scanned a wide range of natural textures across the fingertips of rhesus macaques and recorded the responses evoked in Brodmann’s areas 3b, 1, and 2. We found that texture identity is reliably encoded in the idiosyncratic responses of populations of cortical neurons, giving rise to a high-dimensional representation of texture. Cortical neurons fall along a continuum in their sensitivity to fine vs. coarse texture, and neurons at the extrema of this continuum seem to receive their major input from different afferent populations. Finally, we show that cortical responses can account for several aspects of texture perception in humans.

Titchener's T with flanks

Flanks were added to Titchener's (1901) T-illusion figure to test its susceptibility to context stimuli. The addition of a second divided line yielded H-type figures, and the addition of a second undivided line, +-type figures. The lengths of the Ts' undivided lines was expected to be overestimated relative to the lengths of the divided lines, when all lines were about equally long, and the illusion was expected to become smaller when one or two gaps had been introduced between the lines. Results conformed to the predictions. The amount of illusion was larger for the no-gap H than the T, and was almost annihilated with the two-gaps H, with 3 out of 14 observers showing an inverse response bias. The +-type stimuli produced analogous results. Findings are interpreted in terms of the nonequivalence of the endpoints of the stimuli's lines, which are thought to elicit different responses in end-inhibited cortical neurons, thereby affecting length estimates.

State dependence of stimulus-induced variability tuning in macaque MT

Behavioral states marked by varying levels of arousal and attention modulate some properties of cortical responses (e.g. average firing rates or pairwise correlations), yet it is not fully understood what drives these response changes and how they might affect downstream stimulus decoding. Here we show that changes in state modulate the tuning of response variance-to-mean ratios (Fano factors) in a fashion that is neither predicted by a Poisson spiking model nor changes in the mean firing rate, with a substantial effect on stimulus discriminability. We recorded motion-sensitive neurons in middle temporal cortex (MT) in two states: alert fixation and light, opioid anesthesia. Anesthesia tended to lower average spike counts, without decreasing trial-to-trial variability compared to the alert state. Under anesthesia, within-trial fluctuations in excitability were correlated over longer time scales compared to the alert state, creating supra-Poisson Fano factors. In contrast, alert-state MT neurons have higher mean firing rates and largely sub-Poisson variability that is stimulus-dependent and cannot be explained by firing rate differences alone. The absence of such stimulus-induced variability tuning in the anesthetized state suggests different sources of variability between states. A simple model explains state-dependent shifts in the distribution of observed Fano factors via a suppression in the variance of gain fluctuations in the alert state. A population model with stimulus-induced variability tuning and behaviorally constrained information-limiting correlations explores the potential enhancement in stimulus discriminability by the cortical population in the alert state.

The brain controls behavior fluidly in a wide variety of cognitive contexts that alter the precision of neural responses. We examine how neural variability changes versus the mean response as a function of the stimulus and the behavioral state. We show that this scaled variability can have qualitatively different stimulus tuning in different behavioral contexts. In alert primates, scaled variability is tuned to the direction of motion of a visual stimulus and decreases around the preferred direction of each neuron. Under anesthesia, neurons show flat scaled variability tuning and, overall, responses are significantly more variable. We develop a simple model that includes a parameter describing firing rate gain fluctuations that can explain these changes. Our results suggest that tuned decreases in scaled variability during wakefulness may be mediated by an active process that suppresses synchronization and makes information transmission more reliable.

Theoretical Principles of Multiscale Spatiotemporal Control of Neuronal Networks: A Complex Systems Perspective

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.

Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex

The connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map (such as rodents and lagomorphs) are poorly understood. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. The model predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing a shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole-cell recordings.

Transformation of Feature Selectivity From Membrane Potential to Spikes in the Mouse Superior Colliculus

Neurons in the visual system display varying degrees of selectivity for stimulus features such as orientation and direction. Such feature selectivity is generated and processed by intricate circuit and synaptic mechanisms. A key factor in this process is the input-output transformation from membrane potential (V_m) to spikes in individual neurons. Here, we use in vivo whole-cell recording to study V_m-to-spike transformation of visual feature selectivity in the superficial neurons of the mouse superior colliculus (SC). As expected from the spike threshold effect, direction and orientation selectivity increase from V_m to spike responses. The degree of this increase is highly variable, and interestingly, it is correlated with the receptive field size of the recorded neurons. We find that the relationships between V_m and spike rate and between V_m dynamics and spike initiation are also correlated with receptive field size, which likely contribute to the observed input-output transformation of feature selectivity. Together, our findings provide useful information for understanding information processing and visual transformation in the mouse SC.

Orientation-Selective Retinal Circuits in Vertebrates

Visual information is already processed in the retina before it is transmitted to higher visual centers in the brain. This includes the extraction of salient features from visual scenes, such as motion directionality or contrast, through neurons belonging to distinct neural circuits. Some retinal neurons are tuned to the orientation of elongated visual stimuli. Such ‘orientation-selective’ neurons are present in the retinae of most, if not all, vertebrate species analyzed to date, with species-specific differences in frequency and degree of tuning. In some cases, orientation-selective neurons have very stereotyped functional and morphological properties suggesting that they represent distinct cell types. In this review, we describe the retinal cell types underlying orientation selectivity found in various vertebrate species, and highlight their commonalities and differences. In addition, we discuss recent studies that revealed the cellular, synaptic and circuit mechanisms at the basis of retinal orientation selectivity. Finally, we outline the significance of these findings in shaping our current understanding of how this fundamental neural computation is implemented in the visual systems of vertebrates.

Suppression and Contrast Normalization in Motion Processing

Sensory neurons are activated by a range of stimuli to which they are said to be tuned. Usually, they are also suppressed by another set of stimuli that have little effect when presented in isolation. The interactions between preferred and suppressive stimuli are often quite complex and vary across neurons, even within a single area, making it difficult to infer their collective effect on behavioral responses mediated by activity across populations of neurons. Here, we investigated this issue by measuring, in human subjects (three males), the suppressive effect of static masks on the ocular following responses induced by moving stimuli. We found a wide range of effects, which depend in a nonlinear and nonseparable manner on the spatial frequency, contrast, and spatial location of both stimulus and mask. Under some conditions, the presence of the mask can be seen as scaling the contrast of the driving stimulus. Under other conditions, the effect is more complex, involving also a direct scaling of the behavioral response. All of this complexity at the behavioral level can be captured by a simple model in which stimulus and mask interact nonlinearly at two stages, one monocular and one binocular. The nature of the interactions is compatible with those observed at the level of single neurons in primates, usually broadly described as divisive normalization, without having to invoke any scaling mechanism.SIGNIFICANCE STATEMENT The response of sensory neurons to their preferred stimulus is often modulated by stimuli that are not effective when presented alone. Individual neurons can exhibit multiple modulatory effects, with considerable variability across neurons even in a single area. Such diversity has made it difficult to infer the impact of these modulatory mechanisms on behavioral responses. Here, we report the effects of a stationary mask on the reflexive eye movements induced by a moving stimulus. A model with two stages, each incorporating a divisive modulatory mechanism, reproduces our experimental results and suggests that qualitative variability of masking effects in cortical neurons might arise from differences in the extent to which such effects are inherited from earlier stages.

Bisphenol A exposure perturbs visual function of adult cats by remodeling the neuronal activity in the primary visual pathway

Bisphenol A (BPA), a common environmental xenoestrogen, has been implicated in physiological and behavioral impairment, but the neuronal basis remains elusive. Although various synaptic mechanisms have been shown to mediate BPA-induced brain deficits, there are almost no reports addressing its underlying physiological mechanisms at the individual neuron level, particularly in the primary visual system. In the present study, using multiple-channel recording technique, we recorded the responses of single neurons in the primary visual system of cats to various direction stimuli both before and after BPA exposure. The results showed that the orientation selectivity of neurons in the primary visual cortex (area 17, A17) was obviously decreased after 2 h of intravenous BPA administration (0.2 mg/kg). Moreover, there were worse performances of information transmission of A17 neurons, presenting markedly decreased signal-to-noise ratio (SNR). To some extent, these functional decreases were attributable to the altered information inputs from lateral geniculate nucleus (LGN), which showed an increased spontaneous activity. Additionally, local injection of BPA (3.3 μg/ml) in A17 resulted in an obvious increase in orientation selectivity and a decrease in neuronal activity, involving enhanced activity of fast-spiking inhibitory interneurons. In conclusion, our results first demonstrate that acute BPA exposure can restrict the visual perception of cats, mainly depending on the alteration of the LGN projection, not the intercortical interaction. Importantly, BPA-induced-brain deficits might not only be confined to the cortical level but also occur as early as at the subcortical level.