Parallel Advantage: Further Evidence for Bottom-up Saliency Computation by Human Primary Visual Cortex

Finding a target among uniformly oriented non-targets is typically faster when this target is perpendicular, rather than parallel, to the non-targets. The V1 Saliency Hypothesis (V1SH), that neurons in the primary visual cortex (V1) signal saliency for exogenous attentional attraction, predicts exactly the opposite in a special case: each target or non-target comprises two equally sized disks displaced from each other by 1.2 disk diameters center-to-center along a line defining its orientation. A target has two white or two black disks. Each non-target has one white disk and one black disk, and thus, unlike the target, activates V1 neurons less when its orientation is parallel rather than perpendicular to the neurons’ preferred orientations. When the target is parallel, rather than perpendicular, to the uniformly oriented non-targets, the target’s evoked V1 response escapes V1’s iso-orientation surround suppression, making the target more salient. I present behavioral observations confirming this prediction.

Sensory prediction errors increase coding efficiency in mouse visual cortex through gain amplification

The efficiency of sensory coding is affected both by past events (adaptation) and by expectation of future events (prediction). Here we employed a novel visual stimulus paradigm to determine whether expectation influences orientation selectivity in the primary visual cortex. We used two-photon calcium imaging (GCaMP6f) in awake mice viewing visual stimuli with different levels of predictability. The stimuli consisted of sequences of grating stimuli that randomly shifted in orientation or systematically rotated with occasionally unexpected rotations. At the single neuron and population level, there was significantly enhanced orientation-selective response to unexpected visual stimuli through a boost in gain, which was prominent in awake mice but also present to a lesser extent under anesthesia. We implemented a computational model to demonstrate how neuronal responses were best characterized when adaptation and expectation parameters were combined. Our results demonstrated that adaptation and prediction have unique signatures on activity of V1 neurons.

A cortical circuit for audio-visual predictions

Learned associations between stimuli in different sensory modalities can shape the way we perceive these stimuli. However, it is not well understood how these interactions are mediated or at what level of the processing hierarchy they occur. Here we describe a neural mechanism by which an auditory input can shape visual representations of behaviorally relevant stimuli through direct interactions between auditory and visual cortices in mice. We show that the association of an auditory stimulus with a visual stimulus in a behaviorally relevant context leads to experience-dependent suppression of visual responses in primary visual cortex (V1). Auditory cortex axons carry a mixture of auditory and retinotopically matched visual input to V1, and optogenetic stimulation of these axons selectively suppresses V1 neurons that are responsive to the associated visual stimulus after, but not before, learning. Our results suggest that cross-modal associations can be communicated by long-range cortical connections and that, with learning, these cross-modal connections function to suppress responses to predictable input.

Why Hubel and Wiesel never go out of style

The simple, speculative account of the functional properties of V1 neurons proffered more than half a century ago by Hubel and Wiesel has not proven empirically fruitful. Yet, despite its age, theoretical problems and empirical impotence, this work continues to be uncritically cited as a basic premise of (chronically non-replicable) V1 research, due to the prioritization of simple math over empirical validity.

Transient inhibition to light explains stronger V1 responses to dark stimuli

Neurons in the primary visual cortex (V1) receive excitation and inhibition from two different pathways processing lightness (ON) and darkness (OFF). V1 neurons overall respond more strongly to dark than light stimuli (Yeh, Xing and Shapley, 2010; Kremkow et al., 2014), consistent with a preponderance of darker regions in natural images (Ratliff et al., 2010), as well as human psychophysics (Buchner & Baumgartner, 2007). However, it has been unclear whether this "dark-dominance" is due to more excitation from the OFF pathway (Jin et al., 2008) or more inhibition from the ON pathway (Taylor et al., 2018). To understand the mechanisms behind dark-dominance, we record electrophysiological responses of individual simple-type V1 neurons to natural image stimuli and then train biologically inspired convolutional neural networks to predict the neuronal responses. Analyzing a sample of 74 neurons (in anesthetized, paralyzed cats) has revealed their responses to be more driven by dark than light stimuli, consistent with previous investigations (Yeh et al., 2010; Kremkow et al., 2013). We show this asymmetry to be predominantly due to slower inhibition to dark stimuli rather than by stronger excitation from the thalamocortical OFF pathway. Consistent with dark-dominant neurons having faster responses than light-dominant neurons (Komban et al., 2014), we find dark-dominance to solely occur in the early latencies of neuronal responses. Neurons that are strongly dark-dominated also tend to be less orientation selective. This novel approach gives us new insight into the dark-dominance phenomenon and provides an avenue to address new questions about excitatory and inhibitory integration in cortical neurons.

Traumatic brain injury to primary visual cortex produces long-lasting circuit dysfunction

Primary sensory areas of the mammalian neocortex have a remarkable degree of plasticity, allowing neural circuits to adapt to dynamic environments. However, little is known about the effects of traumatic brain injury on visual circuit function. Here we used anatomy and in vivo electrophysiological recordings in adult mice to quantify neuron responses to visual stimuli two weeks and three months after mild controlled cortical impact injury to primary visual cortex (V1). We found that, although V1 remained largely intact in brain-injured mice, there was ~35% reduction in the number of neurons that affected inhibitory cells more broadly than excitatory neurons. V1 neurons showed dramatically reduced activity, impaired responses to visual stimuli and weaker size selectivity and orientation tuning in vivo. Our results show a single, mild contusion injury produces profound and long-lasting impairments in the way V1 neurons encode visual input. These findings provide initial insight into cortical circuit dysfunction following central visual system neurotrauma.

Beyond Fixation: detailed characterization of neural selectivity in free-viewing primates

Virtually all vision studies use a fixation point to stabilize gaze, rendering stimuli on video screens fixed to retinal coordinates. This approach requires trained subjects, is limited by the accuracy of fixational eye movements, and ignores the role of eye movements in shaping visual input. To overcome these limitations, we developed a suite of hardware and software tools to study vision during natural behavior in untrained subjects. We show this approach recovers receptive fields and tuning properties of visual neurons from multiple cortical areas of marmoset monkeys. Combined with high-precision eye-tracking, it achieves sufficient resolution to recover the receptive fields of foveal V1 neurons. These findings demonstrate the power of this approach for characterizing neural response while simultaneously studying the dynamics of natural behavior.

Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome

Many people affected by fragile X syndrome (FXS) and autism spectrum disorders have sensory processing deficits, such as hypersensitivity to auditory, tactile, and visual stimuli. Like FXS in humans, loss of Fmr1 in rodents also cause sensory, behavioral, and cognitive deficits. However, the neural mechanisms underlying sensory impairment, especially vision impairment, remain unclear. It remains elusive whether the visual processing deficits originate from corrupted inputs, impaired perception in the primary sensory cortex, or altered integration in the higher cortex, and there is no effective treatment. In this study, we used a genetic knockout mouse model (Fmr1KO), in vivo imaging, and behavioral measurements to show that the loss of Fmr1 impaired signal processing in the primary visual cortex (V1). Specifically, Fmr1KO mice showed enhanced responses to low-intensity stimuli but normal responses to high-intensity stimuli. This abnormality was accompanied by enhancements in local network connectivity in V1 microcircuits and increased dendritic complexity of V1 neurons. These effects were ameliorated by the acute application of GABAA receptor activators, which enhanced the activity of inhibitory neurons, or by reintroducing Fmr1 gene expression in knockout V1 neurons in both juvenile and young-adult mice. Overall, V1 plays an important role in the visual abnormalities of Fmr1KO mice and it could be possible to rescue the sensory disturbances in developed FXS and autism patients.

Sensory prediction errors increase coding efficiency in mouse visual cortex through gain amplification

The efficiency of sensory coding is affected both by past events (adaptation) and by expectation of future events (prediction). Here we employed a novel visual stimulus paradigm to determine whether expectation influences orientation selectivity in the primary visual cortex. We used two-photon calcium imaging (GCaMP6f) in awake mice viewing visual stimuli with different levels of predictability. The stimuli consisted of sequences of grating stimuli that randomly shifted in orientation or systematically rotated with occasionally unexpected rotations. At the single neuron and population level, there was significantly enhanced orientation-selective response to unexpected visual stimuli through a boost in gain, which was prominent in awake mice but also present to a lesser extent under anesthesia. We implemented a computational model to demonstrate how neuronal responses were best characterized when adaptation and expectation parameters were combined. Our results demonstrated that adaptation and prediction have unique signatures on activity of V1 neurons.