Cells In Primary Visual Cortex Research Articles

Object Boundary Detection in Natural Images May Depend on "Incitatory" Cell-Cell Interactions.

Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional "simple cells," we labeled 30,000 natural image patches and used Bayes' rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified the following three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, and nonmonotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models, we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition-a compound effect we call "incitation"-can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer "delta" rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems.SIGNIFICANCE STATEMENT Simple cells in primary visual cortex (V1) respond to oriented edges and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell-a divisively normalized linear filter-is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex.

We studied the changes that neuronal receptive field (RF) models undergo when the statistics of the stimulus are changed from those of white Gaussian noise (WGN) to those of natural scenes (NSs), by fitting the models to multielectrode data recorded from primary visual cortex (V1) of female cats. This allowed the estimation of both a cascade of linear filters on the stimulus, as well as the static nonlinearities that map the output of the filters to the neuronal spike rates. We found that cells respond differently to these two classes of stimuli, with mostly higher spike rates and shorter response latencies to NSs than to WGN. The most striking finding was that NSs resulted in RFs that had additional uncovered filters compared with WGN. This finding was not an artifact of the higher spike rates observed for NSs relative to WGN, but rather was related to a change in coding. Our results reveal a greater extent of nonlinear processing in V1 neurons when stimulated using NSs compared with WGN. Our findings indicate the existence of nonlinear mechanisms that endow V1 neurons with context-dependent transmission of visual information.SIGNIFICANCE STATEMENT This study addresses a fundamental question about the concept of the receptive field (RF): does the encoding of information depend on the context or statistical regularities of the stimulus type? We applied state-of-the-art RF modeling techniques to data collected from multielectrode recordings from cat visual cortex in response to two statistically distinct stimulus types: white Gaussian noise and natural scenes. We find significant differences between the RFs that emerge from our data-driven modeling. Natural scenes result in far more complex RFs that combine multiple features in the visual input. Our findings reveal that different regimes or modes of operation are at work in visual cortical processing depending on the information present in the visual input. The complexity of V1 neural coding appears to be dependent on the complexity of the stimulus. We believe this new finding will have interesting implications for our understanding of the efficient transmission of information in sensory systems, which is an integral assumption of many computational theories (e.g., efficient and predictive coding of sensory processing in the brain).

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells.

Unsupervised adaptation to the spatiotemporal statistics of visual experience is a key computational principle that has long been assumed to govern postnatal development of visual cortical tuning, including orientation selectivity of simple cells and position tolerance of complex cells in primary visual cortex (V1). Yet, causal empirical evidence supporting this hypothesis is scant. Here, we show that degrading the temporal continuity of visual experience during early postnatal life leads to a sizable reduction of the number of complex cells and to an impairment of their functional properties while fully sparing the development of simple cells. This causally implicates adaptation to the temporal structure of the visual input in the development of transformation tolerance but not of shape tuning, thus tightly constraining computational models of unsupervised cortical learning.

The Phase and Shift-Invariant Feature by Adaptive Independent Subspace Analysis for Cortical Complex Cells

It has previously been suggested that complex cells in primary visual cortex (V1) selectly respond to bars and edges at a particular location and orientation emerged from the natural image rather than random stimuli. In this paper, we proposed an adaptive independent subspace analysis (AISA) algorithm based on the maximum likelihood of super-gaussian distribution of the norm of the partially independent components involving difference to efficiently study the characteristics of complex cells. The multiple parameters are updated with different restrictions by contrast divergence (CD) method based on the super-Gaussian (sparse) distribution. In terms of the AISA results, the inside and outside subspace of energy correlation illustrate the properties of independence. Furthermore, AISA features, similar to the characteristics of complex cells in V1, have phase and shift-invariant, as well as frequency and orientation selectivity via the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function. Compared with the principal component analysis (PCA) filters and other ICA filters, AISA filters are analogous to Gabor filters for edge detection and human visual system via varying the frequency and orientation representation.

Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding.

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.

Age-Related Structural Changes in Primary Visual Cortex Cells of Rats under High-Intensity Light Exposure

An experiment using 20 white male Wistar rats has shown that exposure to 3500 lux of light for 7 days causes morphological changes in the II, IV, and V layers of the primary visual cortex. This was seen in an increased percentage of reversibly and irreversibly altered neurons, mainly in the fourth layer of 18-month-old rats (p ≤ 0.05). In response to light exposure, the percentage of hyperchromic wrinkled neurons in 18‑month-old rats rises to 6% (5; 8.5), the percentage of neurons with total chromatolysis rises to 10% (8.5; 14) compared to a 1% (0.5; 14) and 6% (5; 8) rise in 3-month-old rats respectively (p ≤ 0.05). The neural damage leads to a glial reaction reflected in an increased percentage of glia with signs of edema and swelling, hyperchromia without the nucleus and cytoplasm shrinkage (p ≤ 0.05), neuronophagia, and also the intrusion of the glia cells into the neuron cytoplasm for the initiation of intracellular reparation. The destructive changes are characterized by the gliacyte hyperchromia with the shrinkage of the nucleus and cytoplasm. The percentage of these gliacytes increases significantly in 18-month-old rats under the light exposure compared to the indices in young animals (p ≤ 0.05).

Levelt's propositions examined at the level of mutually inhibiting pyramidal cells in primary visual cortex

Levelt's propositions examined at the level of mutually inhibiting pyramidal cells in primary visual cortex

Describing complex cells in primary visual cortex: a comparison of context and multifilter LN models.

Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multifilter linear-nonlinear (LN) models and context models. Models are, however, never correct, and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: 1) we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions, and 2) we evaluate context models and multifilter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multifilter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multifilter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantizations of neural behavior.NEW & NOTEWORTHY We used data from complex cells in primary visual cortex to estimate a wide variety of receptive field models from two frameworks that have previously not been compared with each other. The models included traditionally used multifilter linear-nonlinear models and novel variants of context models. Using mutual information and correlation coefficients as performance measures, we showed that context models are superior for describing complex cells and that the novel context models performed the best.

Big data analytics enabled by feature extraction based on partial independence

Abstract Complex cells in primary visual cortex (V1) selectively respond to bars and edges at a particular location and orientation. Namely, they are relatively invariant to the phase as well as selective to the frequency and orientation emerging from natural images that are analogous to the characteristics of complex cells in V1 with the energy function of receptive fields (RFs) from tuning curve test with sinusoidal function in our related jobs. In this paper, we propose a feature learning algorithm based on the overcomplete AISA to apply on big data in parallel computing. In order to demonstrate the effectiveness of the overcomplete AISA features in the classification task, two feature representation architectures are evolved into the partial independent signal bases and partial independent factorial representation, respectively. Experiments on four datasets (Coil20, Extended YaleB, USPS, PIE), acquired conjunction with two classification architectures based on the overcomplete AISA features, show that the classification accuracy is mostly higher than those obtained from the other ICA related features and two other sparse representation features with a small number of training samples via nearest neighbor (NN) classification method.

Inhibition in Simple Cell Receptive Fields Is Broad and OFF-Subregion Biased.

Inhibition in thalamorecipient layer 4 simple cells of primary visual cortex is believed to play important roles in establishing visual response properties and integrating visual inputs across their receptive fields (RFs). Simple cell RFs are characterized by nonoverlapping, spatially restricted subregions in which visual stimuli can either increase or decrease the firing rate of the cell, depending on contrast. Inhibition is believed to be triggered exclusively from visual stimulation of individual RF subregions. However, this view is at odds with the known anatomy of layer 4 interneurons in visual cortex and differs from recent findings in mouse visual cortex. Here we show with in vivo intracellular recordings in cats that while excitation is restricted to RF subregions, inhibition spans the width of simple cell RFs. Consequently, excitatory stimuli within a subregion concomitantly drive excitation and inhibition. Furthermore, we found that the distribution of inhibition across the RF is stronger toward OFF subregions. This inhibitory OFF-subregion bias has a functional consequence on spatial integration of inputs across the RF. A model based on the known anatomy of layer 4 demonstrates that the known proportion and connectivity of inhibitory neurons in layer 4 of primary visual cortex is sufficient to explain broad inhibition with an OFF-subregion bias while generating a variety of phase relations, including antiphase, between excitation and inhibition in response to drifting gratings.SIGNIFICANCE STATEMENT The wiring of excitatory and inhibitory neurons in cortical circuits is key to determining the response properties in sensory cortex. In the visual cortex, the first cells that receive visual input are simple cells in layer 4. The underlying circuitry responsible for the response properties of simple cells is not yet known. In this study, we challenge a long-held view concerning the pattern of inhibitory input and provide results that agree with current known anatomy. We show here that inhibition is evoked broadly across the receptive fields of simple cells, and we identify a surprising bias in inhibition within the receptive field. Our findings represent a step toward a unified view of inhibition across different species and sensory systems.

Developmental Disruption of GABAAR-Meditated Inhibition in Cntnap2 KO Mice.

GABA released from presynaptic sites induces short-lived phasic inhibition mediated by synaptic GABAA receptors (GABAARs) and longer-duration tonic inhibition mediated by extrasynaptic GABAA or GABAB receptors (GABABRs). A number of studies have found that contactin-associated protein 2 (Cntnap2) knockout (KO) mice, a well-established mouse model of autism, exhibit reduced interneuron numbers and aberrant phasic inhibition. However, little is known about whether tonic inhibition is disrupted in Cntnap2 KO mice and when the disruption of inhibition begins to occur during postnatal development. We examined tonic and phasic inhibition in layer 2/3 pyramidal cells of primary visual cortex of Cntnap2 KO at two different developmental stages, three to four and six to eight weeks of age. We found that both phasic inhibition and GABAAR but not GABABR-mediated tonic inhibition was reduced in pyramidal cells from six- to eight-week-old Cntnap2 KO mice, while in three- to four-week-old mice, no significant effects of genotype on tonic or phasic inhibition was observed. We further found that activation of tonic currents mediated by δ-subunit-containing GABAARs reduced neural excitability, an effect that was attenuated by loss of Cntnap2. While the relative contribution of tonic versus phasic inhibition to autism-related symptoms remains unclear, our data suggest that reduced tonic inhibition may play an important role, and δ-subunit-containing GABAARs may be a useful target for therapeutic intervention in autism.

Mutual inhibition circuit as underlying mechanism for bi-stable perception and non-linear responses in vision

It has been hypothesized that mutual inhibition circuit underlies bi-stable perception. It is possible that this circuit functions to resolve ambiguity of input image by quickly shifting the balance of competing signals in response to conflicting features. Furthermore, it is possible that feedback signals influence this "biased competition" to reflect global properties of the image. I developed a system to record from two pyramidal cells in primary visual cortex in vitro by double patch clamp recording technique combined with so-called "dynamic clamp" system. In this system, two neurons can be connected by modeled synapses. When a connection between the two neurons are established by mutually inhibiting model synapses, simultaneous activation of the two neurons caused bi-stable activities: dominance of neural activities alternated between the two neurons in about 1~2 seconds interval. Next, I ran paradigms analogous to various experimental paradigms used for behavioral studies of bi-stable perception. Levelt's 4th law states that increasing the strength of input contrast causes increase of switching frequency. Similarly, increasing the depolarizing current injected to both neurons showed increase of switching frequencies at the higher range of injected current. Flash suppression paradigm was modeled by injecting depolarizing current to one neuron for few seconds followed by simultaneous injections of the current to the two neurons. The simultaneous injection evoked bi-stability that mostly started with the dominant activity of the non-adapted neuron. Intermittent presentation of bi-stable figure is known to prolong the percept duration. When this paradigm was modeled by injecting depolarizing current intermittently, the prolongation effect was not observed. Finally, by placing a stimulus electrode to V2, feedback excitation was evoked. The stimulation often caused the switch of dominance. Hence, feedback signals can influence the outcome of the competition. With this system, the details of neural processes that undergo bi-stable activities can be investigated. Meeting abstract presented at VSS 2017

Motion Vision: Cortical Preferences Influenced by Retinal Direction Selectivity

A recent study shows that retinal direction selectivity influences a subset of cells in primary visual cortex which respond to the optic flow associated with forward motion, while other cortical direction selective cells perform this computation independently.

Postnatal Dendritic Growth and Spinogenesis of Layer-V Pyramidal Cells Differ between Visual, Inferotemporal, and Prefrontal Cortex of the Macaque Monkey.

Pyramidal cells in the primate cerebral cortex, particularly those in layer III, exhibit regional variation in both the time course and magnitude of postnatal growth and pruning of dendrites and spines. Less is known about the development of pyramidal cell dendrites and spines in other cortical layers. Here we studied dendritic morphology of layer-V pyramidal cells in primary visual cortex (V1, sensory), cytoarchitectonic area TE in the inferotemporal cortex (sensory association), and granular prefrontal cortex (Walker's area 12, executive) of macaque monkeys at the ages of 2 days, 3 weeks, 3.5 months, and 4.5 years. We found that changes in the basal dendritic field area of pyramidal cells were different across the three areas. In V1, field size became smaller over time (largest at 2 days, half that size at 4.5 years), in TE it did not change, and in area 12 it became larger over time (smallest at 2 days, 1.5 times greater at 4.5 years). In V1 and TE, the total number of branch points in the basal dendritic trees was similar between 2 days and 4.5 years, while in area 12 the number was greater in the adult monkeys than in the younger ones. Spine density peaked at 3 weeks and declined in all areas by adulthood, with V1 exhibiting a faster decline than area TE or area 12. Estimates of the total number of spines in the dendritic trees revealed that following the onset of visual experience, pyramidal cells in V1 lose more spines than they grow, whereas those in TE and area 12 grow more spines than they lose during the same period. These data provide further evidence that the process of synaptic refinement in cortical pyramidal cells differs not only according to time, but also location within the cortex. Furthermore, given the previous finding that layer-III pyramidal cells in all these areas exhibit the highest density and total number of spines at 3.5 months, the current results indicate that pyramidal cells in layers III and V develop spines at different rates.

Synaptic Mechanisms Generating Orientation Selectivity in the ON Pathway of the Rabbit Retina.

A core goal for visual neuroscientists is to understand how neural circuits at each stage of the visual system extract and encode features from the visual scene. This study documents a novel type of orientation-selective ganglion cell in the retina and shows that the receptive field structure is remarkably similar to that of simple cells in primary visual cortex. However, the data indicate that, unlike in the cortex, orientation selectivity in the retina depends on the activity of inhibitory interneurons. The results further reveal the physiological basis for feature detection in the visual system, elucidate the synaptic mechanisms that generate orientation selectivity at an early stage of visual processing, and illustrate a novel role for NMDA receptors in retinal processing.

Spatial phase sensitivity of complex cells in primary visual cortex depends on stimulus contrast

Neurons in primary visual cortex are classified as simple, which are phase sensitive, or complex, which are significantly less phase sensitive. Previously, we have used drifting gratings to show that the phase sensitivity of complex cells increases at low contrast and after contrast adaptation while that of simple cells remains the same at all contrasts (Cloherty SL, Ibbotson MR. J Neurophysiol 113: 434-444, 2015; Crowder NA, van Kleef J, Dreher B, Ibbotson MR. J Neurophysiol 98: 1155-1166, 2007; van Kleef JP, Cloherty SL, Ibbotson MR. J Physiol 588: 3457-3470, 2010). However, drifting gratings confound the influence of spatial and temporal summation, so here we have stimulated complex cells with gratings that are spatially stationary but continuously reverse the polarity of the contrast over time (contrast-reversing gratings). By varying the spatial phase and contrast of the gratings we aimed to establish whether the contrast-dependent phase sensitivity of complex cells results from changes in spatial or temporal processing or both. We found that most of the increase in phase sensitivity at low contrasts could be attributed to changes in the spatial phase sensitivities of complex cells. However, at low contrasts the complex cells did not develop the spatiotemporal response characteristics of simple cells, in which paired response peaks occur 180° out of phase in time and space. Complex cells that increased their spatial phase sensitivity at low contrasts were significantly overrepresented in the supragranular layers of cortex. We conclude that complex cells in supragranular layers of cat cortex have dynamic spatial summation properties and that the mechanisms underlying complex cell receptive fields differ between cortical layers.

Three counting methods agree on cell and neuron number in chimpanzee primary visual cortex.

Determining the cellular composition of specific brain regions is crucial to our understanding of the function of neurobiological systems. It is therefore useful to identify the extent to which different methods agree when estimating the same properties of brain circuitry. In this study, we estimated the number of neuronal and non-neuronal cells in the primary visual cortex (area 17 or V1) of both hemispheres from a single chimpanzee. Specifically, we processed samples distributed across V1 of the right hemisphere after cortex was flattened into a sheet using two variations of the isotropic fractionator cell and neuron counting method. We processed the left hemisphere as serial brain slices for stereological investigation. The goal of this study was to evaluate the agreement between these methods in the most direct manner possible by comparing estimates of cell density across one brain region of interest in a single individual. In our hands, these methods produced similar estimates of the total cellular population (approximately 1 billion) as well as the number of neurons (approximately 675 million) in chimpanzee V1, providing evidence that both techniques estimate the same parameters of interest. In addition, our results indicate the strengths of each distinct tissue preparation procedure, highlighting the importance of attention to anatomical detail. In summary, we found that the isotropic fractionator and the stereological optical fractionator produced concordant estimates of the cellular composition of V1, and that this result supports the conclusion that chimpanzees conform to the primate pattern of exceptionally high packing density in V1. Ultimately, our data suggest that investigators can optimize their experimental approach by using any of these counting methods to obtain reliable cell and neuron counts.

Slow feature analysis on retinal waves leads to V1 complex cells.

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.

Slowness and sparseness have diverging effects on complex cell learning.

Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA) in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA) with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function.

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.