Contrast-invariant Orientation Tuning Research Articles

In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules

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

Neurophysiological basis of contrast dependent BOLD orientation tuning

Recent work in early visual cortex of humans has shown that the BOLD signal exhibits contrast dependent orientation tuning, with an inverse oblique effect (oblique > cardinal) at high contrast and a horizontal effect (vertical > horizontal) at low contrast. This finding is at odds with decades of neurophysiological research demonstrating contrast invariant orientation tuning in primate visual cortex, yet the source of this discrepancy is unclear. We hypothesized that contrast dependent BOLD orientation tuning may arise due to contrast dependent influences of feedforward (FF) and feedback (FB) synaptic activity, indexed through gamma and alpha rhythms, respectively. To quantify this, we acquired EEG and BOLD in healthy humans to generate and compare orientation tuning curves across all neural frequency bands with BOLD. As expected, BOLD orientation selectivity in V1 was contrast dependent, preferring oblique orientations at high contrast and vertical at low contrast. On the other hand, EEG orientation tuning was contrast invariant, though frequency-specific, with an inverse-oblique effect in the gamma band (FF) and a horizontal effect in the alpha band (FB). Therefore, high-contrast BOLD orientation tuning closely matched FF activity, while at low contrast, BOLD best resembled FB orientation tuning. These results suggest that contrast dependent BOLD orientation tuning arises due to the reduced contribution of FF input to overall neurophysiological activity at low contrast, shifting BOLD orientation tuning towards the orientation preferences of FB at low contrast.

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.

Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width.

Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums of groups of neurons (channels) tuned for visual stimulus properties. Inverting these models to form predicted channel responses from population measurements in humans seemingly offers the potential to infer neuronal tuning properties. Here, we test the ability to make inferences about neural tuning width from inverted encoding models. We examined contrast invariance of orientation selectivity in human V1 (both sexes) and found that inverting the encoding model resulted in channel response functions that became broader with lower contrast, thus apparently violating contrast invariance. Simulations showed that this broadening could be explained by contrast-invariant single-unit tuning with the measured decrease in response amplitude at lower contrast. The decrease in response lowers the signal-to-noise ratio of population responses that results in poorer population representation of orientation. Simulations further showed that increasing signal to noise makes channel response functions less sensitive to underlying neural tuning width, and in the limit of zero noise will reconstruct the channel function assumed by the model regardless of the bandwidth of single units. We conclude that our data are consistent with contrast-invariant orientation tuning in human V1. More generally, our results demonstrate that population selectivity measures obtained by encoding models can deviate substantially from the behavior of single units because they conflate neural tuning width and noise and are therefore better used to estimate the uncertainty of decoded stimulus properties.SIGNIFICANCE STATEMENT It is widely recognized that perceptual experience arises from large populations of neurons, rather than a few single units. Yet, much theory and experiment have examined links between single units and perception. Encoding models offer a way to bridge this gap by explicitly interpreting population activity as the aggregate response of many single neurons with known tuning properties. Here we use this approach to examine contrast-invariant orientation tuning of human V1. We show with experiment and modeling that due to lower signal to noise, contrast-invariant orientation tuning of single units manifests in population response functions that broaden at lower contrast, rather than remain contrast-invariant. These results highlight the need for explicit quantitative modeling when making a reverse inference from population response profiles to single-unit responses.

Comparison of mechanisms for contrast-invariance of orientation selectivity in simple cells

The simple cells of the visual cortex respond over a narrow range of stimulus orientations, and this tuning is invariant to the contrast at which the stimulus is presented. The inputs to a single cell derive from a population of thalamic cells that provide a bell-shaped tuning width and offset that increases with stimulus contrast. Synaptic depression, noise and inhibition have been proposed as feedforward mechanisms to explain why these increases do not appear in simple cells. The extent to which these three mechanisms contribute to contrast-invariant orientation tuning is unknown. Consequently, the aim was to test the hypothesis that these mechanisms do not contribute equally. Unlike previous studies, all mechanisms were examined using the same network model based on Banitt et al. (2007). The results showed that thalamocortical synaptic noise was essential and sufficient to widen tuning widths at low contrasts to that of higher contrasts but could not counteract the offset at higher contrasts.Thalamocortical synaptic depression could only be used to counteract a small fraction of the offset otherwise the relationship between contrast and response rate was disrupted. Only broadly tuned simple and complex cell inhibition could counteract the remaining offset for all stimulus contrasts but complex cell inhibition reduced the gain of the response. These results suggest unequal contributions of these feedforward mechanisms with thalamic synaptic noise widening tuning widths for low contrasts, synaptic depression counteracting a small component of the offset and synaptic inhibition completely removing the remaining offset to produce contrast-invariant orientation tuning.

Contrast invariance of orientation tuning in the lateral geniculate nucleus of the feline visual system.

Responses of most neurons in the primary visual cortex of mammals are markedly selective for stimulus orientation and their orientation tuning does not vary with changes in stimulus contrast. The basis of such contrast invariance of orientation tuning has been shown to be the higher variability in the response for low-contrast stimuli. Neurons in the lateral geniculate nucleus (LGN), which provides the major visual input to the cortex, have also been shown to have higher variability in their response to low-contrast stimuli. Parallel studies have also long established mild degrees of orientation selectivity in LGN and retinal cells. In our study, we show that contrast invariance of orientation tuning is already present in the LGN. In addition, we show that the variability of spike responses of LGN neurons increases at lower stimulus contrasts, especially for non-preferred orientations. We suggest that such contrast- and orientation-sensitive variability not only explains the contrast invariance observed in the LGN but can also underlie the contrast-invariant orientation tuning seen at the level of the primary visual cortex.

Effects of stimulus spatial frequency, size, and luminance contrast on orientation tuning of neurons in the dorsal lateral geniculate nucleus of cat

It is generally thought that orientation selectivity first appears in the primary visual cortex (V1), whereas neurons in the lateral geniculate nucleus (LGN), an input source for V1, are thought to be insensitive to stimulus orientation. Here we show that increasing both the spatial frequency and size of the grating stimuli beyond their respective optimal values strongly enhance the orientation tuning of LGN neurons. The resulting orientation tuning was clearly contrast-invariant. Furthermore, blocking intrathalamic inhibition by iontophoretically administering γ-aminobutyric acid (GABA)A receptor antagonists, such as bicuculline and GABAzine, slightly but significantly weakened the contrast invariance. Our results suggest that orientation tuning in the LGN is caused by an elliptical classical receptive field and orientation-tuned surround suppression, and that its contrast invariance is ensured by local GABAA inhibition. This contrast-invariant orientation tuning in LGN neurons may contribute to the contrast-invariant orientation tuning seen in V1 neurons.

Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System

Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.

A CORF computational model of a simple cell with application to contour detection

Contribution • CORF is a computational model of a simple cell. • The proposed CORF model shares more properties with a simple cell than the Gabor Function (GF) model. Some properties of a real simple cell CORF [1] GF [2] 1. Orientation selectivity 2. Cross orientation suppression 3. Contrast invariant orientation tuning 4. Response saturation • CORF is more effective than GF in contour detection, which is assumed to be the primary biological role of simple cells. Motivation

A CORF computational model of a simple cell with application to contour detection

Contribution • CORF is a computational model of a simple cell. • The proposed CORF model shares more properties with a simple cell than the Gabor Function (GF) model. Some properties of a real simple cell CORF [1] GF [2] 1. Orientation selectivity 2. Cross orientation suppression 3. Contrast invariant orientation tuning 4. Response saturation • CORF is more effective than GF in contour detection, which is assumed to be the primary biological role of simple cells. Motivation

Biologically realistic excitatory and inhibitory cell properties emerge from a sparse coding network

Neurons in the primary visual cortex exhibit a baffling array of tuning properties, often unaccountable by the classical linear feedforward model. Specifically, excitatory neurons display a number of nonlinear effects collectively known as non-classical receptive field (nCRF) effects [1], and inhibitory neurons have diverse orientation tuning properties [2]. Furthermore, excitatory cells outnumber inhibitory cells by a ratio of 9:1 [3], yet the excitatory and inhibitory drives are balanced. Efficient coding models of early vision have been shown to be able to explain key features of linear filtering properties [4] and some single cell nonlinear effects [5]. However, population statistics of nonlinear properties have not been studied in these models. In addition, inhibitory cells were not typically modeled. Here we demonstrate that many of the aforementioned excitatory cell and inhibitory cell properties emerge naturally from a network that implements sparse coding. To be specific, several nCRF effects including surround suppression, contrast invariant orientation tuning, and cross orientation suppression emerge in the excitatory cell population as a result of sparse coding strategy; the excitatory to inhibitory cell ratio could be understood largely as a result of the overcompletness of representation; moreover, a subpopulation of inhibitory interneurons exhibit orientation tuning due to sparse recurrent connections with the principal cells; another subpopulation displays untuned properties due to low rank connectivity patterns. We also demonstrate that the network exhibits balanced excitation and inhibition, as a result of the receptive field structure. We simulated a population of 2048 excitatory neurons with graded response described by the dynamics of locally competitive algorithm (LCA; [6]), which converges to the sparse coding representation at steady state. Inhibitory interneurons were described by linear units. The low rank and sparse recurrent connectivity pattern was a result of low rank plus sparse decomposition [7] of the LCA connectivity matrix. Non-classical receptive field effects were studied by presenting bar and drifting grating stimuli to the simulated network. Receptive fields of the inhibitory cells were mapped by sparse dots patterns.

Feedforward Origins of Response Variability Underlying Contrast Invariant Orientation Tuning in Cat Visual Cortex

Contrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p<10(-4), and Berkeley data set: p<10(-4)). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.

Nonlinearity of two-photon Ca2+imaging yields distorted measurements of tuning for V1 neuronal populations

We studied the relative accuracy of drifting gratings and noise stimuli for functionally characterizing neural populations using two-photon calcium imaging. Calcium imaging has the potential to distort measurements due to nonlinearity in the conversion from spikes to observed fluorescence. We demonstrate a dramatic impact of fluorescence saturation on functional measurements in ferret V1 by showing that responses to drifting gratings strongly violate contrast invariance of orientation tuning, a fundamental property of the spike rates. The observed relationship is consistent with saturation that clips the high-contrast tuning curve peaks by ∼40%. The nonlinearity was also apparent in mouse V1 responses to drifting gratings, but not as strong as in the ferret. Contrast invariance holds, however, for tuning curves measured with a randomized grating stimulus. This finding is consistent with prior work showing that the linear portion of a linear-nonlinear system can be recovered with reverse correlation. Furthermore, we demonstrate that a noise stimulus is more effective at keeping spike rates in the linear operating regime of a saturating nonlinearity, which both maximizes signal-to-noise ratios and simplifies the recovery of fast spike dynamics from slow calcium transients. Finally, we uncover spatiotemporal receptive fields by removing the nonlinearity and slow calcium transient from a model of fluorescence generation, which allowed us to observe dynamic sharpening of orientation tuning. We conclude that for two-photon recordings it is imperative that one considers the nonlinear distortion when designing stimuli and interpreting results, especially in sensory areas, species, or cell types with high firing rates.

Diverse mechanisms of contrast normalization in primary visual cortex

Contrast normalization models are extremely successful at describing the responses of neurons in visual cortex. They consist of two stages. A linear spatio-temporal filter (the receptive field) generates stimulus selectivity, such as orientation and direction preference. A non-selective, contrast-dependent, gain-control stage then normalizes the output of the linear filter. The models can quantitatively describe a variety of response nonlinearities, including contrast saturation, contrast invariance of orientation tuning, cross-orientation suppression, contrast-dependent changes in preferred temporal frequency, and modulation of neuronal responses by attention. In the standard model of contrast normalization, the normalization step is performed by a contrast-dependent shunting inhibition, which scales the size of the EPSPs generated at the linear stage. This shunt must be large, contrast-dependent, and only weakly selective for stimulus orientation and direction. Because we have rarely observed inhibition of this description in intracellular recordings from simple cells, we have investigated alternative mechanisms that might underlie contrast normalization. We applied the measured responses of LGN neurons to a feed-forward model of a simple cell that includes a variety of nonlinear properties of neurons and synapses that we measured directly. These include spike threshold, contrast saturation in the LGN responses, synaptic depression, contrast-dependent changes in trial-to-trial variability, and diversity in the timing of LGN responses. The model makes predictions of the synaptic input to simple cells, which we then compared to the membrane potential responses of simple cells recorded intracellularly in vivo. The model is highly constrained, with only two free parameters: the number of presynaptic LGN cells, and the aspect ratio of the simple cell subfield. And yet nearly all of observed aspects of contrast normalization emerge with considerable precision. We conclude that while the brain may find it advantageous to implement contrast normalization in the spike output of cortical simple cells, it uses a variety of underlying mechanisms to do so.

Effects of electrical coupling among layer 4 inhibitory interneurons on contrast-invariant orientation tuning

Simulations of orientation selectivity in visual cortex have shown that layer 4 complex cells lacking orientation tuning are ideal for providing global inhibition that scales with contrast in order to produce simple cells with contrast-invariant orientation tuning (Lauritzen and Miller in J Neurosci 23:10201-10213, 2003). Inhibitory cortical cells have been shown to be electrically coupled by gap junctions (Fukuda and Kosaka in J Neurosci 120:5-20, 2003). Such coupling promotes, among other effects, spike synchronization and coordination of postsynaptic IPSPs (Beierlein et al. in Nat Neurosci 3:904-910, 2000; Galarreta and Hestrin in Nat Rev Neurosci 2:425-433, 2001). Consequently, it was expected (Miller in Cereb Cortex 13:73-82, 2003) that electrical coupling would promote nonspecific functional responses consistent with the complex inhibitory cells seen in layer 4 which provide broad inhibition in response to stimuli of all orientations (Miller et al. in Curr Opin Neurobiol 11:488-497, 2001). This was tested using a mechanistic modeling approach. The orientation selectivity model of Lauritzen and Miller (J Neurosci 23:10201-10213, 2003) was reproduced with and without electrical coupling between complex inhibitory neurons. Although extensive coupling promotes uniform firing in complex cells, there were no detectable improvements in contrast-invariant orientation selectivity unless there were coincident changes in complex cell firing rates to offset the untuned excitatory component that grows with contrast. Thus, changes in firing rates alone (with or without coupling) could improve contrast-invariant orientation tuning of simple cells but not synchronization of complex inhibitory neurons alone.

Sparse coding models demonstrate some non-classical receptive field effects

Non-classical receptive field (nCRF) effects include several response properties in V1 neurons not explained by a linear-nonlinear (LN) receptive field model, but instead requiring significant interactions between V1 neurons. Using a sparse coding model [1,2] and bar and grating stimuli, simulated physiology experiments were carried out that replicated several nCRF phenomena reported previously in neurophysiology experiments. These include: end-stopping [3] (Fig. ​(Fig.1),1), contrast invariance of orientation tuning [4] (Fig. ​(Fig.2),2), radius, orientation, and contrast tunings of surround suppression [5,6] (Fig. ​(Fig.3,3, ​,4,4, ​,5).5). The results suggest that a sparse coding model can explain many of the nonlinear effects in V1 cells, and is therefore a reasonable candidate for a functional model of striate cortex. Figure 1 End-stopping. Comparison with a LN model. Figure 2 Contrast invariance of orientation tuning. Figure 3 Surround suppression at different contrasts. Figure 4 Orientation tuning of surround suppression. Figure 5 Surround orientation influences contrast tuning.

Orientation response properties of inhibitory cells in a model of cat primary visual cortex

• “Push-Pull” inhibition counteracts untuned DC from LGN • Achieves contrast invariant orientation tuning for cortical excitatory cells • Inhibitory cells follow tuning of LGN: sum of tuned F1 term and untuned DC term • Correlation-based connectivity derived from developmentally motivated learning rules for intracortical synaptic connections • Excitatory connections between cells with correlated receptive fields: same orientation and same spatial phase • Inhibitory connections between cells with anticorrelated receptive fields: same orienatation and opposite spatial phase • Synaptic depression model follows the model of Abbott et al, 1997. • First spike in a spike train elicits strongest postsynaptic potential • Synaptic strength weakens with each spike in rate dependent manner • Synapses return to full strength with time constant ~ 100 msec

Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation

During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RS(C)) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RS(C) neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses.

Sources of response variability underlying contrast-invariant orientation tuning in visual cortex

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