Orientation-selective Cells Research Articles

Anisotropy of object nonrigidity: High-level perceptual consequences of cortical anisotropy.

We demonstrate an unexpected anisotropy in perceived object non-rigidity, a little understood higher-level perceptual phenomenon, and explain this anisotropy by the population distribution of low-level neuronal properties in primary visual cortex. We measured the visual interpretation of two rigidly connected rotating circular rings. In videos where observers predominantly perceived rigidly connected horizontally rotating rings, they predominantly perceived a non-rigid configuration of independently wobbling rings if the video was rotated by 90°. Additionally, vertically rotating rings appeared narrower and longer compared to their physically identical horizontally rotating counterparts. We show that these perceived shape changes can be decoded from V1 outputs by incorporating documented anisotropies in orientation selectivity, i.e. greater numbers of cells and narrower tuning for the horizontal orientation than for the vertical. We then show that even when the shapes are matched, the increased non-rigidity persists in vertical rotations, suggesting a role for uneven distributions of direction-selective motion mechanisms. By incorporating cortical anisotropies into optic flow computations, we show that the kinematic gradients (Divergence, Curl, Deformation) for vertical rotations align more with gradients of derived velocity fields for physical non-rigidity, while those for horizontal rotations align closer to rigidity, indicating that cortical anisotropies contribute to the orientation dependence of the perception of non-rigidity. Our results reveal how high-level percepts can be directly shaped by low-level anisotropies. Cortical anisotropies have been claimed to promote efficient encoding of the statistical properties of natural images, but these surprising failures of shape constancy and object rigidity raise questions about their evolutionary significance.

Retinal origin of orientation but not direction selective maps in the superior colliculus

Neurons in the mouse superior colliculus ("colliculus") are arranged in ordered spatial maps. While orientation-selective (OS) neurons form a concentric map aligned to the center of vision, direction-selective (DS) neurons are arranged in patches with changing preferences across the visual field. It remains unclear whether these maps are a consequence of feedforward input from the retina or local computations in the colliculus. To determine whether these maps originate in the retina, we mapped the local and global distribution of OS and DS retinal ganglion cell axon boutons using invivo two-photon calcium imaging. We found that OS boutons formed patches that matched the distribution of OS neurons within the colliculus. DS boutons displayed fewer regional specializations, better reflecting the organization of DS neurons in the retina. Both eyes convey similar orientation but different DS inputs to the colliculus, as shown in recordings from retinal explants. These data demonstrate that orientation and direction maps within the colliculus are independent, where orientation maps are likely inherited from the retina, but direction maps require additional computations.

A model of amacrine cells for orientation detection

<abstract><p>As the most studied sensory system, the visual system plays an important role in our understanding of brain functions. Biological researchers have divided the nerve cells in the retina into dozens of visual channels carrying various characteristics based on visual features. Although orientation-selective cells have been identified in the retinas of various animals, the specific neural circuits of such cells have been controversial. In this study, a new simple and efficient orientation detection model based on the perceptron is proposed to restore the neural circuitry of orientation-selective cells in the retina. The performance of this model is experimentally compared with that of the convolutional neural network for image orientation recognition, and the results verify that the proposed model offers very good orientation detection. The proposed perceptron-based orientation detection model provides a new perspective to explain the neural circuits of orientation-selective cells.</p></abstract>

Orientation Invariant Sensorimotor Object Recognition Using Cortical Grid Cells.

Grid cells enable efficient modeling of locations and movement through path integration. Recent work suggests that the brain might use similar mechanisms to learn the structure of objects and environments through sensorimotor processing. This work is extended in our network to support sensor orientations relative to learned allocentric object representations. The proposed mechanism enables object representations to be learned through sensorimotor sequences, and inference of these learned object representations from novel sensorimotor sequences produced by rotated objects through path integration. The model proposes that orientation-selective cells are present in each column in the neocortex, and provides a biologically plausible implementation that echoes experimental measurements and fits in with theoretical predictions of previous studies.

Effects of Light Isoflurane Anesthesia on Organization of Direction and Orientation Selectivity in the Superficial Layer of the Mouse Superior Colliculus.

The superior colliculus (SC) is the midbrain center for integrating visual and multimodal sensory information. Neurons in the SC exhibit direction and orientation selectivity. Recent studies reported that neurons with similar preferences formed clusters in the mouse SC (Ahmadlou and Heimel, 2015; Feinberg and Meister, 2015; de Malmazet et al., 2018; Li et al., 2020). However, it remains controversial as to how these clusters are organized within the SC (Inayat et al., 2015; Chen et al., 2021). Here, we found that different brain states (i.e., awake or anesthetized with isoflurane) changed the selectivity of individual SC neurons and organizations of the neuronal population in both male and female mice. Using two-photon Ca2+ imaging, we examined both individual neuronal responses and the spatial patterns of their population responses. Under isoflurane anesthesia, orientation selectivity increased and a larger number of orientation-selective cells were observed when compared with the awake condition, whereas the proportions of direction-selective cells were similar in both conditions. Furthermore, direction- and orientation-selective cells located at closer positions showed more similar preferences, and cluster-like spatial patterns were enhanced. Inhibitory responses of direction-selective neurons were also reduced under isoflurane anesthesia. Thus, the changes in the spatial organization of response patterns were considered to be because of changes in the balance of excitation and inhibition, with excitation dominance, in the local circuits. These results provide new insights into the possibility that the functional organization of feature selectivity in the brain is affected by brain state.SIGNIFICANCE STATEMENT Recent large-scale recording studies are changing our view of visual maps in the superior colliculus (SC), including findings of cluster-like localizations of direction- and orientation-selective neurons. However, results from several laboratories are conflicting regarding the presence of cluster-like organization. Here, we demonstrated that light isoflurane anesthesia affected the direction- and orientation-tuning properties in the mouse superficial SC and that their cluster-like localization pattern was enhanced by the anesthesia. Furthermore, the effect of anesthesia on direction selectivity appeared to be different in the excitatory and inhibitory populations in the SC. Our results suggest that the functional organization of direction and orientation selectivity might be regulated by the excitation-inhibition balance that depends on the brain state.

Pattern Motion Processing by MT Neurons

Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The prevailing theory suggests that pattern selectivity results from the summation of the outputs of component cells as part of a hierarchical visual pathway. We present a computational model of the visual pathway from primary visual cortex (V1) to MT that suggests an alternate model where the progression from component to pattern selectivity is not required. Using standard orientation-selective V1 cells, end-stopped V1 cells, and V1 cells with extra-classical receptive fields (RFs) as inputs to MT, the model shows that the degree of pattern or component selectivity in MT could arise from the relative strengths of the three V1 input types. Dominance of end-stopped V1 neurons in the model leads to pattern selectivity in MT, while dominance of V1 cells with extra-classical RFs result in component selectivity. This model may assist in designing experiments to further understand motion processing mechanisms in primate MT.

Stable memory and computation in randomly rewiring neural networks.

Our brains must maintain a representation of the world over a period of time much longer than the typical lifetime of the biological components producing that representation. For example, recent research suggests that dendritic spines in the adult mouse hippocampus are transient with an average lifetime of ~10 days. If this is true, and if turnover is equally likely for all spines, ~95% of excitatory synapses onto a particular neuron will turn over within 30 days; however, a neuron's receptive field can be relatively stable over this period. Here, we use computational modeling to ask how memories can persist in neural circuits such as the hippocampus and visual cortex in the face of synapse turnover. We demonstrate that Hebbian plasticity during replay of presynaptic activity patterns can integrate newly formed synapses into pre-existing memories. Furthermore, we find that Hebbian plasticity during replay is sufficient to stabilize the receptive fields of hippocampal place cells in a model of the grid-cell-to-place-cell transformation in CA1 and of orientation-selective cells in a model of the center-surround-to-simple-cell transformation in V1. Together, these data suggest that a simple plasticity rule, correlative Hebbian plasticity of synaptic strengths, is sufficient to preserve neural representations in the face of synapse turnover, even in the absence of activity-dependent structural plasticity. NEW & NOTEWORTHY Recent research suggests that synapses turn over rapidly in some brain structures; however, memories seem to persist for much longer. We show that Hebbian plasticity of synaptic strengths during reactivation events can preserve memory in computational models of hippocampal and cortical networks despite turnover of all synapses. Our results suggest that memory can be stored in the correlation structure of a network undergoing rapid synaptic remodeling.

Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing.

We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e.g., Monte Carlo or particle optimizations without gradients. We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina (Wohrer and Kornprobst, 2009), with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator. For instance it can also predict the response of V1 orientation selective cells. Convis is open source under the GPL-3.0 license and available from https://github.com/jahuth/convis/ with documentation at https://jahuth.github.io/convis/.

Coding of spatial attention priorities and object features in the macaque lateral intraparietal cortex.

Primate posterior parietal cortex (PPC) is known to be involved in controlling spatial attention. Neurons in one part of the PPC, the lateral intraparietal area (LIP), show enhanced responses to objects at attended locations. Although many are selective for object features, such as the orientation of a visual stimulus, it is not clear how LIP circuits integrate feature‐selective information when providing attentional feedback about behaviorally relevant locations to the visual cortex. We studied the relationship between object feature and spatial attention properties of LIP cells in two macaques by measuring the cells' orientation selectivity and the degree of attentional enhancement while performing a delayed match‐to‐sample task. Monkeys had to match both the location and orientation of two visual gratings presented separately in time. We found a wide range in orientation selectivity and degree of attentional enhancement among LIP neurons. However, cells with significant attentional enhancement had much less orientation selectivity in their response than cells which showed no significant modulation by attention. Additionally, orientation‐selective cells showed working memory activity for their preferred orientation, whereas cells showing attentional enhancement also synchronized with local neuronal activity. These results are consistent with models of selective attention incorporating two stages, where an initial feature‐selective process guides a second stage of focal spatial attention. We suggest that LIP contributes to both stages, where the first stage involves orientation‐selective LIP cells that support working memory of the relevant feature, and the second stage involves attention‐enhanced LIP cells that synchronize to provide feedback on spatial priorities.

Visual Receptive Field Properties of Neurons in the Mouse Lateral Geniculate Nucleus.

The lateral geniculate nucleus (LGN) is increasingly regarded as a “smart-gating” operator for processing visual information. Therefore, characterizing the response properties of LGN neurons will enable us to better understand how neurons encode and transfer visual signals. Efforts have been devoted to study its anatomical and functional features, and recent advances have highlighted the existence in rodents of complex features such as direction/orientation selectivity. However, unlike well-researched higher-order mammals such as primates, the full array of response characteristics vis-à-vis its morphological features have remained relatively unexplored in the mouse LGN. To address the issue, we recorded from mouse LGN neurons using multisite-electrode-arrays (MEAs) and analysed their discharge patterns in relation to their location under a series of visual stimulation paradigms. Several response properties paralleled results from earlier studies in the field and these include centre-surround organization, size of receptive field, spontaneous firing rate and linearity of spatial summation. However, our results also revealed “high-pass” and “low-pass” features in the temporal frequency tuning of some cells, and greater average contrast gain than reported by earlier studies. In addition, a small proportion of cells had direction/orientation selectivity. Both “high-pass” and “low-pass” cells, as well as direction and orientation selective cells, were found only in small numbers, supporting the notion that these properties emerge in the cortex. ON- and OFF-cells showed distinct contrast sensitivity and temporal frequency tuning properties, suggesting parallel projections from the retina. Incorporating a novel histological technique, we created a 3-D LGN volume model explicitly capturing the morphological features of mouse LGN and localising individual cells into anterior/middle/posterior LGN. Based on this categorization, we show that the ON/OFF, DS/OS and linear response properties are not regionally restricted. Our study confirms earlier findings of spatial pattern selectivity in the LGN, and builds on it to demonstrate that relatively elaborate features are computed early in the visual pathway.

The Migraine Aura: A Problem for Vision Theory?

The scintillating zigzag pattern that a migraine patient may see as an illusion before the onset of headache offers a unique investigative approach to visual mechanisms. The likeliest interpretation of these zigzags is that they are the spontaneous discharges of the orientation-selective neurons first described in the striate cortex by Hubel and Wiesel (Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol (Lond). 1962 Jan;160:106-54; and Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. J Physiol (London). 1968 Mar;195(1):215-43). Although these cells appear to lie in rows in V1, as Hubel and Wiesel found, very few angles in the visual field are represented; this, and the coarseness of the representation, makes it unlikely that the cells act as feature detectors. The orientation-selective cells could, however, monitor the amount of light falling on the retina and thereby enable color constancy to be achieved. The cells may also serve as coarse movement detectors. The new model of cell organization in human V1 enables us to determine the approximate sizes of the receptive fields of the orientation-selective cells.

The broad orientation dependence of the motion streak aftereffect reveals interactions between form and motion neurons.

The extended integration time of visual neurons can lead to the production of the neural equivalent of an orientation cue along the axis of motion in response to fast-moving objects. The dominant model argues that these motion streaks resolve the inherent directional uncertainty arising from the small size of receptive fields in V1, by combining spatial orientation with motion signals in V1. This model was tested in humans using visual aftereffects, in which adapting to a static grating causes the perceived direction of a subsequently presented motion stimulus to be tilted away from the adapting orientation. We found that a much broader range of orientations produced aftereffects than predicted by the current model, suggesting that these orientation cues influence motion perception at a later stage than V1. We also found that varying the spatial frequency of the adaptor changed the aftereffect from repulsive to attractive for motion-test but not form-test stimuli. Finally, manipulations of V1 excitability, using transcranial stimulation, reduced the aftereffect, suggesting that the orientation cue is dependent on V1. These results can be accounted for if the orientation information from the motion streak, gathered in V1, enters the motion system at a later stage of motion processing, most likely V5. A computational model of motion direction is presented incorporating gain modifications of broadly tuned motion-selective neurons by narrowly tuned orientation-selective cells in V1, which successfully accounts for the extant data. These results reinforce the suggestion that orientation places strong constraints on motion processing but in a previously undescribed manner.

The orientation dependence of the motion streak aftereffect reveals interactions between form and motion neurons

The extended integration time of visual neurons leads to fast-moving objects producing the neural equivalent of an orientation cue along the axis of motion. The dominant model [Geisler, W.S. (1999). Motion streaks provide a spatial code for motion direction. Nature, 400(6739), 65–69] proposes that these ‘motion streaks’ resolve the inherent directional uncertainty arising from the small size of receptive fields in V1, by combining spatial orientation with motion signals in V1. This model was tested using visual aftereffects, where adapting to a static grating causes the perceived direction of a subsequently-presented fast motion stimulus to be repelled away from the adapting orientation. Using a similar adaptation method, we measured the angular dependence of this effect and found in each human observer that a much broader range of adapting orientations (mean of 38.82º instead of 21.72°) produced aftereffects than predicted by the current model of motion streaks. This suggests that motion streaks influence motion perception at a later stage than V1. We also found that varying the spatial frequency of the adaptor by approximately two octaves changed the aftereffect from repulsive to attractive for motion, but not form stimuli. Finally, manipulations of V1 excitability, using transcranial direct current stimulation, reduced the aftereffect, suggesting that the orientation cue is dependent upon V1. These results can be accounted for if the orientation information from the motion streak, gathered in V1, enters the motion system at a later stage of motion processing, most likely V5. A new computational model of motion direction is presented incorporating gain modifications of broadly-tuned motion-selective neurons, most likely in V5, by narrowly-tuned orientation-selective cells in V1, which successfully accounts for the data in the current study. These results stress that orientation places strong constraints on motion processing in a different way than the current models predict. Meeting abstract presented at VSS 2015

Synaptic Basis for Differential Orientation Selectivity between Complex and Simple Cells in Mouse Visual Cortex.

Simple and complex cells, two classes of principal neurons in the primary visual cortex (V1), are generally thought to be equally selective for orientation. In mouse V1, we report that complex cells, identified by their overlapping on/off subfields, has significantly weaker orientation selectivity (OS) than simple cells. This can be primarily attributed to the differential tuning selectivity of inhibitory synaptic input: inhibition in complex cells is more narrowly tuned than excitation, whereas in simple cells inhibition is more broadly tuned than excitation. In addition, there is a good correlation between inhibitory tuning selectivity and the spatial organization of inhibitory inputs. These complex and simple cells with differential degree of OS may provide functionally distinct signals to different downstream targets.

Which way is down? Positional distortion in the tilt illusion.

Contextual information can have a huge impact on our sensory experience. The tilt illusion is a classic example of contextual influence exerted by an oriented surround on a target's perceived orientation. Traditionally, the tilt illusion has been described as the outcome of inhibition between cortical neurons with adjacent receptive fields and a similar preference for orientation. An alternative explanation is that tilted contexts could produce a re-calibration of the subjective frame of reference. Although the distinction is subtle, only the latter model makes clear predictions for unoriented stimuli. In the present study, we tested one such prediction by asking four naive subjects to estimate three positions (4, 6, and 8 o'clock) on an imaginary clock face within a tilted surround. To indicate their estimates, they used either an unoriented dot or a line segment, with one endpoint at fixation in the middle of the surround. The surround's tilt was randomly chosen from a set of orientations (±75°, ±65°, ±55°, ±45°, ±35°, ±25°, ±15°, ±5° with respect to vertical) across trials. Our results showed systematic biases consistent with the tilt illusion in both conditions. Biases were largest when observers attempted to estimate the 4 and 8 o'clock positions, but there was no significant difference between data gathered with the dot and data gathered with the line segment. A control experiment confirmed that biases were better accounted for by a local coordinate shift than to torsional eye movements induced by the tilted context. This finding supports the idea that tilted contexts distort perceived positions as well as perceived orientations and cannot be readily explained by lateral interactions between orientation selective cells in V1.

Masking reveals parallel form systems in the visual brain.

It is generally supposed that there is a single, hierarchically organized pathway dedicated to form processing, in which complex forms are elaborated from simpler ones, beginning with the orientation-selective cells of V1. In this psychophysical study, we undertook to test another hypothesis, namely that the brain’s visual form system consists of multiple parallel systems and that complex forms are other than the sum of their parts. Inspired by imaging experiments which show that forms of increasing perceptual complexity (lines, angles, and rhombuses) constituted from the same elements (lines) activate the same visual areas (V1, V2, and V3) with the same intensity and latency (Shigihara and Zeki, 2013, 2014), we used backward masking to test the supposition that these forms are processed in parallel. We presented subjects with lines, angles, and rhombuses as different target-mask pairs. Evidence in favor of our supposition would be if masking is the most effective when target and mask are processed by the same system and least effective when they are processed in different systems. Our results showed that rhombuses were strongly masked by rhombuses but only weakly masked by lines or angles, but angles and lines were well masked by each other. The relative resistance of rhombuses to masking by low-level forms like lines and angles suggests that complex forms like rhombuses may be processed in a separate parallel system, whereas lines and angles are processed in the same one.

Development and matching of binocular orientation preference in mouse V1.

Eye-specific thalamic inputs converge in the primary visual cortex (V1) and form the basis of binocular vision. For normal binocular perceptions, such as depth and stereopsis, binocularly matched orientation preference between the two eyes is required. A critical period of binocular matching of orientation preference in mice during normal development is reported in literature. Using a reaction diffusion model we present the development of RF and orientation selectivity in mouse V1 and investigate the binocular orientation preference matching during the critical period. At the onset of the critical period the preferred orientations of the modeled cells are mostly mismatched in the two eyes and the mismatch decreases and reaches levels reported in juvenile mouse by the end of the critical period. At the end of critical period 39% of cells in binocular zone in our model cortex is orientation selective. In literature around 40% cortical cells are reported as orientation selective in mouse V1. The starting and the closing time for critical period determine the orientation preference alignment between the two eyes and orientation tuning in cortical cells. The absence of near neighbor interaction among cortical cells during the development of thalamo-cortical wiring causes a salt and pepper organization in the orientation preference map in mice. It also results in much lower % of orientation selective cells in mice as compared to ferrets and cats having organized orientation maps with pinwheels.

Predicting the effects of illumination in shape from shading

Shading depends on different interactions between surface geometry and lighting. Under collimated illumination, shading is dominated by the 'direct' term, in which image intensities vary with the angle between surface normals and light sources. Diffuse illumination, by contrast, is dominated by 'vignetting effects' in which image intensities vary with the degree of self-occlusion (the proportion of incoming direction that each surface point 'sees'). These two types of shading thus lead to very different intensity patterns, which raises the question of whether shading inferences are based directly on image intensities. We show here that the visual system uses 2D orientation signals ('orientation fields') to estimate shape, rather than raw image intensities and an estimate of the illuminant. We rendered objects under varying illumination directions designed to maximize the effects of illumination on the image. We then passed these images through monotonic, non-linear intensity transfer functions to decouple luminance information from orientation information, thereby placing the two signals in conflict. In Task 1 subjects adjusted the 3D shape of match objects to report the illusory effects of changes of illumination direction on perceived shape. In Task 2 subjects reported which of a pair of points on the surface appeared nearer in depth. They also reported perceived illumination directions for all stimuli. We find that the substantial misperceptions of shape are well predicted by orientation fields, and poorly predicted by luminance-based shape from shading. For the untransformed images illumination could be estimated accurately, but not for the transformed images. Thus shape perception was, for these examples, independent of the ability to estimate the lighting. Together these findings support neurophysiological estimates of shape from the responses of orientation selective cell populations, irrespective of the illumination conditions.

A spiking model of superior colliculus for bottom-up saliency

Active exploration of visual space is controlled by a saccadic system using a combination of bottom-up and top-down signals. Rate based models [1] and simple V1 models (e.g. [2]), have been proposed to explain bottom-up or attentional mechanisms. We present a biologically detailed spiking model of superior colliculus to generate saccadic eye movements using a competitive mechanism based on spike arrival times. Various pop-out tasks based on color, orientation features and motion is used to analyze the reaction time of the model. The superior colliculus is modeled as a two layer network with feature-specific superficial layers driving a common deep layer. The superficial layers of the superior colliculus are topologically organized and is driven by retinal parasols cells, V1 layer 2/3 orientation-selective cells, V1 layer 2/3 color-selective cells, and MT cells. These enable luminance, orientation, color and motion based popout respectively. In this model, the earliest spikes are considered the most salient. A saccade is triggered when there is a consistent winner in the superficial layers at the same spatial region over an extended period of time, 40 ms to 100 ms. The superficial layers implement a temporal winner-take-all mechanism; specifically, there exists a separate inhibitory subnetwork that allows each superficial layer to choose a winner. The deep layer integrates these winners over time and selects a single spatial region. It implements a bump attractor network mediated by short-range excitation (AMPA and NMDA) and long range inhibition, which generates a bump of activity before and during a saccade. An interesting property of bump attractor network based circuit is that a single cell response is independent of size of the stimulus. Similar spiking response was also observed in most of the visually selective superior colliculus cells in monkeys [3]. Additionally, this model captures known behavioral and neurophysiological properties of the superior colliculus such as saccadic suppression, activity buildup before saccade generation, and GABAb mediated inhibition from the deep layer to superficial layers resulting in selective inhibition in recently attended region. All the connections in the SC model are pre-wired to satisfy the response characteristics of superficial and deep layers. The reaction time of the model was tested on simple and conjunctive pop-out tasks. We observe only a mild shift in reaction time based on the number of distractors. Higher reaction time was observed as the complexity of the search task was increased. Also, reaction time increases based on the level of visual hierarchy that is connected to the superior colliculus layers.

Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression

Theoretical considerations have led to the concept that the cerebral cortex is operating in a balanced state in which synaptic excitation is approximately balanced by synaptic inhibition from the local cortical circuit. This paper is about the functional consequences of the balanced state in sensory cortex. One consequence is gain control: there is experimental evidence and theoretical support for the idea that local circuit inhibition acts as a local automatic gain control throughout the cortex. Second, inhibition increases cortical feature selectivity: many studies of different sensory cortical areas have reported that suppressive mechanisms contribute to feature selectivity. Synaptic inhibition from the local microcircuit should be untuned (or broadly tuned) for stimulus features because of the microarchitecture of the cortical microcircuit. Untuned inhibition probably is the source of Untuned Suppression that enhances feature selectivity. We studied inhibition’s function in our experiments, guided by a neuronal network model, on orientation selectivity in the primary visual cortex, V1, of the Macaque monkey. Our results revealed that Untuned Suppression, generated by local circuit inhibition, is crucial for the generation of highly orientation-selective cells in V1 cortex.