Receptive Field Profiles Research Articles

Acidification decouples gap junctions but enlarges the receptive field size of horizontal cells in carp retina

The receptive field size of retinal horizontal cells is much larger than their dendritic field size due to gap junctional coupling between the same sub-types of cell. Thus, horizontal cells form syncytia by electrical coupling. The basic receptive field profile of horizontal cells can be described by an exponential function based on measurement of responses to a slit of light moved tangentially from a recording electrode. The space constant of this exponential function is proportional to ( g s/ g m) 1/2, where g s and g m represent gap junctional conductance and non-gap junctional conductance, respectively. Acidifying the superfusing solution by lowering the pH from 7.60 to 7.30 decreased the dye-coupling, hyperpolarised the resting membrane potential and reduced the photoresponses of H1 type horizontal cells. Surprisingly, however, the receptive field size expanded significantly. Raising the pH from 7.30 to 7.60 or 7.90 produced opposite effects. These results were consistent with alkaline extracellular pH producing a greater increase in g m than in g s and enhancing release of transmitter from cones acting upon horizontal cells.

What simple and complex cells compute

Visual neuroscientists seek to answer two related questions. First, what does the visual system do? Second, how does it do it? While an answer to the second question is a description based on anatomy and biophysics, an answer to the first question is a description of computations performed on images. Important steps in providing this description were made with the publication of two classical studies on primary visual cortex by Movshon et al. (1978a,b). These studies have not aged in their readability, and do not require an interpretive key to be enjoyed by the contemporary reader. As an introduction, however, this Perspectives article briefly reviews the state of the art at the time of their appearance, their main findings, and their influence on three decades of subsequent investigations.

Dynamic shifts of visual receptive fields in cortical area MT by spatial attention

Voluntary attention is the top-down selection process that focuses cortical processing resources on the most relevant sensory information. Spatial attention--that is, selection based on stimulus position--alters neuronal responsiveness throughout primate visual cortex. It has been hypothesized that it also changes receptive field profiles by shifting their centers toward attended locations and by shrinking them around attended stimuli. Here we examined, at high resolution, receptive fields in cortical area MT of rhesus macaque monkeys when their attention was directed to different locations within and outside these receptive fields. We found a shift of receptive fields, even far from the current location of attention, accompanied by a small amount of shrinkage. Thus, already in early extrastriate cortex, receptive fields are not static entities but are highly modifiable, enabling the dynamic allocation of processing resources to attended locations and supporting enhanced perception within the focus of attention by effectively increasing the local cortical magnification.

Hierarchical feed-forward network for object detection tasks

Recent research on neocognitron-like neural feed-forward architectures, which have formerly been successfully applied to the recognition of artificial stimuli such as paperclip objects, now also opens up application to more natural stimuli. Such networks exhibit high-recognition performance with respect to translation, rotation, scaling and cluttered surroundings. In this contribution, we introduce a new type of hierarchical model, which is trained using a non-negative matrix factorization algorithm. In contrast to previous work, our approach cannot only classify objects but is also capable of rapid object detection in natural scenes. Thus, the time-consuming and conceptually unsatisfying split-up into a localization stage (e.g., using segmentation) and a subsequent classification can be avoided. The network consists of alternating layers of simple and complex cell planes and incorporates nonlinear processing schemes that have been proposed in recent literature. Learning of receptive field profiles for the lower layers of the network takes place by unsupervised learning whereas a final classification layer is trained supervised. This final layer is then utilized for detection. We test the classification performance of the network on images of natural objects which are systematically distorted. To test the ability to detect objects, cluttered natural background is used.

Spatiotemporal Specificity of Neuronal Activity Directs the Modification of Receptive Fields in the Developing Retinotectal System

The precise temporal relation between pre- and postsynaptic activity modulates the strength of synaptic connections. Despite its extensive characterization in vivo and in vitro, the degree to which spike timing-dependent plasticity can shape receptive field properties is unclear. We use in vivo patch-clamp recordings of tectal neurons in developing Xenopus tadpoles to control the precise timing of action potentials with respect to the arrival of a subset of visual inputs evoked by local light stimulation on the retina. The pattern of visual inputs onto a tectal neuron was tracked over time by rapid reverse correlation mapping of receptive fields. Spike timing-dependent potentiation or depression of a subset of synapses reliably shifts the spatial receptive fields toward or away from the trained subregion of visual space, respectively. These results demonstrate that natural patterns of activity evoked by sensory stimuli play an instructive role in the developing nervous system.

Receptive Field Properties of Neurons in the Early Visual Cortex Revealed by Local Spectral Reverse Correlation

We introduce a novel class of white-noise analyses, named local spectral reverse correlation (LSRC), which is capable of revealing various aspects of visual receptive field profiles that were undetectable previously in a single simple measurement. The method is based on spectral analyses in a two-dimensional spatial frequency domain for spatially localized areas within and around their receptive fields. Extracellular single-unit recordings were performed for area 17 and 18 neurons in anesthetized cats. A dynamic dense noise pattern was presented in which the pattern covered an area two to three times larger than the classical receptive field. Spike trains were then cross-correlated with frequency spectra of localized noise pattern to obtain spatially localized selectivity maps in the two-dimensional frequency domain. Our findings are as follows. (1) The new LSRC method allows measurements of two-dimensional frequency tunings and their spatial extent even for cells with substantial nonlinearity. (2) A small subset of neurons shows spatial inhomogeneity in the two-dimensional frequency tunings. (3) In addition to facilitatory response profiles, we can also visualize suppressive profiles localized both in space and spatial frequency domains. Our results suggest that the new analysis technique can be a powerful tool for measuring visual response profiles that contain inhomogeneity in space, as well as for studying neurons with substantial nonlinearities. These features make the method particularly suitable for studying response profiles of neurons in early as well as intermediate extrastriate visual areas.

Dynamics of directional selectivity in MT receptive field centre and surround

We studied receptive field organization of motion-sensitive neurons in macaque middle temporal cortical area (MT), by mapping direction selectivity in space and in time. Stimuli consisted of pseudorandom sequences of single motion steps presented simultaneously at many different receptive field locations. Spatio-temporal receptive field profiles were constructed by cross-correlating stimuli and spikes. The resulting spike-triggered averages revealed centre-surround organization. The temporal dynamics of the receptive fields were generally biphasic with increased probability for the preferred direction at short latency (50-70 ms) and decreased probability at longer latency (80-100 ms). The response latency of the receptive field surround was on average 16 ms longer than that of the centre. Our results show that surround input and biphasic behaviour reflect two different mechanisms, which make MT cells specifically sensitive to motion contrast in space and time.

Selectivity and sparseness in the responses of striate complex cells

Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by either kurtosis or entropy. We call this non-parametric selectivity, in contrast to parametric selectivity, which measures tuning curve bandwidths. To examine how receptive field properties affected non-parametric selectivity, two models of complex cells were created. One was a standard Gabor energy model, and the other a slight variant constructed from a Gabor function and its Hilbert transform. Functionally, these models differed primarily in the size of their DC responses. The Hilbert model produced higher selectivities than the Gabor model, with the two models bracketing the data from above and below. Thus we see that tiny changes in the receptive field profiles can lead to major changes in selectivity. While selectivity looks at the response distribution of a single neuron across a set of stimuli, sparseness looks at the response distribution of a population of neurons to a single stimulus. In the model, we found that on average the sparseness of a population was equal to the selectivity of cells comprising that population, a property we call ergodicity. We raise the possibility that high sparseness is the result of distortions in the shape of response distributions caused by non-linear, information-losing transforms, unrelated to information theoretic issues of efficient coding.

A preference for phase-based disparity in a neuromorphic implementation of the binocular energy model.

The relative depth of objects causes small shifts in the left and right retinal positions of these objects, called binocular disparity. This letter describes an electronic implementation of a single binocularly tuned complex cell based on the binocular energy model, which has been proposed to model disparity-tuned complex cells in the mammalian primary visual cortex. Our system consists of two silicon retinas representing the left and right eyes, two silicon chips containing retinotopic arrays of spiking neurons with monocular Gabor-type spatial receptive fields, and logic circuits that combine the spike outputs to compute a disparity-selective complex cell response. The tuned disparity can be adjusted electronically by introducing either position or phase shifts between the monocular receptive field profiles. Mismatch between the monocular receptive field profiles caused by transistor mismatch can degrade the relative responses of neurons tuned to different disparities. In our system, the relative responses between neurons tuned by phase encoding are better matched than neurons tuned by position encoding. Our numerical sensitivity analysis indicates that the relative responses of phase-encoded neurons that are least sensitive to the receptive field parameters vary the most in our system. We conjecture that this robustness may be one reason for the existence of phase-encoded disparity-tuned neurons in biological neural systems.

Color constancy and the functional significance of McCollough effects

A central problem in visual perception concerns how humans perceive stable and uniform object colors despite variable lighting conditions (i.e. color constancy). One solution is to ‘discount’ variations in lighting across object surfaces by encoding color contrasts, and utilize this information to ‘fill in’ properties of the entire object surface. Implicit in this solution is the caveat that the color contrasts defining object boundaries must be distinguished from the spurious color fringes that occur naturally along luminance-defined edges in the retinal image (i.e. optical chromatic aberration). In the present paper, we propose that the neural machinery underlying color constancy is complemented by an ‘error-correction’ procedure which compensates for chromatic aberration, and suggest that error-correction may be linked functionally to the experimentally induced illusory colored aftereffects known as McCollough effects (MEs). To test these proposals, we develop a neural network model which incorporates many of the receptive-field (RF) profiles of neurons in primate color vision. The model is composed of two parallel processing streams which encode complementary sets of stimulus features: one stream encodes color contrasts to facilitate filling-in and color constancy; the other stream selectively encodes (spurious) color fringes at luminance boundaries, and learns to inhibit the filling-in of these colors within the first stream. Computer simulations of the model illustrate how complementary color-spatial interactions between error-correction and filling-in operations (a) facilitate color constancy, (b) reveal functional links between color constancy and the ME, and (c) reconcile previously reported anomalies in the local (edge) and global (spreading) properties of the ME. We discuss the broader implications of these findings by considering the complementary functional roles performed by RFs mediating color-spatial interactions in the primate visual system.

Spatio–temporal plasticity of cortical receptive fields in response to repetitive visual stimulation in the adult cat

Many psychophysical experiments on perceptual learning in humans show increases of performance that are most probably based on functions of early visual cortical areas. Long-term plasticity of the primary visual cortex has so far been shown in vivo with the use of visual stimuli paired with electrical or pharmacological stimulation at the cellular level. Here, we report that plasticity in the adult visual cortex can be achieved by repetitive visual stimulation. First, spatial receptive field profiles of single units ( n=38) in area 17 or 18 of the anesthetized cat were determined with optimally oriented flashing light bars. Then a conditioning protocol was applied to induce associative synaptic plasticity. The receptive field center and an unresponsive region just outside the excitatory receptive field were synchronously stimulated (‘costimulation’, repetition rate 1 Hz; for 10–75 min). After costimulation the receptive field and its adjacent regions were mapped again. We observed specific increases of the receptive field size, changes of the receptive field subfield structure as well as shifts in response latency. In 37% of the cells the receptive field size increased specifically towards the stimulated side but not towards the non-stimulated opposite side of the receptive field. In addition, changes in the relative strength and size of the on and off subfield regions were observed. These specific alterations were dependent on the level of neuronal activity during costimulation. During recovery, the new responses dropped down to 120% of the preconditioning value on average within 103 min; however, the decay times significantly depended on the response magnitude after costimulation. In the temporal domain, the latency of new responses appeared to be strongly influenced by the latency of the response during costimulation. Twenty-nine percent of the units displayed no receptive field enlargement, most likely because the activity during costimulation was significantly lower than in the cases with enlarged receptive fields. An unspecific receptive field enlargement towards both the stimulated and non-stimulated side was observed in 34% of the tested cells. In contrast to the cells with specifically enlarged receptive fields, the unspecific increase of receptive field size was always accompanied by a strong increase of the general activity level. We conclude that the receptive field changes presumably took place by strengthening of synaptic inputs at the recorded cells in a Hebbian way as previously shown in the visual cortex in vitro and in vivo. The observed receptive field changes may be related to preattentive perceptual learning and could represent a basis of the ‘filling in’ of cortical scotomas obtained with specific training procedures in human patients suffering from visual cortex lesions.

A computational model for the development of simple-cell receptive fields spanning the regimes before and after eye-opening

Motivated by the possible existence of post-natal cortical plasticity, we analyze Miller's (J. Neurosci. 14 (1994) 409–441) correlation-based plasticity dynamics using sinusoidal patterns of varying frequency and orientation. After demonstrating that this leads to the formation of a cluttered receptive field (RF), and analyzing the reasons therefor, we propose a Kohonen-type, response-dependent modulation of Miller's dynamics. We analyze the simulation outputs—the RF profiles and preference maps—arising from changes in the model parameters. Further, in an attempt to quantify the hypothesis that (i) spontaneous activity and (ii) visual experience play prominent roles in the (a) establishment and (b) maturity of orientation selectivity, respectively, we initialize the plasticity dynamics with developing Miller-type RFs. We interpret such an initialization to form a combined pre-natal– post-natal model, and quantify the relative effects of spontaneous activity and visual experience on developing RFs and their preference organization. As a next step, we analyze a possible quantification of the critical period phenomenon in the proposed model, and discuss the biological implications of such a quantification. Further, we subject the model to selective rearing by presenting it with biased visual environments. By analyzing the results, and calibrating the output using its appropriate biological counterparts, we show that the model measures up to biological realities. We also fix bounds for certain model parameters by comparing the results with biological data.

Range and Mechanism of Encoding of Horizontal Disparity in Macaque V1

The responses of single cortical neurons were measured as a function of the binocular disparity of dynamic random dot stereograms for a large sample of neurons (n = 787) from V1 of the awake macaque. From this sample, we selected 180 neurons whose tuning curves were strongly tuned for disparity, well sampled and well described by one-dimensional Gabor functions. The fitted parameters of the Gabor functions were used to resolve three outstanding issues in binocular stereopsis. First, we considered whether tuning curves can be meaningfully divided into discrete tuning types. Careful examination of the distributions of the Gabor parameters that determine tuning shape revealed no evidence for clustering. We conclude that a continuum of tuning types is present. Second, we investigated the mechanism of disparity encoding for V1 neurons. The shape of the disparity tuning function can be used to distinguish between position-encoding (in which disparity is encoded by an interocular shift in receptive field position) and phase-encoding (in which disparity is encoded by a difference in the receptive field profile in the 2 eyes). Both position and phase encoding were found to be common. This was confirmed by an independent assessment of disparity encoding based on the measurement of disparity sensitivity for sinusoidal luminance gratings of different spatial frequencies. The contributions of phase and position to disparity encoding were compared by estimating a population average of the rate of change in firing rate per degree of disparity. When this was calculated separately for the phase and position contributions, they were found to be closely similar. Third, we investigated the range of disparity tuning in V1 as a function of eccentricity in the parafoveal range. We find few cells which are selective for disparities greater than +/-1 degrees even at the largest eccentricity of approximately 5 degrees. The preferred disparity was correlated with the spatial scale of the tuning curve, and for most units lay within a +/-pi radians phase limit. Such a size-disparity correlation is potentially useful for the solution of the correspondence problem.

Second-order processes in vision: introduction.

led to a radical reconceptualization ofthe nature of visual processing within the framework oflinear systems theory. Pattern adaptation and thresholdpattern-discrimination experiments provided dramaticevidence that human vision filters the retinal input into anumber of channels selectively tuned for spatiotemporalfrequency and orientation. These channels were concep-tualized as approximately linear, shift-invariant transfor-mations of the time-varying retinal input. It was hy-pothesized that these filters were realized byretinotopically organized arrays of neurons. The neu-rons in a given such array were all assumed to have thesame receptive field profile (typically modeled as a Gaborfunction of some specific spatial frequency and orienta-tion), and to monitor different locations in the stimulusfield: one neuron for each location. Thus the responsesof these neurons in a given channel array composed a‘‘neural image’’

Computational model of dot-pattern selective cells.

A computational model of a dot-pattern selective neuron is proposed. This type of neuron is found in the inferotemporal cortex of monkeys. It responds strongly to groups of dots and spots of light intensity variation but very weakly or not at all to single dots and spots that are not part of a pattern. This non-linear behaviour is quite different from the spatial frequency filtering behaviour exhibited by other neurons that react to spot-shaped stimuli, such as neurons with centre-surround receptive field profiles found in the lateral geniculate nuclei and layer 4Cbeta of V1. It is implemented in the proposed computational model by using an AND-type non-linearity to combine the responses of centre-surround cells. The proposed model is capable of explaining the results of neurophysiological experiments as well as certain psychophysical observations.

Are there separate first-order and second-order mechanisms for orientation discrimination?

In a series of experiments we compared orientation discrimination performance for Gabor stimuli in which the stimulus profile was either matched to the receptive field profile of single V1 simple cells (‘simple’), or in which the carrier and envelope orientations were different (‘tigertails’). In the first Experiment, using small, high spatial frequency, peripheral stimuli to minimise the number of detectors involved, we found that simple stimuli were more detectable than tigertails of the same contrast energy, and that orientation discrimination thresholds for simple stimuli were lower than for tigertails of equal detectability. In later experiments with larger stimuli we measured thresholds for detecting tilts of the envelope with the carrier fixed in orientation. Envelope thresholds were similar for different carrier orientations, but carrier orientation had a strong biasing effect upon perceived envelope orientation. When the orientation difference between envelope and carrier was small, the carrier orientation was attracted to that of the envelope; when the difference was large (>10°) repulsion was found. The biases were reduced by half-wave rectifying the stimuli, putatively making the envelope visible to a first-order filter (Experiment 2). Discrimination thresholds for envelope orientation were higher than those for carrier orientation, and this difference was greater for briefly-presented parafoveal stimuli than for long duration foveal stimuli (Experiments 3 and 4). We conclude from these results that there are separate mechanisms for envelope and carrier orientation discriminations for large stimuli, but that first- and second-order mechanisms are not independent in the discrimination of orientation.

The effect of intracortical competition on the formation of topographic maps in models of Hebbian learning.

Correlation-based learning (CBL) models and self-organizing maps (SOM) are two classes of Hebbian models that have both been proposed to explain the activity-driven formation of cortical maps. Both models differ significantly in the way lateral cortical interactions are treated, leading to different predictions for the formation of receptive fields. The linear CBL models predict that receptive field profiles are determined by the average values and the spatial correlations of the second order of the afferent activity patterns, whereas SOM models map stimulus features. Here, we investigate a class of models which are characterized by a variable degree of lateral competition and which have the CBL and SOM models as limit cases. We show that there exists a critical value for intracortical competition below which the model exhibits CBL properties and above which feature mapping sets in. The class of models is then analyzed with respect to the formation of topographic maps between two layers of neurons. For Gaussian input stimuli we find that localized receptive fields and topographic maps emerge above the critical value for intracortical competition, and we calculate this value as a function of the size of the input stimuli and the range of the lateral interaction function. Additionally, we show that the learning rule can be derived via the optimization of a global cost function in a framework of probabilistic output neurons which represent a set of input stimuli by a sparse code.

Spatial sensitivity of macaque inferior temporal neurons

Recent findings in dorsal visual stream areas and computational work raise the question whether neurons at the end station of the ventral visual stream can code for stimulus position. The authors provide the first detailed, quantitative data on the spatial sensitivity of neurons in the anterior part of the inferior temporal cortex (area TE) in awake, fixating monkeys. They observed a large variation in receptive field (RF) size (ranging from 2.8 degrees to 26 degrees ). TE neurons differed in their optimal position, with a bias toward the foveal position. Moreover, the RF profiles of most TE neurons could be fitted well with a two-dimensional Gaussian function. Most neurons had only one region of high sensitivity and showed a smooth decline in sensitivity toward more distal positions. In addition, the authors investigated some of the possible determinants of such spatial sensitivity. First, testing with low-pass filtered versions of the stimuli revealed that the general preference for the foveal position and the size of the RFs was not due simply to TE neurons receiving input with a lower spatial resolution at more eccentric positions. The foveal position was still preferred after intense low-pass filtering. Second, although an increase in stimulus size consistently broadened spatial sensitivity profiles, it did not change the qualitative features of these profiles. Moreover, size selectivity of TE neurons was generally position invariant. Overall, the results suggest that TE neurons can code for the position of stimuli in the central region of the visual field.

The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations

In many cortical areas, simple stimuli or task conditions activate large populations of neurons. We hypothesize that such populations support processes of interaction within parametric representations and integration of multiple sources of input and we propose to study these processes using distributions of population activation (DPAs) as a tool. Such distributions can be viewed as neuronal representations of continuous stimulus or task parameters. They are built from basis functions contributed by each neuron. These functions may be explicitly chosen based on tuning curves or receptive field profiles. Or they may be determined by minimizing the distance between chosen target distributions and the constructed DPAs. In both cases, construction of the DPA is based on a set of reference conditions in which the stimulus or task parameters are sampled experimentally. In a second step, basis functions are kept fixed, and the DPAs are used to explore time dependent processing, interaction and integration of information. For instance, stimuli which simultaneously specify multiple parameter values can be used to study interactions within the parametric representation. We review an experiment, in which the representation of retinal position is probed in this way, revealing fast excitatory interactions among neurons representing similar retinal positions and slower inhibitory interactions among neurons representing dissimilar retinal positions. Similarly, DPAs can be used to analyze different sources of input that are fused within a parametric representation. We review an experiment in which the representation of the direction of goal-directed arm movements in motor and premotor cortex is studied when prior and current information about upcoming movement tasks are integrated.

Novel circuits for neural information processing

Information processing in the visual cortex of the mammalian brain allows compact image coding and representation. Receptive field structures found in the visual cortex of the mammalian brain resemble Gabor filters. There has been recent interest in the use of such receptive field profiles for image coding and texture processing. Systems employing such Gabor filters have been implemented in software for a variety of applications. We believe a hardware implementation of such cells will be helpful in artificial visual processing. We have implemented analog VLSI cells which perform such Gabor-like functions. We describe experimental results of our circuit. We have implemented both the sine and cosine type of circuits. The sine and cosine circuits described can be used as edge detection units and on–off center-surround units respectively. Our circuit is the first hardware implementation of a Gabor approximation circuit.