Receptive Fields Of Simple Cells Research Articles

RECEPTIVE FIELD PROPERTIES OF NEAR NEIGHBOR ORIENTATION SELECTIVE NEURONS IN THE VISUAL CORTEX: A MODELING STUDY

The primary visual cortex is organized into clusters of cells having similar receptive fields (RFs). A purely feedforward model has been shown to produce realistic simple cell receptive fields. The modeled cells capture a wide range of receptive field properties of orientation selective cortical cells. We have analyzed the responses of 78 nearby cell pairs to study which RF properties are clustered. Orientation preference shows strongest clustering. Orientation tuning width (hwhh) and tuning height (spikes/sec) at the preferred orientation are not as tightly clustered. Spatial frequency is also not as tightly clustered and RF phase has the least clustering. Clustering property of orientation preference, orientation tuning height and width depend on the location of cells in the orientation map. No such location dependence is observed for spatial frequency and RF phase. Our results agree well with experimental data.

Contrast constancy in natural scenes in shadow or direct light: A proposed role for contrast-normalisation (non-specific suppression) in visual cortex

The range of contrasts in natural scenes is generally thought to far exceed the limited dynamic ranges of individual contrast-encoding neurons in the primary visual cortex. The visual system may employ gain-control mechanisms () to compensate for the mismatch between the range of natural contrast energies and the limited dynamic range of visual neurons; one proposed mechanism is contrast normalisation or non-specific suppression (). This paper aims to evaluate the role of contrast normalisation in human contrast perception, using a computer model of primary visual cortex. The model uses orthogonal pairs of Gabor patches to simulate simple-cell receptive-fields to calculate local, band-limited contrast in a series of 50 digitised photographs of natural scenes. The average range of contrast energies in each image was 2.29 log units, while the “lifetime range” each model simple cell would see across all images was 2.98 log units. These ranges are greater than the dynamic range of real mammalian simple cells. Contrast normalisation (dividing contrast responses by the summed responses of all nearby neurons) reduces contrast ranges, perhaps sufficiently to match them to neurons' limited dynamic ranges. Comparison of images taken under diffuse and direct lighting conditions showed that contrast normalisation can sometimes match these conditions effectively. This may lead to perceptual contrast constancy in the face of spurious changes in contrast caused by natural environmental conditions.

Study of spatial frequency selectivity and its spatial organization in the visual cortex through a feedforward model

A purely feedforward model has been shown to produce realistic simple cell receptive fields (RFs) that show smooth transitions between subregions and fade off gradually at the boundaries (J. Comp. Neurosci. 14 (2003) 211). Here, we show that the modeled cells also capture a wide range of spatial frequency properties of cortical cells. The shape, size and number of the subregions in the RF is found to be an important parameter in determining the frequency selectivity of the cell. The spatial frequency maps obtained through the model show a continuous distribution of spatial frequency preference across the modeled cortex and pinwheels largely co-localize with the extremes of the spatial frequency domains.

Is sparse and distributed the coding goal of simple cells?

The question of why the receptive fields of simple cells in the primary visual cortex are Gabor-like is a crucial one in vision research. Many research efforts (Olshausen and Field 1996, 1997; van Hateren and Ruderman 1998; van Hateren and van der Schaaf 1998) that yield a set of localized, oriented, and bandpass Gabor-like receptive fields believe that sparse and distributed is the coding goal of simple cells. This paper investigates a more general coding strategy that measures equally any departure from normality in the simple cells' responses. That is, we investigate the possibility that highly kurtotic response histograms may result if simple cells explicitly seek, not maximally kurtotic, but rather maximally non-Gaussian response histograms to natural images. It is found that, under this coding strategy, the simulations produce a majority of localized, oriented, bandpass (Gabor-like) receptive fields. Some receptive fields, however, are spatially distributed and show little oriented structure. Nearly all receptive fields, regardless of whether they are Gabor-like or non-Gabor-like, yield highly kurtotic response histograms to natural images. Thus, in seeking maximally non-Gaussian response histograms, receptive fields spontaneously yield highly kurtotic histograms. The presence in our ensemble of nonlocalized, nonoriented receptive fields may be due to the artificial requirement that receptive fields be orthonormal. We conclude that the high kurtoses observed in the response histograms of simple-cell receptive fields to natural images may reflect a property of natural images themselves rather than an explicit coding goal used to structure simple-cell receptive fields.

A neuromorphic parallel vision with orientation selective receptive field

We have developed a vision system which emulates the orientation selective receptive fields of simple cells in the primary visual cortex. The system consists of a silicon retina and a Field Programmable Gate Array (FPGA). First, the image is filtered by the Laplacian–Gaussian-like receptive field of the silicon retina, and then it is transferred to the FPGA. The orientation selection chip selectively aggregates multiple pixels of the silicon retina, mimicking the feedforward model proposed by Hubel and Wiesel. The present system exhibits the orientation selectivity with even and odd-type responses. The design of the present chip is applicable to implement higher order cells in the primary visual cortex, such as the complex cell.

Spatiotemporal receptive fields maximizing temporal coherence in natural image sequences

The relationship between the structure and functionality of the visual system and the properties of natural visual stimuli is an active research topic in computational visual neuroscience. It has previously been shown that maximization of temporal coherence of activity levels is one computational principle which leads to the emergence of simple-cell-like spatial receptive fields from natural image sequences. In this paper we extend previous results by examining the case of spatiotemporal receptive fields. We show that application of the same principle of temporal coherence in the spatiotemporal case yields receptive fields which are not only localized, oriented and multiscale, as in the spatial case, but also share some important temporal characteristics with spatiotemporal simple-cell receptive fields. Quantitative measurements of the properties of the resulting receptive fields are also provided, and these are compared against similar results obtained with independent component analysis.

Binocular disparity encoding cells generated through an Infomax based learning algorithm

A learning algorithm for a model binocular cell was derived according to an information maximization principle and by using a low signal-to-noise-ratio approximation. The algorithm updates cell's synaptic weights so that the information obtained from the cell's output is increased. According to the algorithm, model binocular cells were trained by using computer-generated stereo images as training data. As a result, cells tuned to various disparities were generated. Also, generated synaptic weight patterns of the cells were similar to Gabor-wavelets and receptive fields of simple cells in the visual cortex. Thus, they were orientation and spatial frequency selective as well as disparity selective. Gabor functions were used to fit the generated weight patterns. The fitting results indicated that the generated cells encode disparities in terms of phase disparity and/or position disparity. This result agrees with experimental findings by Anzai et al. [J Neurophys 82 (1999) 874] and is consistent with ICA-based theoretical results obtained [Network: Comput Neural Syst 11 (2000) 191].

Spatiotemporal receptive fields maximizing temporal coherence in natural image sequences

The relationship between the structure and functionality of the visual system and the properties of natural visual stimuli is an active research topic in computational visual neuroscience. It has previously been shown that maximization of temporal coherence of activity levels is one computational principle which leads to the emergence of simple-cell-like spatial receptive fields from natural image sequences. In this paper we extend previous results by examining the case of spatiotemporal receptive fields. We show that application of the same principle of temporal coherence in the spatiotemporal case yields receptive fields which are not only localized, oriented and multiscale, as in the spatial case, but also share some important temporal characteristics with spatiotemporal simple-cell receptive fields. Quantitative measurements of the properties of the resulting receptive fields are also provided, and these are compared against similar results obtained with independent component analysis.

Haphazard wiring of simple receptive fields and orientation columns in visual cortex.

The receptive fields of simple cells in visual cortex are composed of elongated on and off subregions. This spatial arrangement is widely thought to be responsible for the generation of orientation selectivity. Neurons with similar orientation preferences cluster in "columns" that tile the cortical surface and form a map of orientation selectivity. It has been proposed that simple cell receptive fields are constructed by the selective pooling of geniculate receptive fields aligned in space. A recent analysis of monosynaptic connections between geniculate and cortical neurons appears to reveal the existence of "wiring rules" that are in accordance with the classical model. The precise origin of the orientation map is unknown, but both genetic and activity-dependent processes are thought to contribute. Here, we put forward the hypothesis that statistical sampling from the retinal ganglion cell mosaic may contribute to the generation of simple cells and provide a blueprint for orientation columns. Results from computer simulations show that the "haphazard wiring" model is consistent with data on the probability of monosynaptic connections and generates orientation columns and maps resembling those found in the cortex. The haphazard wiring hypothesis could be tested by measuring the correlation between the orientation map and the structure of the retinal ganglion cell mosaic of the contralateral eye.

Estimating distributed coding efficiency in orthogonal models of facial processing.

Orthogonal facial processing models attempt to mimic the local decomposition performed in the visual cortex by simple cell receptive fields. The purpose of this study was to investigate how the neurophysiological validity of orthogonal models of facial processing could be improved by implementing a "whitening" filter, based on current knowledge of similar filtering that occurs in the retina. By using a metric known as the "distributed coding efficiency index" (DCE), this study demonstrates that an orthogonal facial processing model significantly increased coding efficiency when a low-pass, zero-phase whitening filter was applied. The extent to which orthogonal decomposition of filtered data represents a realistic V1 model is discussed.

Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes

Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods-spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution ('reginv') is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment-the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.

Shaping up simple cell's receptive field of animal vision by ICA and its application in navigation system

Scientists and experts have explored the mechanism of visual systems for decades for smart image processing and pattern recognition in order to satisfy sophisticated engineering applications. In this paper we apply independent component analyses' (ICA) unsupervised learning to natural images, topography images and other special environment images for demonstrating the simple-cell's process in animal vision. Our results confirm an early biological experiment (Nature 228 (1970) 419) about the growth of simple cells in cat's V1 area. Furthermore, by applying ICA methodology and the simplex algorithm, the unsupervised neural synapse's learning can obtain the receptive fields in visual cortex and can simulate the growth of the visual cortex of young animal in the special environment. These findings imply that an input image can be efficiently represented by ICA bases. An application of image matching in the navigation by ICA is shown that the animal visual system method is indeed better than those classical methods at least more than 5%.

Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video

Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic time dependencies of image sequences instead of their nongaussianity. We show that simple-cell-type receptive fields emerge when temporal response strength correlation is maximized for natural image sequences. Thus, temporal response strength correlation, which is a nonlinear measure of temporal coherence, provides an alternative to sparseness in modeling simple-cell receptive field properties. Our results also suggest an interpretation of simple cells in terms of invariant coding principles, which have previously been used to explain complex-cell receptive fields.

Methods for first-order kernel estimation: simple-cell receptive fields from responses to natural scenes

Recent studies have recovered receptive-field maps of simple cells in visual cortex from their responses to natural scene stimuli. Natural scenes have many theoretical and practical advantages over traditional, artificial stimuli; however, the receptive-field estimation methods are more complex than for white-noise stimuli. Here, we describe and justify several of these methods—spectral correction of the reverse correlation estimate, direct least-squares solution, iterative least-squares algorithms and regularized least-squares solutions. We investigate the pros and cons of the different methods, and evaluate them in a head-to-head comparison for simulated simple-cell data. This shows that, at least for quasilinear simulated simple cells, a regularized solution (‘reginv’) is most efficient, requiring fewer stimulus presentations for high-resolution reconstruction of the first-order kernel. We also investigate several practical issues that determine the success of this kind of experiment—the effects of neuronal nonlinearities, response variability and the choice of stimulus regime.

Spike-based synaptic plasticity and the emergence of direction selective simple cells: mathematical analysis.

In the companion paper we presented extended simulations showing that the recently observed spike-timing dependent synaptic plasticity can explain the development of simple cell direction selectivity (DS) when simultaneously modifying the synaptic strength and the degree of synaptic depression. Here we estimate the spatial shift of the simple cell receptive field (RF) induced by the long-term synaptic plasticity, and the temporal phase advance caused by the short-term synaptic depression in response to drifting grating stimuli. The analytical expressions for this spatial shift and temporal phase advance lead to a qualitative reproduction of the frequency tuning curves of non-directional and directional simple cells. In agreement with in vivo recordings, the acquired DS is strongest for test gratings with a temporal frequency around 1-4 Hz. In our model this best frequency is determined by the width of the learning function and the time course of depression, but not by the temporal frequency of the 'training' stimuli. The analysis further reveals the instability of the initially symmetric RF, and formally explains why direction selectivity develops from a non-directional cell in a natural, directionally unbiased stimulation scenario.

Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex.

I present measurements of the spatial structure of simple-cell receptive fields in macaque primary visual cortex (area V1). Similar to previous findings in cat area 17, the spatial profile of simple-cell receptive fields in the macaque is well described by two-dimensional Gabor functions. A population analysis reveals that the distribution of spatial profiles in primary visual cortex lies approximately on a one-parameter family of filter shapes. Surprisingly, the receptive fields cluster into even- and odd-symmetry classes with a tendency for neurons that are well tuned in orientation and spatial frequency to have odd-symmetric receptive fields. The filter shapes predicted by two recent theories of simple-cell receptive field function, independent component analysis and sparse coding, are compared with the data. Both theories predict receptive fields with a larger number of subfields than observed in the experimental data. In addition, these theories do not generate receptive fields that are broadly tuned in orientation and low-pass in spatial frequency, which are commonly seen in monkey V1. The implications of these results for our understanding of image coding and representation in primary visual cortex are discussed.

Anatomical evidence of subcortical contributions to the orientation selectivity and columns of the cat's primary visual cortex

Physiological studies have demonstrated a subcortical origin for orientation selectivity and the orientation columns of the primary visual cortex. However, there are no anatomical data showing how subcortical cells contribute to this important property. Optical imaging, combined with 1,1′-dioctadecyl-3,3,3,3′-tetramethylin-docarbocyanine perchlolate (DiI) and biocytin retrograde tracing, reveals that relay cells projecting to a single orientation column representing the horizontal meridian were clustered within 300 μm in the dorsal lateral geniculate nucleus (LGN). Interestingly, some labeled cells were located on a line parallel to an iso-elevation line in the LGN. Thus, according to the quantitative projection of the visual field to the LGN (J. Comp. Neurol. 143 (1971) 101), their receptive fields must distribute horizontally in alignment in the visual field providing the first anatomical evidence for Hubel and Wiesel's model of simple cell receptive fields (J. Physiol. 160 (1962) 106).

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.

Full identification of a linear-nonlinear system via cross-correlation analysis.

A statistical model used extensively in vision research consists of a cascade of a linear operator followed by a static (memoryless) nonlinearity. Common applications include the measurement of simple-cell receptive fields in primary visual cortex and the modeling of human performance in various psychophysical tasks. It is well known that the front-end linear filter of the model can readily be recovered, up to a multiplicative constant, using reverse-correlation techniques. However, a full identification of the model also requires an estimation of the output nonlinearity. Here, we show that for a large class of static nonlinearities, one can obtain analytical expressions for the estimates. The technique works with both Gaussian and binary noise stimuli. The applicability of the method in physiology and psychophysics is demonstrated. Finally, the proposed technique is shown to converge much faster than the currently used linear-reconstruction method.

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.