Shaping of Receptive Fields in the Visual Cortex During Retinal Maturation

We present a computational study of the formation of simple-cell receptive field patterns in the primary visual cortex. Based on the observation that the spatial frequency of the retinal filter increases postnatally, our results explain differences in the time course of the development of orientation selectivity in binocularly deprived and normally reared kittens. Development after eye-opening in normal animals is modelled by training with natural images, whereas in the case of binocular deprivation noise-like stimulation continues. Further, it is shown that different orientation selectivities are obtained for network models trained with natural images in contrast to random phase images of identical second order statistics. The latter finding suggests that higher-order statistics of the inputs influences development of primary visual cortex. Finally, we search for quantities that identify possible signatures of natural image statistics in order to specify the amount of constructiveness that visual experience has on the formation of receptive fields.     

Receptive Fields of Visually Sensitive Neurons of the Extrastriate Associative Area 21b of the Cat Cerebral Cortex

Organization of the receptive fields (RFs) of neurons of the extrastriate associative region 21b of the cerebral cortex was studied in cats. Most neurons under study (63%) were “monocular,” while 37% of the cells were “binocular” units. Among 178 neurons examined in detail, heterogeneous RF functional organization was typical of about 76% of the units; point-to-point testing of the entire RF area by stationary stimuli resulted in the generation of various types of responses (on, off, or on-off). The rest of the neurons (24%) generated homogeneous responses. The dimension, form, and functional organization of RFs of the neurons under study depended to a certain extent on the parameters of visual stimuli used for the measurements. Examination of the influence of the visual space, which surrounded the RF, on responses of the neurons evoked by stimulation of the RF per se showed that darkening of the visual space adjacent to the RF inhibited neuronal responses to moving stimuli; in some cases the responses were totally suppressed. Analysis of spatial overlapping of the RF sequentially recorded in the course of each insertion of the electrode showed that the density of distribution of the overlapping RF areas of neighboring neurons with the RF of the examined neuron is irregular, and that the RF is of a mosaic nature. We hypothesize that the visual space surrounding the RF plays a significant role in the formation of responses of visually sensitive neurons to presentation of moving stimuli. Neirofiziologiya/Neurophysiology, Vol. 37, No. 3, pp. 223–234, May–June, 2005.     

Double Receptive Fields of Neurons of the Lateral Geniculate Body of the Cat

We studied spatial organization of the receptive fields (RF) of neurons of the lateral geniculate body (LGB) on unanesthetized cats (pretrigeminal brainstem section). After identification of localization and borders of the RF of the neuron under study, we scanned with a sufficient resolution the entire visual field and tried to find additional space zones, whose stimulation could influence the impulse activity of the neuron. These experiments demonstrated that 24 neurons of 167 examined units (14%) could react to the presentation of visual stimuli within the visual space outside the main RF, but we were unable to determine borders of these additional zones with a sufficient accuracy. In 12 neurons (7% of the group under study), localization, dimensions, and specific features of an additional RF (RF-2) could be clearly determined. As a rule, the center of the RF-2 was localized at a distance of 20-40^O; or even farther from the center of the main RF (RF-1). To activate the neuron from the RF-2, application of greater visual stimuli was necessary (as compared with stimulation of the RF-1). Thus, two RF of one and the same neuron had dissimilar spatial organizations and qualitatively differed from each other in their stationary and dynamic characteristics. Considering our findings, we hypothesize that the RF-2 of LGB neurons can play a certain role in perception of large objects within the visual field of the animal by promoting formation of the avoidance reaction.     

Topographical Estimation of Visual Population Receptive Fields by fMRI

Visual cortex is retinotopically organized so that neighboring populations of cells map to neighboring parts of the visual field. Functional magnetic resonance imaging allows us to estimate voxel-based population receptive fields (pRF), i.e., the part of the visual field that activates the cells within each voxel. Prior, direct, pRF estimation methods¹ suffer from certain limitations: 1) the pRF model is chosen a-priori and may not fully capture the actual pRF shape, and 2) pRF centers are prone to mislocalization near the border of the stimulus space. Here a new topographical pRF estimation method² is proposed that largely circumvents these limitations. A linear model is used to predict the Blood Oxygen Level-Dependent (BOLD) signal by convolving the linear response of the pRF to the visual stimulus with the canonical hemodynamic response function. PRF topography is represented as a weight vector whose components represent the strength of the aggregate response of voxel neurons to stimuli presented at different visual field locations. The resulting linear equations can be solved for the pRF weight vector using ridge regression³, yielding the pRF topography. A pRF model that is matched to the estimated topography can then be chosen post-hoc, thereby improving the estimates of pRF parameters such as pRF-center location, pRF orientation, size, etc. Having the pRF topography available also allows the visual verification of pRF parameter estimates allowing the extraction of various pRF properties without having to make a-priori assumptions about the pRF structure. This approach promises to be particularly useful for investigating the pRF organization of patients with disorders of the visual system.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.     

初级视皮层神经元非经典感受野研究的新进展

感觉皮层神经元的非经典感受野(简称"外周")对经典感受野(简称"中心")的调节作用广泛存在于哺乳动物中,被认为是感觉皮层神经元的基本特性.以初级视皮层神经元为例,刺激其外周能有效地调节刺激其中心引起的反应,这种作用主要是抑制性的.理解初级视皮层神经元的外周对中心的调节机制能够深入揭示哺乳动物的感觉皮层神经元信息处理的基本原则.本文综述了引起初级视皮层神经元非经典感受野对经典感受野调节作用的神经环路机制和计算模型研究的进展.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Joint coding of visual input and eye/head position in V1 of freely moving mice

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A cooperation and competition based simple cell receptive field model and study of feed-forward linear and nonlinear contributions to orientation selectivity

We present a model for development of orientation selectivity in layer IV simple cells. Receptive field (RF) development in the model, is determined by diffusive cooperation and resource limited competition guided axonal growth and retraction in geniculocortical pathway. The simulated cortical RFs resemble experimental RFs. The receptive field model is incorporated in a three-layer visual pathway model consisting of retina, LGN and cortex. We have studied the effect of activity dependent synaptic scaling on orientation tuning of cortical cells. The mean value of hwhh (half width at half the height of maximum response) in simulated cortical cells is 58° when we consider only the linear excitatory contribution from LGN. We observe a mean improvement of 22.8° in tuning response due to the non-linear spiking mechanisms that include effects of threshold voltage and synaptic scaling factor.     

Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex

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应用菌紫质膜模拟动物“边”感受野并检测图像边界轮廓

菌紫质膜是一种颇有前途的分子电子器件材料。Tsutornu^[1]以菌紫质膜为材料,完成了具有动物视觉功能特点的含256像素的图像传感器。此外,菌紫质膜还被用来模拟视觉感受野的运算功能^[2-4]简单细胞“边”感受野由两个互为颉顽的ON响应区和OFF响应区组成。它对图像中的对比度信息具有敏感性。菌紫质膜因其优良的分辨率(5000线/min),灵敏度(1-805/cm²)以及光电特性而足以被用来制作人工“边”感受野,并模拟简单细胞“边”感受野的这一特性。以此为基础,本文构建了一个图像轮席检测的原理系统,成功地检测了简单图像轮廊。     

猫V1区不同外周整合细胞的经典感受野属性研究

以家猫为动物模型,采用细胞外记录的方法,测试了82个初级视皮层细胞的方位和方向调谐以及感受野大小.基于细胞的面积整合特性,区分出52个外周抑制型细胞和30个外周无抑制型细胞.所有被测细胞均存在强的方位选择性,而外周无抑制型细胞比抑制型细胞有更强的方位选择性.两类细胞的方向选择性没有显著性差异.外周抑制型细胞比外周无抑制型细胞有着更大的动作电位发放率.采用两种不同方法测量两类细胞的感受野范围,却产生了不同的结果:用最小反应区测量发现抑制型细胞的经典感受野更大,而用面积整合曲线测量时外周无抑制型细胞的感受野更大.     

Dependence of Visual Cell Properties on Intracortical Synapses Among Hypercolumns: Analysis by a Computer Model

The role of intracortical synapses in affecting the property of visual cells is investigated by means of an original mathematical model of cortical circuitry in V1. The model represents a compromise between computational simplicity and physiological reliability. The model incorporates four different inputs into a cortical cell: thalamic input from the lateral geniculate nucleus, according to an even Gabor function; short-range inhibition confined within the hypercolumn; a long-range excitation, which emphasizes the properties of the input; and a long-range inhibition. In the model we assume that all cells receive a similar thalamic input, which differs simply according to its position in the retina and orientation preference. Simulations were performed, with different parameter values, to assess the main characteristics of cell response (i.e., the width and locations of subregions in the receptive field (RF), orientation tuning curve, and response to drifting and counterphase gratings) as a function of the strength and extension of intracortical excitatory synapses. Results suggest that, if intracortical excitation is confined within the hypercolumn, the cells exhibit the same properties as simple cells, both with regards to the width and shape of the RF, orientation tuning curve, and response to drifting and counterphase gratings. By contrast, if excitatory synapses extend beyond the hypercolumn with sufficient strength, the cells exhibit the typical characteristics of complex cells. A progressive shift from complex to simple cells can be realized with a monotonic variation in parameters. Simulations are also performed with a hierarchical model, to suggest possible experiments able to discriminate the present recurrent mechanism from the classical hierarchical one. Results support the assumptions of previous simpler models (Chance et al., 1999) and may help to understand and assess the role of intracortical synapses in rigorous quantitative terms.     

在体神经元胞外记录用于大脑皮层可塑性研究

神经元网络可塑性是大脑学习和记忆功能的基础,可塑性的变化也是某些脑功能疾病的成因。研究大脑皮层可塑性不仅可以为认识可塑性机制提供基本方法,也可对自然衰老过程和神经退行性疾病的病理过程进行观测,进而可以为评价抗衰老药物和治疗神经退行性疾病提供新方法。本文基于经典的大鼠胡须配对模型建立了一套实验方案,通过在体细胞外记录实验的数据分析,比较修剪胡须后相同时间内神经元感受野不对称变化程度的差异,衡量不同生理条件下大鼠体感皮层神经元网络可塑性。本文以中年和青年大鼠体感皮层神经元网络可塑性比较为例,详细介绍了实验方法中的关键技术和操作,如皮层D2功能柱的定位和D2功能柱内不同层神经元的定位等,结果和我室以前相关研究证明了此实验方案的可行性。     

Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex

In the primate visual pathway, orientation tuning of neurons is first observed in the primary visual cortex. The LGN cells that comprise the thalamic input to V1 are not orientation tuned, but some V1 neurons are quite selective. Two main classes of theoretical models have been offered to explain orientation selectivity: feedforward models, in which inputs from spatially aligned LGN cells are summed together by one cortical neuron; and feedback models, in which an initial weak orientation bias due to convergent LGN input is sharpened and amplified by intracortical feedback. Recent data on the dynamics of orientation tuning, obtained by a cross-correlation technique, may help to distinguish between these classes of models. To test this possibility, we simulated the measurement of orientation tuning dynamics on various receptive field models, including a simple Hubel-Wiesel type feedforward model: a linear spatiotemporal filter followed by an integrate-and-fire spike generator. The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells. A simple feedback model of interacting cells is also considered. This model is successful in explaining the appearance of Mexican-hat orientation profiles, but other features of the data continue to be unexplained.     

Sparse thalamocortical convergence

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Responses of neurons of the extrastriate cortex of the cat brain to moving stimuli of different forms

We examined responses of neurons of the field 21b of the cat brain cortex to presentation of moving visual stimuli of different forms. Characteristics of the responses of about 54% of the studied neurons showed that in these cases configurations of the contours of moving stimuli were to a certain extent discriminated. Most neurons selectively reacting to changes in the form of the stimulus were dark-sensitive units (they generated optimum responses to presentation of dark visual stimuli on the light background). Detailed examination of the spatial infrastructure of receptive fields (RFs) of the neurons and comparison of this structure with the selectivity of neuronal responses showed that there is no significant correlation between static organization of the RF and responses of the neuron to the movements of stimuli of different forms. We hypothesize that the dynamic infrastructure of the RF and the combined activity of functional groups of neurons, whose RFs spatially overlap the RF of the neuron under study, play a definite role in the mechanisms responsible for neuronal discrimination of the form of the visual stimulus. Neirofiziologiya/Neurophysiology, Vol. 38, No. 1, pp. 61–71, January–February, 2006.     

A Nonlinear Model of the Behavior of Simple Cells in Visual Cortex

Despite their structured receptive fields (RFs) and the strong linear components in their responses, most simple cells in mammalian visual cortex exhibit nonlinear behaviors. Besides the contrast-response function, nonlinearities are evident in various types of failure at superposition tasks, in the disagreement between direction indices computed from drifting and counterphase flickering gratings, in various forms of response suppression (including end- and side-stopping, spatial-frequency-specific inhibition and cross-orientation inhibition), in the advance of phase with increasing contrast, and in phase-insensitive and frequency-doubled responses to counterphase flickering gratings. These behaviors suggest that nonlinearities are involved in the operation of simple cells, but current models fail to explain them. A quantitative model is presented here that purports to describe basic and common principles of operation for all visual cortical cells. Simple cells are described as receiving afferents from multiple subunits that differ in their individual RFs and temporal impulse responses (TIRs). Subunits are independent and perform a spatial integration across their RFs followed by halfwave rectification and temporal convolution with their TIRs. This parallel operation yields a set of temporal functions representing each subunit's contribution to the membrane potential of the host cell, whose final form is given by the weighted sum of all subunits' contributions. By varying the number of subunits and their particular characteristics, different instances of the model are obtained each of which displays a different set of behaviors. Extensive simulation results are presented that illustrate how all of the reported nonlinear behaviors of simple cells arise from these multi-subunit organizations.     

Responses of Neurons of the Extrastriate Area 21b of the Cat Brain Cortex to Changes in Orientation of Moving Visual Stimuli

We studied the responses of neurons of the extrastriate cortical area 21b of the cat to changes in orientation of the movements of visual stimuli within the receptive field (RF) of the neuron under study. Our experiments demonstrated that 24 of 108 cells (22%) responded differentially to a certain extent to orientation of the movements of visual stimuli. As a whole, neurons of the area 21b did not demonstrate fine tuning on the optimum angle of orientation. In many cases, neuronal responses to different orientations of the movement of visual stimulus depended significantly on specific parameters of this stimulus (its shape, dimensions, and contrast). Some directionally sensitive neurons responded to a change in orientation of the movement of visual stimuli by modification of the index of directionality. We also studied spatial organization of the RF of neurons with the presentation of stationary visual stimuli. Comparison of the neuronal responses to a change in orientation of the movements of stimuli and to presentation of stationary stimuli showed that the correlation between the orientation sensitivity of the neuron under study and the stationary functional organization of its RF was insignificant. We hypothesize that inhibitory processes and subthreshold influences from a space surrounding the RF play a special role in the formation of the neuronal responses generated in the associative visual cortical regions to visual stimulation.