内容和运动方向感知计算模型

对视野中的物体及运动方向进行感知是视觉感知的基本问题之一,较高级视皮层从V1区的简单细胞开始分为两个通路：“What通路”和“Where通路”．前者对物体的形状、颜色、纹理等内容感知,后者对空间运动速度和方向等感知．本文利用仿脑视觉信息处理计算结构,研究视觉内容和运动方向上的感知计算模型、计算机理和学习算法．该计算模型是一个三层的神经网络,第一层是视觉信号输入层,用于接收外界图像刺激．第二层是神经信息内部表象层,与第一层的网络联结是通过神经元稀疏表象原理自适应形成神经元的感受野．为此,引入Kullback_Leibler散度描述神经元响应的独立性,极小化该代价函数导出网络联结权值的学习算法．从自然图像块中学习得到图像基函数,这些基函数具有局部性、朝向性和带通滤波性．这些性质与生理实验结果中的V1区简单细胞感受野特征相吻合．将这些基函数作为神经元的感受野,并在第三层对较高级视皮层的内容感知和运动感知神经元进行建模．在理想刺激中加入一定量的噪声后,该模型对内容和运动方向的感知仍有较高的准确率和较好的鲁棒性．最后给出的实验仿真结果说明模型的可行性和学习算法的简单有效性．     

初级视觉皮层“简单”与“复杂”神经元动力学的计算建模

本文回顾了我们在哺乳动物视觉皮层的建模工作.利用初级视觉皮层的大规模神经元网络模型,我们解释了初级视觉皮层里“简单”与“复杂”神经元现象的网络机制.所谓的“简单”细胞对视觉刺激的反应近似线性,而“复杂”细胞对视觉刺激是非线性的.我们的模型成功地再现了简单和复杂细胞分布的实验数据.     

视觉经验与皮层神经元感受野特征

视觉经验在高等动物视皮层神经元感受野特征形成的过程中起着重要作用。正常成年动物皮层细胞的感受野具有明显的方位选择性和双眼视差,而缺乏视觉经验的初生动物和在失视条件下成长的动物,其视皮层中方位选择性细胞数量很少,双眼细胞没有视差调谐。后天的视觉训练能够明显地影响和改变幼年动物视皮层感受野的大小、方位和视差调谐。视觉经验的作用在生后4～7周达到高峰,以后逐渐减弱,对成年动物影响不明显。     

以菌紫质LB膜为基础的两种视觉感受野的某些特性的模拟

发展视觉模拟的新技术和新材料一直为人们所期待。本文用菌紫质ＬＢ膜模拟动物视皮层简单细胞的ＯＮ－中心条型感受野,又用此膜以一维形式模拟了视网膜ＯＮ－中心型神经节Ｘ型细胞感受野。在观察了它们的感受野地图后,用前一种感受野模拟了取向调谐曲线和长度调谐曲线,用后一种感受野模拟了“零位置”和马赫效应。所有的模拟结果与电生理实验的有关结果符合较好。说明以菌紫质为材料的视觉功能和特性模拟前景良好。     

视觉皮层复杂细胞时空编码特性

针对输入在视皮层的编码表达,在地空滤波窗口基础上构建了一个复杂细胞时空编码模型,对几种特殊的输入函数进行了编码仿真实验,结果说明了视皮层复杂细胞时空整合编码序列的精细时间结构进行视觉输入的神经表象。     

基于光敏蛋白的猫视皮层细胞双眼汇聚功能的模拟

已知光敏蛋白菌紫质ＬＢ膜具有类似于视觉系统感受野的对光微分响应。利用这个特性,本文组装了一对人工视皮层条型简单细胞感受野,并测定了其朝向选择特性及ＯＮ－区闪光融合频率响应特性。在此基础上,用这一对人工感受野组成了猫视皮层细胞双眼汇聚功能模拟系统,并模拟了猫视皮层细胞双眼汇聚功能。     

基于光敏蛋白的猫视皮层细胞双眼汇聚功能的模拟

已知光敏蛋白菌紫质ＬＢ膜具有类似于视觉系统感受野的对光微分响应。利用这个特性,本文组装了一对人工视皮层条型简单细胞感受野,并测定了其朝向选择特性及ＯＮ－区闪光融合频率响应特性。在此基础上,用这一对人工感受野组成了猫视皮层细胞双眼汇聚功能模拟系统,并模拟了猫视皮层细胞双眼汇聚功能。     

简单细胞方位选择性感受野组织形成的神经网络模型

为了阐明视皮层简单细胞方位选择性感受野形成的动态组织过程, 试图构建一个由侧膝体神经元和视皮层简单细胞组成的, 且遵从Hebbian学习规则的神经网络模型. 通过该模型来考察简单细胞对自然图像刺激特征的编码过程和神经表达. 结果表明, 感受野的结构正反映了简单细胞的最优方位选择性, 它也是由非监督学习过程决定并自组织涌现的. 这还说明简单细胞的方位选择性是在层间细胞的相互作用基础上动态自组织的结果.     

基于fMRI的屈光参差性弱视静息视觉网络的研究

利用静息功能磁共振成像技术,对屈光参差性弱视(anisometropic amblyopia)患者静息态视觉网络进行研究,分析此类患者大脑视觉皮层功能受到的影响。采用独立成分分析(independent component analysis, ICA)这一数据驱动方法对8名屈光参差性弱视患者和11名正常对照的静息数据进行分离,并采用拟合度值(goodness-of-fit scores)分析挑选出静息视觉网络,将结果进行组内分析和组间分析。结果表明,屈光参差性弱视的静息视觉网络中,多级视觉皮层均发生了明显的功能损害,其功能连接度的范围与强度显著低于正常组,而且,高级别纹外皮层比低级别纹状皮层损害更加明显。静息fMRI为深入研究弱视初、高级视觉皮层功能损害的发病机制提供了新的方法。     

老年猫视皮层细胞对刺激反应的对比敏感度下降伴随皮层内抑制作用减弱(英文)

对人类和动物的心理学研究证实,老年个体的视觉对比敏感度相对青年个体显著下降。为揭示其可能的神经机制,采用在体细胞外单细胞记录技术研究青、老年猫(Felis catus)初级视皮层(primary visual cortex,V1)细胞对不同视觉刺激对比度的调谐反应。结果显示,老年猫V1细胞对视觉刺激反应的平均对比敏感度比青年猫显著下降,这与灵长类报道的研究结果相一致,表明衰老影响视皮层细胞对视觉刺激反应的对比敏感度是灵长类和非灵长类哺乳动物中普遍存在的现象,并可能是介导老年性视觉对比敏感度下降的神经基础。另外,与青年猫相比,老年猫初级视皮层细胞对视觉刺激的反应性显著增强,信噪比下降,感受野显著增大,表明衰老导致的初级视皮层细胞对视觉刺激反应的对比敏感度下降伴随着皮层内抑制性作用减弱。     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

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.     

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 computational 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.     

A self-organizing neural network model for the development of complex cells

On the basis of recent neurophysiological findings on the mammalian visual cortex, a selforganizing neural network model is proposed for the understanding of the development of complex cells. The model is composed of two kinds of connections from LGN cells to a complex cell. One is direct excitatory connections and the other is indirect inhibitory connections via simple cells. Inhibitory synapses between simple cells and complex cells are assumed to be modifiable. The model was simulated on a computer to confirm its behavior.     

Sparsity-Regularized HMAX for Visual Recognition

About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC) or independent component analysis (ICA), two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC) and medial temporal lobe (MTL). Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.     

Energy and phase orientation mechanisms: a computational model

A computational model is proposed for spatial orientation processing beyond the initial stage of linear filtering in visual cortex. The model accounts for orientation pop-out, edge location and orientation, and bar location and orientation. It naturally extends to higher order orientation symmetries. The model is consistent with much of the current understanding of early processing in mammalian visual cortex. It builds on the notions of orientation and spatial frequency specific simple cells, any subsequent non-linearity, and orientation 'pooling'. The processing treats simple cell energy, real, and imaginary responses in a unified way to generate 'feature maps'. The 'pooling' operation in each case is a discrete Fourier transform of the simple cell responses over orientation. The suggested processing has implications for psychophysics (e.g. providing an explanation of why orientation discrimination thresholds are more than an order of magnitude less than simple cell orientation bandwidths), provides some understanding of the variety of 'complex-cell' properties found in visual cortex, and provides a plausible starting point for subsequent processing.     

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.