基于初级视觉皮层的同步振荡目标检测模型

提出一种基于初级视觉皮层的目标检测模型,该模型只采用方位选择性细胞和皮层内水平连接等Ｖ１基本单元,它以链码表示的目标轮廓作为知识,允许该知识以时间脉冲的形式控制Ｖ１区内神经细胞的动态活动,使与知识轮廓形状相符合的轮廓内的细胞进入同步振荡状态,实现对视野中特定目标轮廓的识别。计算机仿真结果表明,在较高级皮层的“知识”控制之下,初级视觉皮层结构上实现简单的目标检测是可行的。     

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。     

视差检测：简单细胞、复杂细胞及能量模型

立体视觉信息的处理在于皮层双眼性细胞的活动.皮层中简单细胞对视差的编码方式被认为有两种：位置差（position shift）和相位差（phase shift）,但简单细胞并不适合作为视差检测器.对一些复杂细胞的视差响应特性的生理研究,发现复杂细胞是一种比较适合的视差检测器.模型的研究提出基于这类简单细胞的复杂细胞能量模型,可以很好的检测视差,并可以较好的解释一些生理现象.     

视觉皮层下通路与拓扑知觉

研究表明，视觉通路除了经典的视觉皮层通路外，还有一条“古老的”、快速的皮层下通路负责在有意识和无意识状态下快速处理与情绪相关的信息。皮层下视觉通路由上丘、枕核和杏仁核组成，而且不经过初级视觉皮层。我们前期研究表明，初级视觉皮层与拓扑知觉信息加工没有关系，而皮层下视觉通路负责处理视觉拓扑信息。基于这些发现，我们认为，在早期视觉中，大脑检测涉及生命攸关的信号，这些信号告诉大脑：环境中有物体出现或消失，使大脑进入警戒状态，这对物种的生存至关重要。因此，在早期视觉中，需要检测的要素只是物体的“出现”和“消失”，而不是“纹理”、“形状”等。“出现”和“消失”都是拓扑特征的变化。拓扑感知和皮层下视觉通路的存在可能是早期预警的神经基础。在灵长类动物中，视网膜外周区域主要由视杆细胞构成，该区域接收的视觉信息主要通过皮层下视觉通路进行处理；视网膜中心区域（即中央凹）主要由视锥细胞构成，视觉空间分辨率变得非常高，该区域视觉信息处理主要是由视觉皮层负责。研究表明，由视杆细胞构成的视网膜是个“古老”结构，在1亿多年前就出现了；而视锥细胞构成的视网膜类型较为“年轻”，在5 000万年前才出现。所以，我们的视网膜从“古老”结构演化到“年轻”结构至少用了5 000万年的时间，它是由一个古老结构和一个年轻结构共同组成的“嵌合体”。当我们讨论皮层下通路存在的意义时，我们也应该结合皮层通路的功能来统一考虑。     

视觉中枢研究的新进展

在视觉传入通路中,各级神经元对视觉图象的传递和加工起着不同的作用。视网膜和外膝体细胞能传递图象的平均亮度,增强边缘和拐角;初级视皮层细胞能检测图象的基本特征,即线条/边缘的方位、运动方向、空间频率和深度等;高级视皮层细胞能对基本特征进行整合,从而反应复杂图形的整体特性。在视皮层内,调谐特性相同的细胞在垂直方向上排列成规则的“功能柱”结构,各种不同的功能柱组合成“超柱”,它可能是分析图象的基本功能单元.     

视觉刺激对猴初级视皮层神经元眼球位置效应的影响

用细胞外记录的方法,研究了视觉刺激对清醒猴初级视皮层神经元眼球位置效应的影响,当猴注视电视屏幕上２５个不同位置的小光点时,屏蔽上分别给予两种不同形式的视觉刺激：注视点周围的闪光圆环和感受野内的移动光条。这两种刺激都能增强初级视皮层神经元的眼球位置相关活动,并相应地使受眼球位置调制的神经元的比例明显增加。     

综述: 初级视皮层的亮度信息加工

亮度(luminance)是最基本的视觉信息.与其他视觉特征相比,由于视神经元对亮度刺激的反应较弱,并且许多神经元对均匀亮度无反应,对亮度信息编码的神经机制知之甚少.初级视皮层部分神经元对亮度的反应要慢于对比度反应,被认为是由边界对比度诱导的亮度知觉(brightness)的神经基础.我们的研究表明,初级视皮层许多神经元的亮度反应要快于对比度反应,并且这些神经元偏好低的空间频率、高的时间频率和高的运动速度,提示皮层下具有低空间频率和高运动速度通路的信息输入对产生初级视皮层神经元的亮度反应有贡献.已经知道初级视皮层神经元对空间频率反应的时间过程是从低空间频率到高空间频率,我们发现的早期亮度反应是对极低空间频率的反应,与这一时间过程是一致的,是这一从粗到细的视觉信息加工过程的第一步,揭示了处理最早的粗的视觉信息的神经基础.另外,初级视皮层含有偏好亮度下降和高运动速度的神经元,这群神经元的活动有助于在光照差的环境中检测高速运动的低亮度物体.     

急性毁损猫的初级视区使高级视区细胞失去对视觉刺激的诱发反应(英文)

心理物理学研究提示,初级视区毁损后的视觉残留可能是通过外纹状皮层的神经网络重组介导的,但缺少支持这一假说的电生理实验证据。采用在体细胞外单细胞记录技术,该研究分别检测了初级视区(主要包括17和18区)急性毁损猫和正常对照猫的高级视区(包括19、20和21区)神经元对不同视觉刺激的反应性。结果显示,与对照相比,急性毁损初级视区使99.3%的高级视区神经元丧失对运动光栅刺激的诱发反应,93%的神经元丧失对闪光刺激的反应。该结果表明,急性毁损成年猫的初级视皮层可能会导致其绝大部分视觉能力丧失。在幼年期实施初级视皮层毁损后,成年猫出现的残留视觉可能主要是由于手术后皮层下神经核团与外纹状皮层之间的通路重组引起的。     

初级视皮层的亮度信息加工

亮度(luminance)是最基本的视觉信息.与其他视觉特征相比,由于视神经元对亮度刺激的反应较弱,并且许多神经元对均匀亮度无反应,对亮度信息编码的神经机制知之甚少.初级视皮层部分神经元对亮度的反应要慢于对比度反应,被认为是由边界对比度诱导的亮度知觉(brightness)的神经基础.我们的研究表明,初级视皮层许多神经元的亮度反应要快于对比度反应,并且这些神经元偏好低的空间频率、高的时间频率和高的运动速度,提示皮层下具有低空间频率和高运动速度通路的信息输入对产生初级视皮层神经元的亮度反应有贡献.已经知道初级视皮层神经元对空间频率反应的时间过程是从低空间频率到高空间频率,我们发现的早期亮度反应是对极低空间频率的反应,与这一时间过程是一致的,是这一从粗到细的视觉信息加工过程的第一步,揭示了处理最早的粗的视觉信息的神经基础.另外,初级视皮层含有偏好亮度下降和高运动速度的神经元,这群神经元的活动有助于在光照差的环境中检测高速运动的低亮度物体.     

一种新的视觉皮层简单细胞工作机制的模型

提出了一种基于独立元分析（ICA）的视觉皮层简单细胞工作机制的模型。用Gabor函数逼近对自然图像进行ICA而获得的基函数,揭示了ICA基函数与视觉皮层简单细胞感受野反应间存在内在的关系。并对水平条纹的图像进行ICA,模拟在特殊视觉环境下生长的幼年动物的视觉皮层发育过程,证实了1970年Blakemore和Cooper在幼猫上的实验结果。从而说明ICA可以模拟动物的视觉皮层简单细胞工作过程。     

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.     

Dimensional reduction of a V1 ring model with simple and complex cells

In this paper, we extend a framework for constructing low-dimensional dynamical systems models of mammalian primary visual cortex to a cortical network model that incorporates the full nonlinear effects of complex cells. The procedure consists of capturing the essential dynamics in a low-dimensional subspace using empirical methods, then recasting the equations in the reduced vector space. Previously, we considered visual cortical network models consisting of only simple cells with nearly linear responses to external stimuli. Here we show that fully nonlinear effects can be incorporated by examining the dimensional reduction of an idealized ring model of V1 with both simple and complex cells. We found it expedient to divide the subspace into four separate neuronal populations: excitatory simple, excitatory complex, inhibitory simple and inhibitory complex. In order to reproduce the fluctuation-driven dynamics in this reduced space, we incorporated (1) white noises with different intensities into individual neuronal populations, and (2) firing rate estimates to capture the probability of firing due to subthreshold fluctuations. With a more accurate, fitted connectivity, our modified dimensional reduced models can reproduce the firing rates, circular variances and modulation ratios observed in the original ring model.     

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。     

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.     

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.     

Learning receptive field properties of complex cells in V1

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.     

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.     

Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.     

Construction of complex receptive fields in cat primary visual cortex.

In primary visual cortex, neurons are classified into simple cells and complex cells based on their response properties. Although the role of these two cell types in vision is still unknown, an attractive hypothesis is that simple cells are necessary to construct complex receptive fields. This hierarchical model puts forward two main predictions. First, simple cells should connect monosynaptically to complex cells. Second, complex cells should become silent when simple cells are inactivated. We have recently provided evidence for the first prediction, and here we do the same for the second. In summary, our results suggest that the receptive fields of most layer 2+3 complex cells are generated by a mechanism that requires simple cell inputs.