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.     

猫前内侧上雪氏区视神经元对运动随机线条的反应

对于运动信息在脑内的加工,一种观点认为分两阶段进行,低级视皮层只对运动图形内部成分的取向进行调谐,高级视皮层整合低级视皮层的输入,对图形整体的运动方向敏感。用网格（plaid）作为刺激的实验表明,在较低级皮层区,细胞多表现为成分方向选择性（Component-motion Selectivity),即对刺激中的取向因素敏感：而较高视皮层的细胞多表现为整体方向选择性（Pattern-motion Selecitivity),对运动整体的方向敏感,从而支持运动信息加工的“两阶段”理论。实验中,用一系列运动随机线条刺激（random line patterns)。研究猫前内侧上雪氏区（Anteriormedial lateral suprasylvian area,AMLS)神经元的方向调谐特性。结果表明多数细胞为整体方向选择性,且随线长增加此类细胞比例下降,而成分方向选择性细胞的比例有所增加,呈现由整体方向选择性向中间类型（Unclassified),由中间类型向成分方向选择性变化的趋势,提示整体或成分方向选择性可能并非细胞的固有特性,而是可以随刺激取向因素的变化而改变的。     

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.     

S100蛋白在猫初级视皮层中分布及其年龄相关性变化

比较青年猫和老年猫初级视皮层(primary visual cortex)各层神经元密度,及S100蛋白在初级视皮层各层中的表达与分布,探讨其表达与分布的年龄相关性变化及意义.Nissl法显示初级视皮层各层神经元,免疫组织化学方法(SABC法)示S100蛋白免疫阳性(S100-IR)细胞.光镜下观察、拍照,计数初级视皮层各层中神经元密度和S100-IR细胞密度.S100-IR细胞在初级视皮层中分布呈现区域性特点,白质较灰质密集.与青年猫相比,老年猫初级视皮层神经元密度有下降,老年猫初级视皮层各层S100-IR细胞密度均有不同程度的显著增加(尤其是Ⅱ、Ⅲ、Ⅳ层),胞体较大,阳性较强.动物衰老过程中,初级视皮层存在着明显的星形胶质细胞反应性增生,这种增生可能对灰质层中神经元的丢失有补偿作用,并对维持老年个体初级视皮层形态结构和延缓老年动物初级视皮层功能衰退具有积极意义.     

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。     

金黄地鼠视皮层中乙酰胆碱(Ach)阳性神经元及纤维

本文通过色疫组织化学方法-PAP法,使用乙酰胆碱(Ach)抗体对金黄地鼠视皮层中的Ach进行定位Ach阳性神经元分布于视皮层的第Ⅱ—Ⅵ层,主要集中在Ⅱ、Ⅲ层.其细胞形态除大部分为非锥体神经元外,还发现有极少数锥体型神经元.这些神经元的数量和树突形态在视皮层17区和18区存在某些区域性差异.17区细胞比较密集,18区比较稀疏.17区第IA层细胞排列整齐,顶树突特别粗大,18区这种现象不太明显.皮层白质中也有少量的Ach阳性神经元.视皮层Ach阳性神经元的相对含量约为10%.视皮层中遍布外源性和内源性Ach阳性神经纤维,它们主要分布在第Ⅳ层.在视皮层微血管周围有大量Ach阳性神经元,说明对血管有舒张作用的Ach可能来源于血管内壁.     

视觉系统皮层下细胞的方位和方向敏感性

视觉方位、方向选择性曾被认为是高等哺乳动物视皮层细胞的特有功能。近年来大量的实验结果表明,视皮层下的外膝体神经元和视网膜神经节细胞都具一定程度的方位和方向敏感性,这些性质是遗传决定的,不受后天环境的影响。在外膝体内,已为视皮层细胞高度的方位、方向选择性和功能柱的形成做出了初步的分类与编组,提供了前级安排。这种皮层下的方位、方向敏感性细胞在发育过程中传递和加工了环境视觉信息,促进了视皮层更强的方位、方向选择性机制和方位功能柱的形成。外膝体在视觉信息平行处理通道的形成上起着分类集聚的重要作用。     

皮层神经元动作电位不应期和阈电位与神经元动作电位编码的关系

目的：测量和比较感觉运动皮层Ⅱ/Ⅲ层锥体神经元和中间神经元的内在特性并研究其与动作电位编码频率和精确性的关系。方法：采用全细胞电流钳记录模式,获得的数据输入pClamp和Origin进行处理分析。结果：与锥体神经元相比,中间神经元群集动作电位具有较低的阈电位水平和较短的不应期,从而中间神经元具有较高的动作电位编码频率和精确性。结论：皮层神经元动作电位的阈电位水平和不应期调控动作电位的编码频率和精确性。     

GABA神经元在金黄地鼠视觉中枢的分布

本文用免疫细胞化学技术研究了GABA在金黄地鼠视觉中枢的分布特征,同时用统计学方法作了定量分析,结果表明:GABA阳性神经元分布在整个视皮层和上丘中,呈不均匀分布,外膝体中GABA阳性神经元密度较低.视皮层中GABA阳性神经元密度为781mm~2,占视皮层细胞总数的19.7%,上丘中其密度为812/mm~2,占22.3%,视皮层Ⅰ层中GABA阳性神经元为52%,上丘表层(浅灰层及视觉层GABA阳性神经元为56%,GABA阳性神经元包括不同类型的细胞.在视皮层中可观察到GABA免疫疫应阳性的锥体细胞.     

Independent Component Analysis of Temporal Sequences Subject to Constraints by Lateral Geniculate Nucleus Inputs Yields All the Three Major Cell Types of the Primary Visual Cortex

Information maximization has long been suggested as the underlying coding strategy of the primary visual cortex (V1). Grouping image sequences into blocks has been shown by others to improve agreement between experiments and theory. We have studied the effect of temporal convolution on the formation of spatiotemporal filters—that is, the analogues of receptive fields—since this temporal feature is characteristic to the response function of lagged and nonlagged cells of the lateral geniculate nucleus. Concatenated input sequences were used to learn the linear transformation that maximizes the information transfer. Learning was accomplished by means of principal component analysis and independent component analysis. Properties of the emerging spatiotemporal filters closely resemble the three major types of V1 cells: simple cells with separable receptive field, simple cells with nonseparable receptive field, and complex cells.     

Slowness and sparseness lead to place, head-direction, and spatial-view cells

We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system []. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.     

Evidence for a generalized Laguerre transform of temporal events by the visual system

It is generally assumed that the early visual processing is constituted by a set of filters operating in parallel. In this respect the visual system performs a transform, generating a code of the characteristics of the input signal. Recently, it has been suggested that the coding of the spatial characteristics by the visual system can be described by a Hermite transform (Martens, 1990a, b). It was also suggested that a three-dimensional Hermite transform can be used to code spatiotemporal events. In contrast to this latter suggestion, we argue that the coding of temporal events takes the form of a generalized Laguerre transform. We review psychophysical evidence supporting this hypothesis.     

A model of the saccade-generating system that accounts for trajectory variations produced by competing visual stimuli

Variable saccade trajectories are produced in visual search paradigms in which multiple potential target stimuli are present. These variable trajectories provide a rich source of information that may lead to a deeper understanding of the basic control mechanisms of the saccadic system. We have used published behavioral observations and neural recordings in the superior colliculus (SC), gathered in monkeys performing visual search paradigms, to guide the construction of a new distributed model of the saccadic system. The new model can account for many of the variations in saccade trajectory produced by the appearance of multiple visual stimuli in a search paradigm. The model uses distributed feedback about current eye motion from the brainstem to the SC to reduce activity there at physiologically realistic rates during saccades. The long-range lateral inhibitory connections between SC cells used in previous models have been eliminated to match recent physiological evidence. The model features interactions between visually activated multiple populations of cells in the SC and distributed and topologically organized inhibitory input to the SC from the SNr to produce some of the types of variable saccadic trajectories, including slightly curved and averaging saccades, observed in visual search tasks. The distributed perisaccadic disinhibition of SC from the substantia nigra (SNr) is assumed to have broad spatial tuning. In order to produce the strongly curved saccades occasionally recorded in visual search, the existence of a parallel input to the saccadic burst generators in addition to that provided by the distributed input from the SC is required. The spatiotemporal form of this additional parallel input is computed based on the assumption that the input from the model SC is realistic. In accordance with other recent models, it is assumed that the parallel input comes from the cerebellum, but our model predicts that the parallel input is delayed during highly curved saccadic trajectories.     

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.     

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.     

Development of spatiotemporal receptive fields of simple cells: I. Model formulation

A model for the development of spatiotemporal receptive fields of simple cells in the visual cortex is proposed. The model is based on the 1990 hypothesis of Saul and Humphrey that the convergence of four types of input onto a cortical cell, viz. non-lagged ON and OFF inputs and lagged ON and OFF inputs, underlies the spatial and temporal structure of the receptive fields. It therefore explains both orientation and direction selectivity of simple cells. The response properties of the four types of input are described by the product of linear spatial and temporal response functions. Extending the 1994 model of one of the authors (K.D. Miller), we describe the development of spatiotemporal receptive fields as a Hebbian learning process taking into account not only spatial but also temporal correlations between the different inputs. We derive the correlation functions that drive the development both for the period before and after eye-opening and demonstrate how the joint development of orientation and direction selectivity can be understood in the framework of correlation-based learning. Our investigation is split into two parts that are presented in two papers. In the first, the model for the response properties and for the development of direction-selective receptive fields is presented. In the second paper we present simulation results that are compared with experimental data, and also provide a first analysis of our model. Received: 18 June 1997 / Accepted: 16 September 1997     

Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli

Encoding features of spatiotemporally varying stimuli is quite important for understanding the neural mechanisms of various sensory coding. Temporal coding can encode features of time-varying stimulus, and population coding with temporal coding is adequate for encoding spatiotemporal correlation of stimulus features into spatiotemporal activity of neurons. However, little is known about how spatiotemporal features of stimulus are encoded by spatiotemporal property of neural activity. To address this issue, we propose here a population coding with burst spikes, called here spatiotemporal burst (STB) coding. In STB coding, the temporal variation of stimuli is encoded by the precise onset timing of burst spike, and the spatiotemporal correlation of stimuli is emphasized by one specific aspect of burst firing, or spike packet followed by silent interval. To show concretely the role of STB coding, we study the electrosensory system of a weakly electric fish. Weakly electric fish must perceive the information about an object nearby by analyzing spatiotemporal modulations of electric field around it. On the basis of well-characterized circuitry, we constructed a neural network model of the electrosensory system. Here we show that STB coding encodes well the information of object distance and size by extracting the spatiotemporal correlation of the distorted electric field. The burst activity of electrosensory neurons is also affected by feedback signals through synaptic plasticity. We show that the control of burst activity caused by the synaptic plasticity leads to extracting the stimulus features depending on the stimulus context. Our results suggest that sensory systems use burst spikes as a unit of sensory coding in order to extract spatiotemporal features of stimuli from spatially distributed stimuli.     

Spatiotemporal firing patterns in the cerebellum

Neurons are generally considered to communicate information by increasing or decreasing their firing rate. However, in principle, they could in addition convey messages by using specific spatiotemporal patterns of spiking activities and silent intervals. Here, we review expanding lines of evidence that such spatiotemporal coding occurs in the cerebellum, and that the olivocerebellar system is optimally designed to generate and employ precise patterns of complex spikes and simple spikes during the acquisition and consolidation of motor skills. These spatiotemporal patterns may complement rate coding, thus enabling precise control of motor and cognitive processing at a high spatiotemporal resolution by fine-tuning sensorimotor integration and coordination.     

Reverse spikeology: predicting single spikes.

Neural models that simulate single spike trains can help us understand the basic principles of neural coding in vision. Keat et al. (2001) develop a hybrid model that combines spatiotemporal filtering with nonlinear spike generation. The model does a good job of predicting the responses of single retinal ganglion cells and thalamic relay neurons.