视网膜神经节细胞空间传输特性的模拟

以新近提出的同心圆感受野模型为基础。从感受野大工上周区内各亚区之间的抑制性相互作用入手,对视网膜神经节细胞的各种空间传输特性进行了模拟,通过改变外周亚区间抑制性相互作用的敏感度和有效范围,可逼真地模拟神经节细胞的各种不同的面积反应函数,用该模型来处不同空间频率的正弦光栅时,它既能很好地传递图像的高频成份,又可十分有效地提升被感受野中心／外周拮抗机制所衰减了的低频信息,此外,由于该模型引进了外周亚区     

某些脊椎动物视网膜感受野的数学模型及其推广

根据视网膜感受野的组织学和电生理资料,我们提出视网膜感受野的数学模型,其一维离散的情况的表达式是:g=[W·[K·A·R]_α]_β其中:A是输入向量,R是感受细胞的传递特性,K是第一层细胞与第二层细胞之间的联系矩阵,W是第二层细胞与神经节细胞之间的联系向量,α和β分别代表第二层细胞和节细胞的阈值。当K取某些特定值时,可分别模拟on-RF,off-RF,on-off-RF,在用小光点刺激下感受野的性质来检验模型,在感受野的一些空间特性方面定性上有较好的符合。本文讨论了构造特殊功能感受野的模型的一些方法。最后,把模型推广到二维连续的形式:并对其中的传递密度函数k(x,y,λ,θ),作了一些讨论。     

某些脊椎动物视网膜感受野的数学模型及其推广

根据视网膜感受野的组织学和电生理资料,我们提出视网膜感受野的数学模型,其一维离散的情况的表达式是:g=[W·[K·A·R]_a]_β其中:A是输入向量,R是感受细胞的传递特性,K是第一层细胞与第二层细胞之间的联系矩阵,W是第二层细胞与神经节细胞之间的联系向量,α和β分别代表第二层细胞和节细胞的阈值。当K取某些特定值时,可分别模拟on-RF,off-RF,on-off-RF,在用小光点刺激下感受野的性质来检验模型,在感受野的一些空间特性方面定性上有较好的符合。本文讨论了构造特殊功能感受野的模型的一些方法。最后,把模型推广到二维连续的形式:■并对其中的传递密度函数k(x,y,λ,θ),作了一些讨论。     

用数量场描述方向敏感感受野

1965年Barlow ＆ Levick及我国科技工作者分别独立提出了原理大致相同的方向检测感受野模型,但其数学模型却一直未见有报道.本文用数量场方法给出了其数学模型,这是一个Gabor函数模型,并据此解释了方向检测感受野的方向调谐曲线以及方向检测通道数量问题.     

线画图形质地辨认的一个神经网络模型

本文提出了一个能辨认线画质地的神经网络模型,并在IBM-PC机上对它进行了成功的模拟.该模型可与真实的视觉系统建立起很好的对应关系,因此可以认为它是对视觉系统质地辨认过程的一个机器再现.另外,本文推广了原广义Gabor函数模型,得到了一个适合于描写网络层次视觉功能的单细胞感受野的数学描述.     

用菌紫质Langmuir—Blodgett膜模拟视网膜神经节细胞感受野的DOG滤波等特性

用菌紫质膜以一维形式模拟了视网膜神经节细胞的ＯＮ－中心型感受野。实验表明菌紫质ＬＢ膜具有ＯＮ型和ＯＦＦ型微分响应特性,对运动狭缝,所模拟的人工视网膜感受野的周边区和中心区都具有类高斯函数形式的滤波特性,整个人工视网膜感受野具有与高等动物视网膜相似的ＤＯＧ滤波运算功能。     

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。     

视觉神经元感受野的数学模型及其空间频谱响应特性研究

本文在分析了现有视觉信息处理神经元感受野的数学模型的不足的基础上,提出了视觉通路各向同性和各向异性神经元感受野的数学描述,并考察其在空间频谱域的响应及其与感受野模型空间分布参数的关系.     

视网膜神经节细胞感受野的一种新模型：Ⅱ.神经节细胞方位选 …

在前文建立的二维视网膜神经节细胞含大周迷感受野模型基础上,结合生理实验模拟了神经节细胞的方位选择性特性。文中采用椭圆感受野的观点解释了方位选择性的成因。并通过中心区以外区域对中心区方位选择怀的复杂调制组合,展示了感受野不同亚地方位选择性的影响作用;指出方位选择性的成因是感受野椭圆亚单元的存在,感受野复杂的方位选择性是由于中心和周边在不同刺激条件下竞争的不同结果造成的;同时指出对椭圆感受野,倍频反应     

用菌紫质Langmuir—Blodgett膜模拟视网膜神经节细胞感受野的DOG滤波等特性

用菌紫质ＬＢ（Ｌａｎｇｍｕｉｒ—Ｂｌｏｄｇｅｔｔ）膜以一维形式模拟了视网膜神经节细胞的ＯＮ－中心型感受野。实验表明菌紫质ＬＢ膜具有ＯＮ型和ＯＦＦ型微分响应特性。对运动狭缝,所模拟的人工视网膜感受野的周边区和中心区都具有类高斯函数形式的滤波特性,整个人工视网膜感受野具有与高等动物视网膜相似的ＤＯＧ（ＤｉｆｆｅｒｅｎｃｅｏｆＧａｕｓｓｉａｎｓ）滤波运算功能。     

Modeling neuronal dynamic coding in primary visual cortex

The visual system can be considered as a multi-layered and dynamic image processing system. According to experimental evidence, the receptive field (RF) organization is characterized by spatio-temporal properties. The modified extended Gabor (MEG) function model was proposed to describe the main spatio-temporal properties of RF at different levels of visual pathway. Based on the MEG model, a three-layered dynamic coding model was constructed for a complex cell. The responses of the complex cell depend on synaptic events from a simple cell assembly within a time window. The membrane potential evolution equation was applied to the analysis of the length of a time window. The simulation results demonstrated that a complex cell plays as a coincidence detector in encoding synaptic events within the time window.     

Spatiotemporal organization of simple-cell receptive fields in area 18 of cat’s cortex

Spatiotemporal structures of receptive-fields (RF) have been studied for simple cells in area 18 of eat by measuring the temporal transfer function (TTF) over different locations (subregions) within the RF. The temporal characteristics of different subregions differed from each other in the absolute phase shift (APS) to visual stimuli. Two types of relationships can be seen: (i)The APS varied continuously from one subregion to the next: (ii) A 180°-phase jump was seen as the stimulus position changed somewhere within the receptive field. Spatiotemporal receptive field profiles have been determined by applying reverse Fourier analysis to responses in the frequency domain. For the continuous type, spatial and temporal characteristics cannot be dissociated (space time inseparable) and the spatiotemporal structure is oriented. On the contrary, the spatial and temporal characteristics for the jumping type can be dissociated (space-time separable) and the structure is not oriented in the space-time plane. Based on the APSs measured at different subregions, the optimal direction of motion and optimal spatial frequency of neurons can be predicted.     

A linear model for symmetric receptive fields: implications for classification tests with flashed and moving images

The purpose of this study was to explore the effects of spatial and temporal properties on the expected responses of visual neurons that have linear receptive fields (RFs), particularly those having a mirror symmetric distribution of spatial subregions. Receptive fields that are symmetric in at least one spatial dimension occur in neurons of the retina, the lateral geniculate nucleus (LGN), and the visual cortex of mammals. Responses to flashing bars, moving bars, and moving edges were studied for different configurations of an analog RF model in which spatial and temporal aspects were varied independently. Responses of the model at intermediate stimulus speeds were found to agree with responses in the literature for X and Y units of the LGN and often for simple units of the visual cortex. In particular, having separated regions of response to light and dark edges, an identifying property of simple cells, was found to be a linear consequence of RF regions responding inversely to stimuli of opposite polarity. Model differences from responses of cortical complex units show that a linear model cannot mimic their responses, and imply that complex units employ major nonlinearities in coding image polarity (light vs dark), which signifies a nonlinearity in coding intensity. Because sudden flux changes inherent in flashing bars test mainly temporal RF properties, and slowly moving edges test mainly spatial properties, these two tests form a useful minimal set with which to describe and classify RFs. The usefulness of this set derives both from its sensitivity to spatial and temporal variables, and from the correlation between the linearity of a cell's processing of stimulus intensity and its RF classification.     

The Gaussian derivative model for spatial-temporal vision: I. Cortical model

How do we see the motion of objects as well as their shapes? The Gaussian Derivative (GD) spatial model is extended to time to help answer this question. The GD spatio-temporal model requires only two numbers to describe the complete three-dimensional space-time shapes of individual receptive fields in primate visual cortex. These two numbers are the derivative numbers along the respective spatial and temporal principal axes of a given receptive field. Nine transformation parameters allow for a standard geometric association of these intrinsic axes with the extrinsic environment. The GD spatio-temporal model describes in one framework the following properties of primate simple cell fields: motion properties, number of lobes in space-time, spatial orientation. location, and size. A discrete difference-of-offset-Gaussians (DOOG) model provides a plausible physiological mechanism to form GD-like model fields in both space and time. The GD model hypothesizes that receptive fields at the first stage of processing in the visual cortex approximate 'derivative analyzers' that estimate local spatial and temporal derivatives of the intensity profile in the visual environment. The receptive fields as modeled provide operators that can allow later stages of processing in either a biological or machine vision system to estimate the motion as well as the shapes of objects in the environment.     

Spatiotemporal organization of simple-cell receptive fields in area 18 of cat's cortex

Spatiotemporal structures of receptive-fields (RF) have been studied for simple cells in area 18 of eat by measuring the temporal transfer function (TTF) over different locations (subregions) within the RF. The temporal characteristics of different subregions differed from each other in the absolute phase shift (APS) to visual stimuli. Two types of relationships can be seen: (i)The APS varied continuously from one subregion to the next: (ii) A 180°-phase jump was seen as the stimulus position changed somewhere within the receptive field. Spatiotemporal receptive field profiles have been determined by applying reverse Fourier analysis to responses in the frequency domain. For the continuous type, spatial and temporal characteristics cannot be dissociated (space time inseparable) and the spatiotemporal structure is oriented. On the contrary, the spatial and temporal characteristics for the jumping type can be dissociated (space-time separable) and the structure is not oriented in the space-time plane. Based on the APSs measured at different subregions, the optimal direction of motion and optimal spatial frequency of neurons can be predicted. Project supported by the National Natural Science Foundation of China (Grant Nos. 39570206, 39330110) and the Laboratory of Visual Information Processing, Chinese Academy of Sciences.     

Spatial and temporal coding in single neurons

Convergence between cells which differ in both spatial and temporal properties create higher order neurons with response properties that are distinctly different from those of the input neurons. The spatial properties of target neurons are not necessarily cosinetuned. In addition, unlike the independence between spatial and temporal properties in cosine-tuned afferent neurons, higher-order target cells generally exhibit a dependence of temporal dynamics on spatial properties. The response properties of target neurons receiving spatio-temporal convergence (STC) from tonic and phasic-tonic or phasic afferents is investigated here by considering a general case where the dynamic input is represented by a fractional, leaky, derivative transfer function. It is shown that, at frequencies below the corner frequency of the dynamic input, the temporal properties of target neurons can be described by leaky differentiators having time constants that are a function of spatial direction. Thus, STC target neurons exhibit tonic temporal response properties during stimulation along some spatial directions (having small time constants) and phasic properties along other directions (having large time constants). Specifically, target neurons encode the complete derivative of the stimulus along certain spatial directions. Thus, STC acts as a directionally specific high-pass filter and produces complete derivatives from fractional, leaky derivative afferent signals. In addition, spatio-temporal transformations can generate novel temporal dynamics in the central nervous system. These observations suggest that spatio-temporal computations might constitute an alternative to parallel, independent spatial and temporal channels.     

Resolving spatio-temporal uncertainty in rare resource acquisition: smell the shell

Complex environments present substantial spatio-temporal uncertainty in where and when rare ecological resources become available. How animals navigate this uncertainty to turn the seemingly unpredictable into the predictable is a fundamental question in evolutionary ecology. Here we use subtidal hermit crabs (Pagurus acadianus) as a model system to experimentally test in the field how animals resolve spatio-temporal uncertainty in resource availability. Quadrat sampling within the subtidal zone revealed that hermit crabs face an extreme ecological challenge, based on the rarity of empty shells across space and time. We show how this spatio-temporal uncertainty is ultimately resolved using long-distance chemical cues, which are associated with non-destructive shell predation on living gastropods, the original source of shells. By experimentally releasing cues that simulated the chemical by-products of predation, we reveal that certain flesh cues provide fine-grained information about the precise spatial and temporal window of new shell availability. These cues were most attractive to individuals with the greatest existing resource needs, and in the absence of this information individuals were highly constrained in their ability to discover newly available resources. Broadly, these experiments reveal that exploiting simple cues from heterospecific predators can provide a solution to the general ecological challenge of finding resources that are rare in space and time.     

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.     

Influence of activity in a heterogeneous environment on the dynamics of fish growth: an individual-based model of roacl

A spatio-temporal individual-based model (IBM), including bioenergetic principles, is used to investigate how energy costs coupled to activity represent a variable fraction in a heterogeneous environment and how this is related to another energetic criterion, the potential individual growth, under the conditions of a structured environment. With this approach, it is possible to connect simulated spatio-temporal activity patterns with the energetic needs required for these activities. By using simple foraging rules and a modified random walk model it is possible to reproduce spatial distributions and length frequency distributions. The simulated spatial distribution of roach Rutilus rutilus in Lake Belau, Germany, results in a mean weekly activity multiplier [(standard metabolic rate + activity costs) (standard metabolic rate)⁻¹] of 1–9 with deviations >100% during a simulated year. These deviations are of key importance to differences in the growth rate of individual simulated roach.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.