大鼠性周期内缰核神经元电活动的变化

目的：探讨缰核与生殖神经内分泌功能的关系。方法：采用电生理学方法记录细胞外放电。结果：①本实验观察到动情前期及动情间期大鼠缰核（ｈａｂｅｎｕｌａｒ ｎｕｃｌｅｕｓ,Ｈｂ） 的神经元对阴道、宫颈刺激（ｖａｇｉｎｏｃｅｒｖｉｃａｌｓｔｉｍｕｌａｔｉｏｎ,ＶＣ刺激） 发生两种反应。一类神经元对ＶＣ 刺激发生放电频率增快或持续时间延长的反应,即ＶＣＥ 神经元,另一类神经元则对ＶＣ刺激发生放电频率降低或暂时终止的反应,即ＶＣＩ神经元。动情前期大鼠的ＶＣＥ及ＶＣＩ神经元多呈周期性放电;②本实验还发现,大鼠Ｈｂ 中对ＶＣ刺激发生反应的性相关神经元中多数为非特异性反应型神经元,即对ＶＣ刺激和夹尾等其它刺激发生兴奋或抑制反应,少数为特异反应型神经元,即只对ＶＣ 刺激发生反应。结论：①提示这两种神经元可能与神经分泌活动有关; ②特异反应型神经元的存在表明,Ｈｂ 可以接受ＶＣ刺激的传入信息,并对其发生反应。ＶＣ刺激可能通过影响Ｈｂ 神经元的活动,从而影响生殖神经内分泌功能     

红核在肌梭传入抑制伤害性反应中的作用

本实验用玻璃微电极细胞外记录方法, 观察了刺激红核对皮肤强电刺激诱发的大鼠脊髓背角广动力范围（ｗｉｄｅ ｄｙｎａｍｉｃ ｒａｎｇｅ, ＷＤＲ） 神经元长潜伏期反应（Ｃ反应） 的作用, 及红核对琥珀胆碱（ｓｕｃｃｉｎｙｌｃｈｏｌｉｎｅ,ＳＣＨ） 诱发的肌梭传入抑制ＷＤＲ神经元Ｃ反应效应的影响。结果表明： 电刺激红核对ＷＤＲ 神经元Ｃ反应具有抑制作用, 此作用可被静注噻庚啶明显减弱。静脉注射ＳＣＨ 对ＷＤＲ神经元Ｃ反应有明显抑制作用, 损毁单侧红核后,ＳＣＨ 对ＷＤＲ神经元Ｃ反应的抑制效应明显减弱。结果提示,５ＨＴ参与红核的痛下行抑制作用, 在肌梭传入镇痛中红核起着一定的作用     

微电泳锂盐对大鼠尾壳核痛反应神经元电活动的影响

应用多管微电极离子微电泳技术,观察微电沪锂盐对大鼠尾壳核痛反应神经元电活动的影响,实验结果表明,痛相关神经元在ＣＰＮ头区内呈现均匀的分布,但痛兴奋神经元和痛抑制神经元的分布可能不均匀,其中ＰＩＮ主要分布在ＣＰＮ的头前区,ＰＥＮ则较集中于ＣＰＮ中心区。微电泳锂盐能抑制ＣＰＮ头区内ＰＥＮ的痛放活动,并使ＰＩＮ放电活动增加;这种ＰＥＮ与ＰＩＮ锂盐的反应型式与它们对啡的反应型式呈正相关,提示锂盐的镇痛作用     

视觉图形诱发的瞳孔反应

以图形变化刺激进行瞳孔反应研究,实验表明：（１）空间平均亮度守恒的光栅或棋盘格图形翻转能激发起瞳孔反应,为瞬态收缩波形,与ｐｕｐｉｌｌａｒｙ ｅｓｃａｐｅ相似;（２）光栅或棋盘格的空间频率的变化也能引起瞳孔反应,且反应幅度随空间频率差别增大而变大;（３）从均匀亮背景变化到棋盘格图形或者从棋盘格图形变化到黑暗背景,虽然不存在任何局部亮度增强,皆能引起瞳孔反应。实验结果明确证明了人的瞳孔反应系统除接收     

C－反应蛋白与模型受体的特异性结合

Ｃ－反应蛋白是动物体内一种典型的急性期反应蛋白。本文人工合成了两种可以与Ｃ－反应蛋白特异性结合的兼性分子作为Ｃ－反应蛋白的模型受体,以便进一步在脂单层膜表面上组装Ｃ－反应蛋白的二维晶体。作为第一步工作,本文研究了兼性分子的特性以及荧光光谱方法监测兼性分子与Ｃ－反应蛋白之间特异性相互作用。荧光光谱实验结果表明受体与Ｃ－反应的特异结合会引起荧光强度的下降。     

瞳孔控制系统非线性的频率特性与固有频率

本文用实验方法从时域和频域上揭示了瞳孔对光反应的非线性特性.在亮度以正弦变化的光刺激下,瞳孔反应波形呈同步倍频现象;描述函数的频率-振幅特性曲线呈多峰的锯齿形;具有1.2Hz左右的系统固有频率和极限环现象.     

C－反应蛋白与模型受体的特异性结合

Ｃ－反应蛋白是动物体内一种典型的急性期反应蛋白,本文人工合成了两种可以与Ｃ－反应蛋白特异性结合的兼性分子作为Ｃ－反应蛋白的模型受体,以便进一步在脂单层膜表面上组装Ｃ－反应蛋白的二维晶体。作为第一步工作。本文研究了兼性分子的特性以及荧光光谱方法监测兼性分子与Ｃ－反应蛋白之间特异性相互作用。荧光光谱实验结果表明受体与Ｃ－反应的特异结合会引起荧光强度的下降。     

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．     

Parvalbumin和Calbindin－D28k负疫反应神经元在人胎视网膜中的分布和发育

本实验采用免疫细胞化学方法观察１９例人胎视网膜内Ｐａｒｖａｌｂｕｍｉｎ（ＰＶ）和Ｃａｌｂｉ－ｄｉｎ－Ｄ２８ｋ（ＣａＢＰ）免疫反应神经元的分布和发育,对它们在人胎视网膜发育中的演变规律进行了研究。结果显示,ＰＶ和ＣａＢＰ免疫反应神经元属于视网膜水平细胞,无长实细胞和节细胞的不同亚群,ＣａＢＰ免疫反应神经元还可能分属于视细胞。ＰＶ和ＣａＢＰ免疫反应神经元的发育主要是在胚胎中期,胎１４周时它们已分别出现于视网膜内的不同部位,其各自的演变规律不同,至胚胎２７周时初步建立了各自的分布模式。在整个发育过程,颞测机网膜内ＰＶ免疫反应神经元的密度及反应强度均高于鼻侧视网膜内的ＰＶ免疫反应神经元,ＰＶ和ＣａＢＰ免疫反应神经元的发育与视网膜的组织发生也有着密切的联系。本文结果提示,ＰＶ和ＣａＢＰ可能在视网膜神经元的分化、迁移及突触形成中分别起着重要的作用。     

大鼠海马CA1区神经元在衰老过程中的形态学变化

对不同年龄组雄性ＳＤ大鼠海马ＣＡ１区锥体层神经元分别做光学和电镜观察与测定,结果表明ＣＡ１区锥体层单位面积内神经元数目随增龄下降达３３％（Ｐ＜０．００１）,同时伴有锥体层厚度的增加（Ｐ＜０．００１）;ＣＡ１区部分锥体神经元细胞器与胞突在老化过程中出现一系列形态学变化。本文对上述结果及其意义进行了讨论。     

Neural network mosaic model for pupillary responses to spatial stimuli

A neural network mosaic model was developed to investigate the spatial-temporal properties of the human pupillary control system. It was based on the double-layer neural network model developed by Cannon and Robinson and the pupillary dual-path model developed by Sun and Stark. The neural network portion of the model received its input from a sensor array and consisted of a retina-like two-dimensional neuronal layer. The dual-path portion of the model was composed of interconnections of the neurons that formed a mosaic of AC transient and DC sustained paths. The spatial aggregates of the AC and DC signals were input to the AC and DC summing neurons, respectively. Finally, the weighted sum of the aggregate AC and DC signals provided the output for driving the pupillary response. An important property of the model was that it could adaptively learn from training samples by adjustment of the weights. The neural network mosaic model showed excellent performance in simulating both the traditional pupillary phenomena and the new spatial stimulation findings such as responses to change in stimulus pattern and shift of light spot. Moreover, the model could also be used for the diagnosis of clinical deficits and image processing in machine vision. Received: 12 December 1997 / Accepted in revised form: 22 April 1998     

Binocular alternating pulse stimuli: Experimental and modeling studies of the pupil reflex to light

In our experiment, alternating pulse stimuli of both low and high intensity are used to study the pupil reflex to light. When applied monocularly, high intensity stimulation normally results in a sustained contraction; when alternated between the two eyes, it is found to produce small transient responses similar to those obtained with low intensity monocular stimulation. In order to study the mechanisms regulating these binocular responses, a model of the pupillary light reflex is constructed. It includes parallel AC and DC pathways for processing the light stimulus to produce motor signals to the iris muscles, nonlinear parameter control of pathway gains dependent upon internal operating level, binocular summation of DC pathway signals to produce that operating level, equal motor responses of both pupils, and iris neuromuscular delays and lags. The model is found to simulate the experimental data. It shows the binocular transient responses to be due to the canceling by summation of the symmetric DC pathway responses to alternating stimuli, thus allowing the AC pathway signals to become manifest. Therefore the dilatory portion of the transient responses is shown to be due to the lead-lag operator in the AC pathway and not to the off-dilatation elicited by removal of the light stimulus from the eye. Finally the results of our study are used to discuss the Marcus Gunn pupillary sign, a clinical test utilizing this binocular alternating pulse stimulation for detecting unilateral afferent defects.     

在双脉冲光刺激下瞳孔系统的采样控制特性

本文用实验揭示了瞳孔对光动态反应具有采样控制特性。实验中采用各种不同时间间隔的双脉冲光,以开环的方式(Maxwellian View)刺激瞳孔,当双脉冲之间间隔较长时,瞳孔反应相当于对双脉冲光的两次脉冲分别产生瞬态收缩;当双脉冲时间间隔短于0.6s 时,其反应就成了一次瞬态收缩,与单个光脉冲所引起的瞳孔反应一样。同—受试者的多次实验结果相同,不同受试者所得结果也基本一致。故瞳孔对脉冲刺激光引起反应后,必须至少约隔0.6s 才能对另一次脉冲光产生反应,这就说明了瞳孔动态反应具有离散的采样控制特性。实验还进一步证明,瞳孔系统的控制机制是双重模式的控制:不同的刺激条件下,瞳孔反应可呈现为瞬态反应(AC)或持续反应(DC),瞬态反应的 AC 通道为离散的采样控制,持续反应的 DC 通道为连续控制。     

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 cascading structure and training method for multilayer neural networks

A new structure and training method for multilayer neural networks is presented. The proposed method is based on cascade training of subnetworks and optimizing weights layer by layer. The training procedure is completed in two steps. First, a subnetwork, m inputs and n outputs as the style of training samples, is trained using the training samples. Secondly the outputs of the subnetwork is taken as the inputs and the outputs of the training sample as the desired outputs, another subnetwork with n inputs and n outputs is trained. Finally the two trained subnetworks are connected and a trained multilayer neural networks is created. The numerical simulation results based on both linear least squares back-propagation (LSB) and traditional back-propagation (BP) algorithm have demonstrated the efficiency of the proposed method.     

Level dependent signal flow in the light pupil reflex

Latency of pupillary responses to light stimuli are smaller for larger steps of light, and larger for smaller steps of light (Alpern 1954; Lowenstein et al. 1964; Lee et al. 1969; Terdiman et al. 1969; Cibis et al. 1977; and many others). Miller and Thompson (1978), however, reported negligible change in pupil cycle time (period of high gain instability oscillations) with increased mean brightness. Sandberg and Stark (1968) reportd a negligible reduction in phase lag of pupillary responses to sinusoidal light stimuli as the modulation coefficient (m) increased. To resolve the inconsistency between the well-documented dependence of latency upon brightness, and the apparent absence of level dependence in the phase characteristics (as reflected directly in the responses to sinusoidal stimuli and indirectly in pupil cycle time experiments) we measured: 1. Latency to step stimuli of light, 2. Phase of responses to sinusoidal light stimuli and 3. Period (pupil cycle time) of high gain instability oscillations. The dependence of pupillary latency upon stimulus level (both light and accommodation) and the interaction between accommodation and light responses were investigated. We show that most of the level dependence of light-pupil latency resides in the afferent path. In the companion papers, we demonstrate that: 1. Phase of pupillary response to sinusoidal light stimuli is reduced by increased mean light level, but is independent of pupil size and accommodative stimulus level; and 2. The period of high gain oscillations is shown to decrease with increased mean light level. Taken together, these results imply the existence of a Level Dependent Signal Flow (LDSF) operator that resides in the light-pupil pathway, but not in the accommodation-pupil pathway. We propose a systems model of this operator in which the neural signals controlling pupil size are treated as waves whose phase velocity increases in response to brighter stimuli, and decreases in response to dimmer stimuli. When parameters of the model are adjusted to fit measured pupillary latency over a range of light levels, the model exhibits reduced phase lag in response to increased mean light level in the sinusoidal paradigm, and it exhibits reduced pupil cycle time in the high-gain oscillation paradigm. The model exhibits saturation of the LDSF effect in all paradigms at high light levels, as do experimental results. It simulates directional asymmetry of pupillary response to positive and negative steps of light, with constriction more rapid than dilatation. Finally, it simulates tonic pupillary constriction in response to modulation of a light simulus without changing average light level (Varju 1964; Troelstra 1968). All of these stimulated results are in accord with experimental observation.     

Nervöse Wechselwirkung in der pupillomotorischen Bahn des Menschen

Summary Comparison of the human pupillary responses to monocular and simultaneous binocular stimuli indicates that the signals evoked in both eyes by binocular stimulation first inhibit each other and then combine by addition. In this paper several possible inhibitory mechanisms are considered and a functional model is proposed which involves a shunting type non-recurrent lateral inhibition. Although the site of inhibitory interaction is not specified by the model, certain assumptions are made regarding the succession of neural events along the pupillomotor pathway. The postulated succession of transmitting stages is: nonlinear transformation, first order lowpass filter with time constant characteristic for the pupillary response, lateral inhibition, addition and second order lowpass filter with the same time constant as before. Besides predicting the experimental data this functional model resolves certain contradictions in the conclusions of different autors regarding the succession of nonlinear transformation and signal combination in the human pupillary system.     

Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of surface electromyographic (EMG) signals from the semitendinosus and vastus medialis muscles. The inputs consisted of flexion and extension angles measured at the hip and knee of the ipsilateral leg, their first and second derivatives, and bilateral foot contact information. The training set consisted of data from six trials, at two different speeds. The testing set consisted of data from two additional trials (one at each speed), which were not in the training set. It was possible to reconstruct the muscular activation at both speeds using both techniques. Timing of the reconstructed signals was accurate. The integrated value of the activation bursts was less accurate. The neural network gave a continuous output, whereas the rule-based inductive learning rule tree gave a quantised activation level. The advantage of rule-based inductive learning was that the rules used were both explicit and comprehensible, whilst the rules used by the neural network were implicit within its structure and not easily comprehended. The neural network was able to reconstruct the activation patterns of both muscles from one network, whereas two separate rule sets were needed for the rule-based technique. It is concluded that machine learning techniques, in comparison to explicit inverse muscular skeletal models, show good promise in modelling nearly cyclic movements such as locomotion at varying walking speeds. However, they do not provide insight into the biomechanics of the system, because they are not based on the biomechanical structure of the system.     

A hierarchical neural network model for associative memory

A hierarchical neural network model with feedback interconnections, which has the function of associative memory and the ability to recognize patterns, is proposed. The model consists of a hierarchical multi-layered network to which efferent connections are added, so as to make positive feedback loops in pairs with afferent connections. The cell-layer at the initial stage of the network is the input layer which receives the stimulus input and at the same time works as an output layer for associative recall. The deepest layer is the output layer for pattern-recognition. Pattern-recognition is performed hierarchically by integrating information by converging afferent paths in the network. For the purpose of associative recall, the integrated information is again distributed to lower-order cells by diverging efferent paths. These two operations progress simultaneously in the network. If a fragment of a training pattern is presented to the network which has completed its self-organization, the entire pattern will gradually be recalled in the initial layer. If a stimulus consisting of a number of training patterns superposed is presented, one pattern gradually becomes predominant in the recalled output after competition between the patterns, and the others disappear. At about the same time when the recalled pattern reaches a steady state in he initial layer, in the deepest layer of the network, a response is elicited from the cell corresponding to the category of the finally-recalled pattern. Once a steady state has been reached, the response of the network is automatically extinguished by inhibitory signals from a steadiness-detecting cell. If the same stimulus is still presented after inhibition, a response for another pattern, formerly suppressed, will now appear, because the cells of the network have adaptation characteristics which makes the same response unlikely to recur. Since inhibition occurs repeatedly, the superposed input patterns are recalled one by one in turn.     

Dynamic output feedback stabilization for nonlinear systems based on standard neural network models

A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.