Distributed processing and temporal codes in neuronal networks

The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the selective routing of signals across densely interconnected networks, the flexible and context dependent binding of neuronal groups into functionally coherent assemblies and the task and attention dependent integration of subsystems. In order to implement these mechanisms, it is proposed that neuronal responses should convey two orthogonal messages in parallel. They should indicate (1) the presence of the feature to which they are tuned and (2) with which other neurons (specific target cells or members of a coherent assembly) they are communicating. The first message is encoded in the discharge frequency of the neurons (rate code) and it is proposed that the second message is contained in the precise timing relationships between individual spikes of distributed neurons (temporal code). It is further proposed that these precise timing relations are established either by the timing of external events (stimulus locking) or by internal timing mechanisms. The latter are assumed to consist of an oscillatory modulation of neuronal responses in different frequency bands that cover a broad frequency range from <2 Hz (delta) to >40 Hz (gamma) and ripples. These oscillations limit the communication of cells to short temporal windows whereby the duration of these windows decreases with oscillation frequency. Thus, by varying the phase relationship between oscillating groups, networks of functionally cooperating neurons can be flexibly configurated within hard wired networks. Moreover, by synchronizing the spikes emitted by neuronal populations, the saliency of their responses can be enhanced due to the coincidence sensitivity of receiving neurons in very much the same way as can be achieved by increasing the discharge rate. Experimental evidence will be reviewed in support of the coexistence of rate and temporal codes. Evidence will also be provided that disturbances of temporal coding mechanisms are likely to be one of the pathophysiological mechanisms in schizophrenia. This article was part of LNCS 5286 (2008), Maria Marinaro, Silvia Scarpetta, Yoko Yamaguchi (eds.), “Dynamic Brain—from Neural Spikes to Behaviors, 12th International Summer School on Neural Networks Erice, Italy, December 2007 Revised Lectures” and summarized some of the putative functions of temporal codes resulting either from the timing of external events (feed forward/bottom up) or from internal timing mechanisms (top down). For comprehensive reviews of the theoretical prerequisites of synchronization in these processes see Yamaguchi and Shimizu (1994) and Shimizu et al. (1985).     

Deep architecture neural network-based real-time image processing for image-guided radiotherapy

IntroductionTo develop real-time image processing for image-guided radiotherapy, we evaluated several neural network models for use with different imaging modalities, including X-ray fluoroscopic image denoising.Methods & materialsSetup images of prostate cancer patients were acquired with two oblique X-ray fluoroscopic units. Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN). We changed the convolutional kernel size and number of convolutional layers for both networks, and the number of pooling and upsampling layers for rCAE. The ground-truth image was applied to the contrast-limited adaptive histogram equalization (CLAHE) method of image processing. Network models were trained to keep the quality of the output image close to that of the ground-truth image from the input image without image processing. For image denoising evaluation, noisy input images were used for the training.ResultsMore than 6 convolutional layers with convolutional kernels >5 × 5 improved image quality. However, this did not allow real-time imaging. After applying a pair of pooling and upsampling layers to both networks, rCAEs with >3 convolutions each and rCNNs with >12 convolutions with a pair of pooling and upsampling layers achieved real-time processing at 30 frames per second (fps) with acceptable image quality.ConclusionsUse of our suggested network achieved real-time image processing for contrast enhancement and image denoising by the use of a conventional modern personal computer.     

Sensory processing and the network mechanisms for reading neuronal population codes

In many neuronal systems, information appears to be represented in the activity of populations of neurons. Such neuronal population codes must also be read out, or interpreted, by downstream networks. Recent studies in both vertebrate and invertebrate systems have begun to elucidate some of the general mechanisms underlying these processes. Directed behaviors, that involve a directional response to a directional sensory input, have been a particularly useful context for these studies because, among other things, their input-output relationship is easily defined and experimentally controlled. We have recently shown that the neuronal network underlying a directed behavior in the medicinal leech utilizes a specific population coding scheme based on a neuronal population vector. A population vector of mechanosensory neuron activity correlates well with behavioral output and the connectivity of the downstream network is well suited for accurately reading out this population code. Accepted: 17 April 1999     

Temporal codes and sparse representations: A key to understanding rapid processing in the visual system

Where neural information processing is concerned, there is no debate about the fact that spikes are the basic currency for transmitting information between neurons. How the brain actually uses them to encode information remains more controversial. It is commonly assumed that neuronal firing rate is the key variable, but the speed with which images can be analysed by the visual system poses a major challenge for rate-based approaches. We will thus expose here the possibility that the brain makes use of the spatio-temporal structure of spike patterns to encode information. We then consider how such rapid selective neural responses can be generated rapidly through spike-timing-dependent plasticity (STDP) and how these selectivities can be used for visual representation and recognition. Finally, we show how temporal codes and sparse representations may very well arise one from another and explain some of the remarkable features of processing in the visual system.     

Quantification of polyacrylamide gel bands by digital image processing

A method of quantifying bands on polyacrylamide gels based on image processing of digitized photographic negatives is presented.     

An ensemble approach for healthcare application and diagnosis using natural language processing

Integration of healthcare records into a single application is still a challenging process There are additional issues when data becomes heterogeneous, and its application based on users does not appear to be the same. Hence, we propose an application called MEDSHARE which is a web-based application that integrates the data from various sources and helps the patient to access all their health records in a single point of source. Apart just from the collection of data, this portal enables the process of diagnosis using Natural language processing. The process is carried out by fuzzy logic ruleset which is generated by using NLP packages. The resulted information is given to the SVM classifier which helps in the prediction of diseases resulting in 89% of accuracy and standing the best compared to other classifiers. Finally, the observations resulted are sent to the front end application and the concerned user mobile through text message in their own native language for which translation package is been used.     

An approach to the architecture of Scutigera coleoptrata hemocyanin by electron microscopy and image processing

Summary— The 6 × 6meric hemocyanin of the centipede Scutigera coleoptrata, prepared by the single layer negative staining technique with uranyl acetate, has been investigated under the electron microscope. Isolated molecules were windowed from selected digitized micrographs. After alignment, they were submitted to correspondence analysis and hierarchical ascendant classification. Two main clusters of molecules were differentiated, and their average images were obtained. A 3D-model constructed from these images is proposed.     

A chaotic neural network mimicking an olfactory system and its application on image recognition

1 Introduction A biological neural system is complicated and ef-ficient. People have tried for years to simulate it to per-form complex signal processing functions. For example,the artificial neural network is a kind of model derivedfrom a biological neural system. Most artificial neuralnetworks simulate some important features such as thethreshold behaviour and plasticity of synapses. However,they are primary simulations and still much simpler incomparison with specific biological neural…     

Role of phospholipase D1 in neurite outgrowth of neural stem cells

Employing neural stem cells from the brain cortex of E12 rat embryos, we investigated the possible role of phospholipase D (PLD) in the synaptogenesis and neurite formation of neural cells during differentiation. Expression level of PLD1 increased during neuronal differentiation of the neural stem cells, resulting in increased PLD activity. Expression level of synapsin I, a marker of synaptogenesis, also increased as the differentiation of neural stem cells progressed. To figure out the effect of PLD on synapsin I expression, we treated the neural stem cells with phorbol myristate acetate (PMA) to stimulate PLD activity. Increased PLD activity induced by PMA treatment resulted in elevated synapsin I expression and neurite outgrowth during neuronal differentiation. To further confirm the role of PLD in neurite outgrowth, we transfected the dominant-negative form of rat PLD1 cDNA (DN-rPLD1) into neural stem cells to downregulate PLD activity. Overexpression of DN-rPLD1 showed the complete inhibition of neurite outgrowth of neural stem cells under differentiation condition. While transfection of DN-rPLD1 did not affect the synapsin I expression, overexpression of rPLD1 resulted in increased synapsin I expression of the neural cells. These results suggest that PLD1 plays a critical role in neurite outgrowth during differentiation of the neural stem cells. In conclusion, this is the first evidence to show that PLD1 acts as an important regulator of neurite outgrowth in neural stem cell by promoting neuronal differentiation via increase of synapsin I expression.     

A hybrid image processing system for X-ray images of an external fixator

In the field of orthopaedics, treatment of extremity deformities can be realised by means of external fixators. However, control of such biomedical system is very difficult. Some different mathematical models have been developed to improve quality of this service. Most of the parameters, which are used in these models, have been obtained from two orthogonal X-ray images: one from anteroposterior, AP, direction and the other from a lateral, L, direction. The quality of the results of this model is dependent on the accuracy of the input parameters. Measuring these parameters is a time-consuming issue, and the accuracy of the results is also low. To increase the quality of the measurement, the reference points should be chosen from the edges of the biomedical system, and it is important to find the edges without noise. To achieve this purpose, Sobel edge detector, binary large object analysis, thresholding and inverting are applied as image processing steps. The results are compared with manual measurement values which have been obtained earlier. The results show that semi-automatic measurement of the parameters is more accurate and faster than manual measurement. It shows that the efficiency of the fixator method has been improved.     

Hyperspectral imaging in medicine: image pre‐processing problems and solutions in Matlab

The paper presents problems and solutions related to hyperspectral image pre‐processing. New methods of preliminary image analysis are proposed. The paper shows problems occurring in Matlab when trying to analyse this type of images. Moreover, new methods are discussed which provide the source code in Matlab that can be used in practice without any licensing restrictions.

The proposed application and sample result of hyperspectral image analysis.     

A hybrid model for the neural representation of complex mental processing in the human brain

In the present conceptual review several theoretical and empirical sources of information were integrated, and a hybrid model of the neural representation of complex mental processing in the human brain was proposed. Based on empirical evidence for strategy-related and inter-individually different task-related brain activation networks, and further based on empirical evidence for a remarkable overlap of fronto-parietal activation networks across different complex mental processes, it was concluded by the author that there might be innate and modular organized neuro-developmental starting regions, for example, in intra-parietal, and both medial and middle frontal brain regions, from which the neural organization of different kinds of complex mental processes emerge differently during individually shaped learning histories. Thus, the here proposed model provides a hybrid of both massive modular and holistic concepts of idiosyncratic brain physiological elaboration of complex mental processing. It is further concluded that 3-D information, obtained by respective methodological approaches, are not appropriate to identify the non-linear spatio-temporal dynamics of complex mental process-related brain activity in a sufficient way. How different participating network parts communicate with each other seems to be an indispensable aspect, which has to be considered in particular to improve our understanding of the neural organization of complex cognition.     

An easy,inexpensive, and sensitive method for the quantification of chitin in insect peritrophic membrane by image processing

ABSTRACT

Chitin, poly (β-(1→4)-N-acetyl-d-glucosamine), is an important biopolymer for insects that is utilized as a major component of peritrophic membrane. The chitin content in peritrophic membrane is of expedient interest from a pest control perspective, although it is hard to quantify chitin. In this study, we establish a facile method for the quantification of chitin in peritrophic membrane by image processing. In this method, chitin was indirectly quantified using chitosan–I₃^? complex, which exhibited a specific red-purple color. A calibration curve using a chitosan solution showed good linearity in a concentration range of 0.05–0.5 μg/μL. We quantified the amount of chitin in peritrophic membrane of Spodoptera litura (Lepidoptera: Noctuidae) larvae using this method. Throughout the study, only common inexpensive regents and easily attainable apparatuses were employed. This method can be easily applied to the sensitive quantification of the amounts of chitin and chitosan in materials by wide range of researchers.

Abbreviations: LOD: limit of detection; LOQ: limit of quantification; ROI: region of interest; RSD: relative standard deviation.     

形态学图像处理方法在湿地破碎化格局中的应用

以三江平原沼泽湿地为研究单元,应用形态学图像处理技术在基于像元水平的二元沼泽湿地图上对沼泽湿地类型进行分类,并应用形态学模型和类型统计模型系统分析了三江平原1980-2000年间沼泽湿地景观破碎化的时空变化格局.结果表明,20年间,三江平原沼泽湿地面积由快速下降趋于缓慢下降.核心湿地面积急剧减小,由大面积分布逐步向三江平原东北部的别拉洪河和挠力河流域集中.孔隙湿地面积减小比例最大,目前处于消亡状态,由原来的沼泽湿地内部异质景观向同质景观过渡.核心和孔隙湿地大部分转化为斑块湿地,主要位于三江平原西北部、中部和南部,有逐步取代东北部核心湿地的趋势.边缘湿地面积比例年增长最快,且边缘像元宽度越来越宽,三江平原西部、南部和东部地区的核心湿地几乎全部由边缘湿地取代,易产生一定的边缘效应,导致种群间的竞争更加激烈.     

Use of negative stain and single-particle image processing to explore dynamic properties of flexible macromolecules

Flexible macromolecules pose special difficulties for structure determination by crystallography or NMR. Progress can be made by electron microscopy, but electron cryo-microscopy of unstained, hydrated specimens is limited to larger macromolecules because of the inherently low signal-to-noise ratio. For three-dimensional structure determination, the single particles must be invariant in structure. Here, we describe how we have used negative staining and single-particle image processing techniques to explore the structure and flexibility of single molecules of two motor proteins: myosin and dynein. Critical for the success of negative staining is a hydrophilic, thin carbon film, because it produces a low noise background around each molecule, and stabilises the molecule against damage by the stain. The strategy adopted for single-particle image processing exploits the flexibility available within the SPIDER software suite. We illustrate the benefits of successive rounds of image alignment and classification, and the use of whole molecule averages and movies to analyse and display both structure and flexibility within the dynein motor.     

昆虫图像分割方法及其应用

昆虫图像自动鉴定是一种快速鉴定昆虫的方法,图像分割则是其中关键步骤。通过搜集和整理国内外近年来针对昆虫图像的分割方法和研究,发现对昆虫图像分割的研究日趋增多。随着计算机图像技术的发展,昆虫图像分割方法吸收了许多图像分割领域中新兴的方法, 诸如采用水平集、边缘流以及结合形状、纹理、色彩等多种要素的智能分割(如JSEG方法)等。虽然大量的图像分割方法被引入到昆虫图像研究中,但是目前分割技术依然是阻碍昆虫图像广泛应用的关键。本文经过总结和分析,发现目前昆虫图像分割研究的往往在各自的测试集上有良好表现, 但是缺乏统一的评价标准, 因此很多方法在昆虫图像中应用难以推广。针对研究中的存在的这些问题,需建立良好的昆虫图像分割评价体系,本文建议通过建立统一的昆虫图像库以及对昆虫图像分割的评价方法深入研究,并且这些工作是当前昆虫图像分割研究亟待完善任务。     

Robust discriminant analysis and its application to identify protein coding regions of rice genes

Identification of protein coding regions is fundamentally a statistical pattern recognition problem. Discriminant analysis is a statistical technique for classifying a set of observations into predefined classes and it is useful to solve such problems. It is well known that outliers are present in virtually every data set in any application domain, and classical discriminant analysis methods (including linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)) do not work well if the data set has outliers. In order to overcome the difficulty, the robust statistical method is used in this paper. We choose four different coding characters as discriminant variables and an approving result is presented by the method of robust discriminant analysis.     

The processing of audio-visual speech: empirical and neural bases

In this selective review, I outline a number of ways in which seeing the talker affects auditory perception of speech, including, but not confined to, the McGurk effect. To date, studies suggest that all linguistic levels are susceptible to visual influence, and that two main modes of processing can be described: a complementary mode, whereby vision provides information more efficiently than hearing for some under-specified parts of the speech stream, and a correlated mode, whereby vision partially duplicates information about dynamic articulatory patterning.Cortical correlates of seen speech suggest that at the neurological as well as the perceptual level, auditory processing of speech is affected by vision, so that 'auditory speech regions' are activated by seen speech. The processing of natural speech, whether it is heard, seen or heard and seen, activates the perisylvian language regions (left>right). It is highly probable that activation occurs in a specific order. First, superior temporal, then inferior parietal and finally inferior frontal regions (left>right) are activated. There is some differentiation of the visual input stream to the core perisylvian language system, suggesting that complementary seen speech information makes special use of the visual ventral processing stream, while for correlated visual speech, the dorsal processing stream, which is sensitive to visual movement, may be relatively more involved.