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).     

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 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.     

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.     

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.     

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.     

Binding of Cdc42 to phospholipase D1 is important in neurite outgrowth of neural stem cells

We previously demonstrated that phospholipase D (PLD) expression and PLD activity are upregulated during neuronal differentiation. In the present study, employing neural stem cells from the brain cortex of E14 rat embryos, we investigated the role of Rho family GTPases in PLD activation and in neurite outgrowth of neural stem cells during differentiation. As neuronal differentiation progressed, the expression levels of Cdc42 and RhoA increased. Furthermore, Cdc42 and PLD1 were mainly localized in neurite, whereas RhoA was localized in cytosol. Co-immunoprecipitation revealed that Cdc42 was bound to PLD1 during differentiation, whereas RhoA was associated with PLD1 during both proliferation and differentiation. These results indicate that the association between Cdc42 and PLD1 is related to neuronal differentiation. To examine the effect of Cdc42 on PLD activation and neurite outgrowth, we transfected dominant negative Cdc42 (Cdc42N17) and constitutively active Cdc42 (Cdc42V12) into neural stem cells, respectively. Overexpression of Cdc42N17 decreased both PLD activity and neurite outgrowth, whereas co-transfection with Cdc42N17 and PLD1 restored them. On the other hand, Cdc42V12 increased both PLD activity and neurite outgrowth, suggesting that active state of Cdc42 is important in upregulation of PLD activity which is responsible for the increase of neurite outgrowth.     

Circulating hormone adrenomedullin and its binding protein protect neural cells from hypoxia-induced apoptosis

Background

Brain ischemia is the underlying cause of neuron death during stroke and brain trauma. Neural cells exposed to ischemia can undergo apoptosis. Adrenomedullin (AM) in combination with its enhancing binding protein, AMBP-1, has been shown to reduce tissue damage in inflammation.

Methods

To evaluate a beneficial effect of AM/AMBP-1 administration in brain ischemia, we employed an in vitro model of neuronal hypoxia using differentiated human neuroblastoma SH-SY5Y cells.

Results

After exposure to 1% O₂ for 20 h, neural cells were injured with decreased ATP levels and increased LDH release. Pre-administration of AM/AMBP-1 significantly reduced hypoxia-induced cell injury. Moreover, AM/AMBP-1 treatment reduced the number of TUNEL-positive cells and activation of caspase-3, compared to cells exposed to hypoxia alone. AM/AMBP-1 prevented a reduction of cAMP levels and protein kinase A (PKA) activity in neural cells after hypoxia exposure. Correspondingly, an elevation of cAMP levels by forskolin protected neural cells from hypoxia-induced injury. Inhibition of PKA by KT5720 abolished the protective effect of AM/AMBP-1 on hypoxia-induced apoptosis.

Conclusions

AM/AMBP-1 elevates cAMP levels, followed by activating PKA, to protect neural cells from the injury caused by hypoxia.

General significance

AM/AMBP-1 may be used as therapeutic agents to prevent neuron damage from brain ischemia.     

Morphological characterisation of yeast colony growth on solid media using image processing

To rapidly determine the effect of environmental factors on yeast growth, a cell counting and colony sizing image analysis method was developed to characterise colony growth on solid media. A digitised microscopic image of the yeast was analysed using the Watershed algorithm for cell number determination and a morphological edge detection for colony size determination. The influence of temperature and physiological stress on yeast growth was then investigated over 12.5 h and data extracted by the image analysis method. © Rapid Science Ltd. 1998     

Counting pollen grains using readily available, free image processing and analysis software

Background and Aims

Although many methods exist for quantifying the number of pollen grains in a sample, there are few standard methods that are user-friendly, inexpensive and reliable. The present contribution describes a new method of counting pollen using readily available, free image processing and analysis software.

Methods

Pollen was collected from anthers of two species, Carduus acanthoides and C. nutans (Asteraceae), then illuminated on slides and digitally photographed through a stereomicroscope. Using ImageJ (NIH), these digital images were processed to remove noise and sharpen individual pollen grains, then analysed to obtain a reliable total count of the number of grains present in the image. A macro was developed to analyse multiple images together. To assess the accuracy and consistency of pollen counting by ImageJ analysis, counts were compared with those made by the human eye.

Key Results and Conclusions

Image analysis produced pollen counts in 60 s or less per image, considerably faster than counting with the human eye (5–68 min). In addition, counts produced with the ImageJ procedure were similar to those obtained by eye. Because count parameters are adjustable, this image analysis protocol may be used for many other plant species. Thus, the method provides a quick, inexpensive and reliable solution to counting pollen from digital images, not only reducing the chance of error but also substantially lowering labour requirements.     

Polycation-mediated enhancement of retroviral transduction efficiency depends on target cell types and pseudotyped Env proteins: implication for gene transfer into neural stem cells

Polycations such as polybrene (PB) are routinely used for most retroviral vector-mediated gene transfer studies because they can increase the infectivity of retroviruses. However, it was not systematically determined if addition of the polycation is an essential prerequisite for all retroviral transductions. To test this, we measured the effects of the polycation on transduction efficiency using various combinations of target cells and pseudotyped viral envelope (Env) proteins. Here, we show polycations do not always increase retroviral transduction efficiency and that their enhancing effect depends on both the type of target cells and Env proteins. The findings presented here also suggest that high transduction rates can be achieved in primary neural stem cells in vitro and in vivo by choosing an appropriate Env protein for pseudotyping without using polycations which are potentially toxic to primary cells and may change the intrinsic characteristics of cells.     

Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms

The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice for dealing with the massive amount of image data produced by Earth observation missions. In this paper, we develop a new morphological/neural algorithm for parallel classification of high-dimensional (hyperspectral) remotely sensed image data sets. The algorithm’s accuracy and parallel performance is tested in a variety of homogeneous and heterogeneous computing platforms, using two networks of workstations distributed among different locations, and also a massively parallel Beowulf cluster at NASA’s Goddard Space Flight Center in Maryland.