Bottom�Cup attention: pulsed PCA transform and pulsed cosine transform

In this paper we propose a computational model of bottom–up visual attention based on a pulsed principal component analysis (PCA) transform, which simply exploits the signs of the PCA coefficients to generate spatial and motional saliency. We further extend the pulsed PCA transform to a pulsed cosine transform that is not only data-independent but also very fast in computation. The proposed model has the following biological plausibilities. First, the PCA projection vectors in the model can be obtained by using the Hebbian rule in neural networks. Second, the outputs of the pulsed PCA transform, which are inherently binary, simulate the neuronal pulses in the human brain. Third, like many Fourier transform-based approaches, our model also accomplishes the cortical center-surround suppression in frequency domain. Experimental results on psychophysical patterns and natural images show that the proposed model is more effective in saliency detection and predict human eye fixations better than the state-of-the-art attention models.     

Saliency computation via whitened frequency band selection

Many saliency computational models have been proposed to simulate bottom-up visual attention mechanism of human visual system. However, most of them only deal with certain kinds of images or aim at specific applications. In fact, human beings have the ability to correctly select attentive focuses of objects with arbitrary sizes within any scenes. This paper proposes a new bottom-up computational model from the perspective of frequency domain based on the biological discovery of non-Classical Receptive Field (nCRF) in the retina. A saliency map can be obtained according to the idea of Extended Classical Receptive Field. The model is composed of three major steps: firstly decompose the input image into several feature maps representing different frequency bands that cover the whole frequency domain by utilizing Gabor wavelet. Secondly, whiten the feature maps to highlight the embedded saliency information. Thirdly, select some optimal maps, simulating the response of receptive field especially nCRF, to generate the saliency map. Experimental results show that the proposed algorithm is able to work with stable effect and outstanding performance in a variety of situations as human beings do and is adaptive to both psychological patterns and natural images. Beyond that, biological plausibility of nCRF and Gabor wavelet transform make this approach reliable.     

A self-organising neural network model of image velocity encoding

A self-organising neural network has been developed which maps the image velocities of rigid objects, moving in the fronto-parallel plane, topologically over a neural layer. The input is information in the Fourier domain about the spatial components of the image. The computation performed by the network may be viewed as a neural instantiation of the Intersection of Constraints solution to the aperture problem. The model has biological plausibility in that the connectivity develops simply as a result of exposure to inputs derived from rigid translation of textures and its overall organisation is consistent with psychophysical evidence.     

Control Analysis for Autonomously Oscillating Biochemical Networks

It has hitherto not been possible to analyze the control of oscillatory dynamic cellular processes in other than qualitative ways. The control coefficients, used in metabolic control analyses of steady states, cannot be applied directly to dynamic systems. We here illustrate a way out of this limitation that uses Fourier transforms to convert the time domain into the stationary frequency domain, and then analyses the control of limit cycle oscillations. In addition to the already known summation theorems for frequency and amplitude, we reveal summation theorems that apply to the control of average value, waveform, and phase differences of the oscillations. The approach is made fully operational in an analysis of yeast glycolytic oscillations. It follows an experimental approach, sampling from the model output and using discrete Fourier transforms of this data set. It quantifies the control of various aspects of the oscillations by the external glucose concentration and by various internal molecular processes. We show that the control of various oscillatory properties is distributed over the system enzymes in ways that differ among those properties. The models that are described in this paper can be accessed on http://jjj.biochem.sun.ac.za.     

Listen to Genes: Dealing with Microarray Data in the Frequency Domain

Background

We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes.

Methodology

Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail.

Conclusions

We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers.     

Regional Principal Color Based Saliency Detection

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms.     

Inferring phylogenetic networks by the maximum parsimony criterion: a case study

Horizontal gene transfer (HGT) may result in genes whose evolutionary histories disagree with each other, as well as with the species tree. In this case, reconciling the species and gene trees results in a network of relationships, known as the "phylogenetic network" of the set of species. A phylogenetic network that incorporates HGT consists of an underlying species tree that captures vertical inheritance and a set of edges which model the "horizontal" transfer of genetic material. In a series of papers, Nakhleh and colleagues have recently formulated a maximum parsimony (MP) criterion for phylogenetic networks, provided an array of computationally efficient algorithms and heuristics for computing it, and demonstrated its plausibility on simulated data. In this article, we study the performance and robustness of this criterion on biological data. Our findings indicate that MP is very promising when its application is extended to the domain of phylogenetic network reconstruction and HGT detection. In all cases we investigated, the MP criterion detected the correct number of HGT events required to map the evolutionary history of a gene data set onto the species phylogeny. Furthermore, our results indicate that the criterion is robust with respect to both incomplete taxon sampling and the use of different site substitution matrices. Finally, our results show that the MP criterion is very promising in detecting HGT in chimeric genes, whose evolutionary histories are a mix of vertical and horizontal evolution. Besides the performance analysis of MP, our findings offer new insights into the evolution of 4 biological data sets and new possible explanations of HGT scenarios in their evolutionary history.     

A robust orthogonal algorithm for system identification and time-series analysis

We describe and illustrate methods for obtaining a parsimonious sinusoidal series representation or model of biological time-series data. The methods are also used to identify nonlinear systems with unknown structure. A key aspect is a rapid search for significant terms to include in the model for the system or the time-series. For example, the methods use fast and robust orthogonal searches for significant frequencies in the time-series, and differ from conventional Fourier series analysis in several important respects. In particular, the frequencies in our resulting sinusoidal series need not be commensurate, nor integral multiples of the fundamental frequency corresponding to the record length. Freed of these restrictions, the methods produce a more economical sinusoidal series representation (than a Fourier series), finding the most significant frequencies first, and automatically determine model order. The methods are also capable of higher resolution than a conventional Fourier series analysis. In addition, the methods can cope with unequally-spaced or missing data, and are applicable to time-series corrupted by noise. Fially, we compare one of our methods with a wellknown technique for resolving sinusoidal signals in noise using published data for the test time-series.     

Mechanisms for allocating auditory attention: an auditory saliency map

Our nervous system is confronted with a barrage of sensory stimuli, but neural resources are limited and not all stimuli can be processed to the same extent. Mechanisms exist to bias attention toward the particularly salient events, thereby providing a weighted representation of our environment. Our understanding of these mechanisms is still limited, but theoretical models can replicate such a weighting of sensory inputs and provide a basis for understanding the underlying principles. Here, we describe such a model for the auditory system-an auditory saliency map. We experimentally validate the model on natural acoustical scenarios, demonstrating that it reproduces human judgments of auditory saliency and predicts the detectability of salient sounds embedded in noisy backgrounds. In addition, it also predicts the natural orienting behavior of naive macaque monkeys to the same salient stimuli. The structure of the suggested model is identical to that of successfully used visual saliency maps. Hence, we conclude that saliency is determined either by implementing similar mechanisms in different unisensory pathways or by the same mechanism in multisensory areas. In any case, our results demonstrate that different primate sensory systems rely on common principles for extracting relevant sensory events.     

Processing of ND NMR spectra sampled in polar coordinates: a simple Fourier transform instead of a reconstruction

In order to reduce the acquisition time of multidimensional NMR spectra of biological macromolecules, projected spectra (or in other words, spectra sampled in polar coordinates) can be used. Their standard processing involves a regular FFT of the projections followed by a reconstruction, i.e. a non-linear process. In this communication, we show that a 2D discrete Fourier transform can be implemented in polar coordinates to obtain directly a frequency domain spectrum. Aliasing due to local violations of the Nyquist sampling theorem gives rise to base line ridges but the peak line-shapes are not distorted as in most reconstruction methods. The sampling scheme is not linear and the data points in the time domain should thus be weighted accordingly in the polar FT; however, artifacts can be reduced by additional data weighting of the undersampled regions. This processing does not require any parameter tuning and is straightforward to use. The algorithm written for polar sampling can be adapted to any sampling scheme and will permit to investigate better compromises in terms of experimental time and lack of artifacts.     

A biologically plausible model of early visual motion processing II: Psychophysical application

We test the model of early visual processing introduced in the companion paper by simulating a range of psychophysical phenomena. We present new data concerning our ability to discriminate the speed of drifting gratings when spatiotemporally apertured in a variety of ways. We shall investigate the role played by the aperture in modifying the grating's behaviour from its idealisation as a pure Fourier component and show that this is not negligible. Other phenomena which we simulate and explain relate to the way perceived velocity is influenced by contrast and spatial frequency. Many of our explanations are couched in terms of the relative number of cells occurring within each locale of the Fourier domain. This use of the cell density map is a unifying concept and avoids the necessity for a range of separate mechanisms. We argue that a neurophysiologically detailed model is necessary in order to explain psychophysical data (Weber fractions) which vary over less than an order of magnitude, and small deviations from veridical encoding of velocity.     

Response to temporal parameter fluctuations in biochemical networks

Metabolic response coefficients describe how variables in metabolic systems, like steady state concentrations, respond to small changes of kinetic parameters. To extend this concept to temporal parameter fluctuations, we define spectral response coefficients that relate Fourier components of concentrations and fluxes to Fourier components of the underlying parameters. It is also straightforward to generalize other concepts from metabolic control theory, such as control coefficients with their summation and connectivity theorems. The first-order response coefficients describe forced oscillations caused by small harmonic oscillations of single parameters: they depend on the driving frequency and comprise the phases and amplitudes of the concentrations and fluxes. Close to a Hopf bifurcation, resonance can occur: as an example, we study the spectral densities of concentration fluctuations arising from the stochastic nature of chemical reactions. Second-order response coefficients describe how perturbations of different frequencies interact by mode coupling, yielding higher harmonics in the metabolic response. The temporal response to small parameter fluctuations can be computed by Fourier synthesis. For a model of glycolysis, this approximation remains fairly accurate even for large relative fluctuations of the parameters.     

Prefoldin 6 promotes glioma progression via the AKT signalling pathway

Gliomas are one of the most aggressive primary tumours, accounting for 81% of malignant brain tumours, and are associated with a significant mortality. Therefore, the elucidation of the molecular mechanism underlying glioma progression and identification of promising treatment targets are necessary. Here, the expression of prefoldin (PFDN) 6 in human glioma tissues and cell lines was evaluated using immunohistochemistry and quantitative polymerase chain reaction. Celigo and CCK-8 assays were performed for assessing cell viability. Flow cytometry was used to analyse apoptosis and cell cycle distribution. Wound-healing and transwell assays were performed to observe cell migration. Lastly, xenograft models were developed for the in vivo validation of the results, and a human phospho-kinase array was used to explore the downstream signalling pathways. PFDN6 was upregulated in gliomas, and PFDN6 overexpression was significantly correlated with a low survival rate, estimated glomerular filtration rate (EGFR) expression, and tumour grade and recurrence. Moreover, PFDN6 knockdown significantly attenuated cell proliferation and migration, induced apoptosis, and blocked cell cycle progression in the G2 phase, which was further confirmed in the in vivo experiments. Mechanistically, the effects of PFDN6 may be mediated via the AKT signalling pathway. In conclusion, we showed that PFDN6 promotes glioma development by activating AKT signalling and emphasised the potential of PFDN6 as a crucial target in glioma therapy.     

The role of retinal waves and synaptic normalization in retinogeniculate development

The prenatal development of the cat retinogeniculate pathway is thought to involve activity-dependent mechanisms driven by spontaneous waves of retinal activity. The role of these waves upon the segregation of the dorsal lateral geniculate nucleus (LGN) into two eye-specific layers and the development of retinotopic mappings have previously been investigated in a computer model. Using this model, we examine three aspects of retinogeniculate development. First, the mapping of visual space across the whole network into projection columns is shown to be similar to the mapping found in the cat. Second, the simplicity of the model allows us to explore how different forms of synaptic normalization affect development. In comparison to most previous models of ocular dominance, we find that subtractive postsynaptic normalization is redundant and divisive presynaptic normalization is sufficient for normal development. Third, the model predicts that the more often one eye generates waves relative to the other eye, the more LGN units will monocularly respond to the more active eye. In the limit when one eye does not generate any waves, that eye totally disconnects from the LGN allowing the non-deprived eye to innervate all of the LGN. Thus, as well as accounting for normal retinogeniculate development, the model also predicts development under abnormal conditions which can be experimentally tested.     

Unified Saliency Detection Model Using Color and Texture Features

Saliency detection attracted attention of many researchers and had become a very active area of research. Recently, many saliency detection models have been proposed and achieved excellent performance in various fields. However, most of these models only consider low-level features. This paper proposes a novel saliency detection model using both color and texture features and incorporating higher-level priors. The SLIC superpixel algorithm is applied to form an over-segmentation of the image. Color saliency map and texture saliency map are calculated based on the region contrast method and adaptive weight. Higher-level priors including location prior and color prior are incorporated into the model to achieve a better performance and full resolution saliency map is obtained by using the up-sampling method. Experimental results on three datasets demonstrate that the proposed saliency detection model outperforms the state-of-the-art models.     

Computing Complex Visual Features with Retinal Spike Times

Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.     

What Do Contrast Threshold Equivalent Noise Studies Actually Measure? Noise vs. Nonlinearity in Different Masking Paradigms

The internal noise present in a linear system can be quantified by the equivalent noise method. By measuring the effect that applying external noise to the system’s input has on its output one can estimate the variance of this internal noise. By applying this simple “linear amplifier” model to the human visual system, one can entirely explain an observer’s detection performance by a combination of the internal noise variance and their efficiency relative to an ideal observer. Studies using this method rely on two crucial factors: firstly that the external noise in their stimuli behaves like the visual system’s internal noise in the dimension of interest, and secondly that the assumptions underlying their model are correct (e.g. linearity). Here we explore the effects of these two factors while applying the equivalent noise method to investigate the contrast sensitivity function (CSF). We compare the results at 0.5 and 6 c/deg from the equivalent noise method against those we would expect based on pedestal masking data collected from the same observers. We find that the loss of sensitivity with increasing spatial frequency results from changes in the saturation constant of the gain control nonlinearity, and that this only masquerades as a change in internal noise under the equivalent noise method. Part of the effect we find can be attributed to the optical transfer function of the eye. The remainder can be explained by either changes in effective input gain, divisive suppression, or a combination of the two. Given these effects the efficiency of our observers approaches the ideal level. We show the importance of considering these factors in equivalent noise studies.     

Prefoldin 5 is required for normal sensory and neuronal development in a murine model

Molecular chaperones and co-chaperones are crucial for cellular development and maintenance as they assist in protein folding and stabilization of unfolded or misfolded proteins. Prefoldin (PFDN), a ubiquitously expressed heterohexameric co-chaperone, is necessary for proper folding of nascent proteins, in particular, tubulin and actin. Here we show that a genetic disruption in the murine Pfdn5 gene, a subunit of prefoldin, causes a syndrome characterized by photoreceptor degeneration, central nervous system abnormalities, and male infertility. Our data indicate that a missense mutation in Pfdn5, may cause these phenotypes through a reduction in formation of microtubules and microfilaments, which are necessary for the development of cilia and cytoskeletal structures, respectively. The diversity of phenotypes demonstrated by models carrying mutations in different PFDN subunits suggests that each PFDN subunit must confer a distinct substrate specificity to the prefoldin holocomplex.     

Normalization for probabilistic inference with neurons

Recently, there have been a number of proposals regarding how biologically plausible neural networks might perform probabilistic inference (Rao, Neural Computation, 16(1):1-38, 2004; Eliasmith and Anderson, Neural engineering: computation, representation and dynamics in neurobiological systems, 2003; Ma et?al., Nature Neuroscience, 9(11):1432-1438, 2006; Sahani and Dayan, Neural Computation, 15(10):2255-2279, 2003). To be able to repeatedly perform such inference, it is essential that the represented distributions be appropriately normalized. Past approaches have considered normalization mechanisms independently of inference, often leaving them unexplored, or appealing to a notion of divisive normalization that requires pooling across many neurons. Here, we demonstrate how normalization and inference can be combined into an appropriate connection matrix, eliminating the need for pooling or a division-like operation. We algebraically demonstrate that such a solution is available regardless of the inference being performed. We show that such a solution is relevant to neural computation by implementing it in a recurrent spiking neural network.