Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

Helioseismic Holography of an Artificial Submerged Sound Speed Perturbation and Implications for the Detection of Pre-Emergence Signatures of Active Regions

We use a publicly available numerical wave-propagation simulation of Hartlep et al. 2011 to test the ability of helioseismic holography to detect signatures of a compact, fully submerged, 5% sound-speed perturbation placed at a depth of 50 Mm within a solar model. We find that helioseismic holography as employed in a nominal "lateral-vantage" or "deep-focus" geometry employing quadrants of an annular pupil is capable of detecting and characterizing the perturbation. A number of tests of the methodology, including the use of a plane-parallel approximation, the definition of travel-time shifts, the use of different phase-speed filters, and changes to the pupils, are also performed. It is found that travel-time shifts made using Gabor-wavelet fitting are essentially identical to those derived from the phase of the Fourier transform of the cross-covariance functions. The errors in travel-time shifts caused by the plane-parallel approximation can be minimized to less than a second for the depths and fields of view considered here. Based on the measured strength of the mean travel-time signal of the perturbation, no substantial improvement in sensitivity is produced by varying the analysis procedure from the nominal methodology in conformance with expectations. The measured travel-time shifts are essentially unchanged by varying the profile of the phase-speed filter or omitting the filter entirely. The method remains maximally sensitive when applied with pupils that are wide quadrants, as opposed to narrower quadrants or with pupils composed of smaller arcs. We discuss the significance of these results for the recent controversy regarding suspected pre-emergence signatures of active regions

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted

Hopfield Networks in Relevance and Redundancy Feature Selection Applied to Classification of Biomedical High-Resolution Micro-CT Images

We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filters — a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our Hopfield network selection. We conclude that on the whole, Hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when\ud more features are selected

Estimation of the vertical wavelength of atmospheric gravity waves from airglow imagery

Abstract In the summer of 2010, two imagers were installed in New Mexico with the objective of making stereoscopic observations of atmospheric gravity waves (AGWs). As AGWs propagate vertically, they spatially perturb the airglow emission layers in all three dimensions. Estimates of the vertical wavelength, horizontal wavelength, and the intrinsic frequency are needed to characterize an AGW and quantify its effects on upper atmospheric dynamics. The dispersion relation describes the relationship between vertical and horizontal wavelengths as a function of the intrinsic frequency. Thus, any two of the three aforementioned parameters can be used to determine the third. Mesospheric winds are hard to measure and consequently the intrinsic frequency is difficult to estimate. However, the horizontal wavelength can be directly measured from airglow imagery once the three-dimensional imager field of view is projected onto the two-dimensional image plane. This thesis presents a method to estimate the vertical wavelength using an airglow perturbation model proposed by Anderson et al. (2009). The model is subsequently validated using the observations from ground-based imagers installed in New Mexico. Abstract The perturbed airglow is modeled as a quasi-monochromatic wave and thus, it can be characterized using only a few parameters, one of which is the vertical wavelength. Because the vertical wavelength is embedded in both the phase and the magnitude of this model, two values of the vertical wavelength are estimated by applying two different parameter estimation techniques on the phase and magnitude. The estimation of the vertical wavelength from the phase of the model entails solving an overdetermined system of linear equations by minimizing the sum of the squared residuals. This estimate is then compared to that obtained by iteratively finding the best approximation to the roots of a function, representing the magnitude of the perturbation model. These two techniques are applied on three nights in 2010, and the estimates for the vertical wavelength match to within a few kilometers. Thus, the perturbation model is validated using real data

Reward sharpens orientation coding independently on attention

Rewarding improves performance. Is it due to modulations of the output modules of the neural systems or are there mechanisms favoring more 'generous' inputs? Some recent study included V1 in the the circuitry of reward-based modulations, but the effects of reward can easily be confused with effects of attention. Here we address this issue with a psychophysical dual task to control attention while orientation sensitivity on targets associated to different levels of reward is measured. We found that different reward rates improve orientation discrimination and sharpen the internal response distributions. Data are unaffected by changing attentional load nor by dissociating the feature of the reward cue from the feature relevant for the task. This suggests that reward may act independently on attention by modulating the activity of early sensory stages, perhaps V1, through a SNR improvement of task-relevant channels. Reward acts like attention, but using separate channels

Modeling of evolving textures using granulometries

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