380 research outputs found

    Learning Mid-Level Auditory Codes from Natural Sound Statistics

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    Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. Although models exist in the visual domain to explain how mid-level features such as junctions and curves might be derived from oriented filters in early visual cortex, little is known about analogous grouping principles for mid-level auditory representations. We propose a hierarchical generative model of natural sounds that learns combina- tions of spectrotemporal features from natural stimulus statistics. In the first layer the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first layer coefficients. Because second-layer features are sensitive to combi- nations of spectrotemporal features, the representation they support encodes more complex acoustic patterns than the first layer. When trained on corpora of speech and environmental sounds, some second-layer units learned to group spectrotemporal features that occur together in natural sounds. Others instantiate opponency between dissimilar sets of spectrotemporal features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for mid-level neuronal computation.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

    The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).

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    This paper explores the theoretical consequences of a simple assumption: the computational goal of the feedforward path in the ventral stream -- from V1, V2, V4 and to IT -- is to discount image transformations, after learning them during development

    A survey of visual preprocessing and shape representation techniques

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    Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

    Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

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    This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

    Perceptual Image Quality Of Launch Vehicle Imaging Telescopes

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    A large fleet (in the hundreds) of high quality telescopes are used for tracking and imaging of launch vehicles during ascent from Cape Canaveral Air Force Station and Kennedy Space Center. A maintenance tool has been development for use with these telescopes. The tool requires rankings of telescope condition in terms of the ability to generate useful imagery. It is thus a case of ranking telescope conditions on the basis of the perceptual image quality of their imagery. Perceptual image quality metrics that are well-correlated to observer opinions of image quality have been available for several decades. However, these are quite limited in their applications, not being designed to compare various optical systems. The perceptual correlation of the metrics implies that a constant image quality curve (such as the boundary between two qualitative categories labeled as excellent and good) would have a constant value of the metric. This is not the case if the optical system parameters (such as object distance or aperture diameter) are varied. No published data on such direct variation is available and this dissertation presents an investigation made into the perceptual metric responses as system parameters are varied. This investigation leads to some non-intuitive conclusions. The perceptual metrics are reviewed as well as more common metrics and their inability to perform in the necessary manner for the research of interest. Perceptual test methods are also reviewed, as is the human visual system. iv Image formation theory is presented in a non-traditional form, yielding the surprising result that perceptual image quality is invariant under changes in focal length if the final displayed image remains constant. Experimental results are presented of changes in perceived image quality as aperture diameter is varied. Results are analyzed and shortcomings in the process and metrics are discussed. Using the test results, predictions are made about the form of the metric response to object distance variations, and subsequent testing was conducted to validate the predictions. The utility of the results, limitations of applicability, and the immediate ability to further generalize the results is presented

    Biologically inspired feature extraction for rotation and scale tolerant pattern analysis

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    Biologically motivated information processing has been an important area of scientific research for decades. The central topic addressed in this dissertation is utilization of lateral inhibition and more generally, linear networks with recurrent connectivity along with complex-log conformal mapping in machine based implementations of information encoding, feature extraction and pattern recognition. The reasoning behind and method for spatially uniform implementation of inhibitory/excitatory network model in the framework of non-uniform log-polar transform is presented. For the space invariant connectivity model characterized by Topelitz-Block-Toeplitz matrix, the overall network response is obtained without matrix inverse operations providing the connection matrix generating function is bound by unity. It was shown that for the network with the inter-neuron connection function expandable in a Fourier series in polar angle, the overall network response is steerable. The decorrelating/whitening characteristics of networks with lateral inhibition are used in order to develop space invariant pre-whitening kernels specialized for specific category of input signals. These filters have extremely small memory footprint and are successfully utilized in order to improve performance of adaptive neural whitening algorithms. Finally, the method for feature extraction based on localized Independent Component Analysis (ICA) transform in log-polar domain and aided by previously developed pre-whitening filters is implemented. Since output codes produced by ICA are very sparse, a small number of non-zero coefficients was sufficient to encode input data and obtain reliable pattern recognition performance

    Multispectral data analysis Final report

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    Contour maps and prediction lines demonstrating existence of water depth information in multispectral dat
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