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Diverse tuning underlies sparse activity in layer 2/3 vibrissal cortex of awake mice

JOURNAL OF PHYSIOLOGY-LONDON 2019年第10期597卷 2803-2817页

作者： Ranjbar-Slamloo, Yadollah Arabzadeh, Ehsan Australian Natl Univ John Curtin Sch Med Res Eccles Inst Neurosci Canberra ACT 0200 Australia Australian Natl Univ Node Ctr Excellence Integrat Brain Funct Australian Res Council Canberra ACT Australia

It is widely reported that superficial layers of the somatosensory cortex exhibit sparse firing. This sparseness could reflect weak feedforward sensory inputs that are not sufficient to generate action potentials in these layers. Alternatively, sparseness might reflect tuning to unknown or higher-level complex features that are not fully explored in the stimulus space. Here, we examined these hypotheses by applying a range of vibrotactile and manual vibrissal stimuli in awake, head-fixed mice while performing loose-seal cell-attached recordings from the vibrissal primary somatosensory (vS1) cortex. A high-velocity stimulus delivered by a piezo-electric actuator evoked activity in a small fraction of regular spiking supragranular neurons (23%) in the awake condition. However, a majority of the supragranular regular spiking neurons (84%) were driven by manual stimulation of whiskers. Our results suggest that most neurons in the superficial layers of vS1 cortex contribute to coding in the awake condition when neurons may encounter their preferred feature(s) during whisker-object interactions.

关键词： sparse coding Vibrissal cortex Whisker contact Cell-attached recording Whisker tracking Sensory coding Neuronal tuning Layer 2 3 sparseness

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sparse Softmax Vector coding Based Deep Cascade Model 2nd

Sparse Softmax Vector Coding Based Deep Cascade Model

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2nd CCF Chinese Conference on Computer Vision (CCCV)

作者： Liu, Ji Zhang, Lei Chongqing Univ Coll Commun Engn 174 Shazheng St Chongqing 400044 Peoples R China

ISBN: (纸本)9789811073021;9789811073014

Recently, many sparse coding techniques like sparse representation based classification (SRC) have been proposed to deal with face recognition (FR) problem. In SRC, a testing image is linearly coded by the training images to calculate the sparse coefficients by l1-norm minimization. Then, SRC needs to compute the representation error of each category when classifying the testing image. The corresponding category of the testing image would have the minimum representation error. In other words, representation errors of all classes show class discrimination. In this paper, we take advantage of this distinct representation errors that are transformed into softmax vector and find that the subpattern of the whole image is sometimes more discriminative than the whole image. sparse softmax vector coding based deep cascade model (SSVD) is proposed to improve the pattern classification performance. The experiments demonstrate that the proposed model is much more effective than state-of-the-art methods.

关键词： sparse coding Softmax vector Spatial pyramid Deep cascade model

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Middle-Level Features for the Explanation of Classification Systems by sparse Dictionary Methods

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2020年第8期30卷 2050040页

作者： Apicella, A. Isgro, F. Prevete, R. Tamburrini, G. Univ Napoli Federico II Dipartimento Ingn Elettr & Tecnol Informaz I-80125 Naples Italy

Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs. However, these approaches leave a substantive explanatory burden with human users, insofar as the latter are required to map low-level properties into more salient and readily understandable parts of the input. To alleviate this cognitive burden, an alternative model-agnostic framework is proposed here. This framework is instantiated to address explanation problems in the context of ML image classification systems, without relying on pixel relevance maps and other low-level features of the input. More specifically, one obtains sets of middle-level properties of classification inputs that are perceptually salient by applying sparse dictionary learning techniques. These middle-level properties are used as building blocks for explanations of image classifications. The achieved explanations are parsimonious, for their reliance on a limited set of middle-level image properties. And they can be contrastive, because the set of middle-level image properties can be used to explain why the system advanced the proposed classification over other antagonist classifications. In view of its model-agnostic character, the proposed framework is adaptable to a variety of other ML systems and explanation problems.

关键词： XAI and explainable artificial intelligence machine learning sparse coding

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Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing

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REMOTE SENSING 2020年第18期12卷 2882-2882页

作者： Wang, Wenhong Liu, Hongfu Liaocheng Univ Coll Comp Sci Liaocheng 252059 Shandong Peoples R China Brandeis Univ Volen Natl Ctr Complex Syst Dept Comp Sci Waltham MA 02453 USA

As a powerful blind source separation tool, Nonnegative Matrix Factorization (NMF) with effective regularizations has shown significant superiority in spectral unmixing of hyperspectral remote sensing images (HSIs) owing to its good physical interpretability and data adaptability. However, the majority of existing NMF-based spectral unmixing methods only adopt the single layer factorization, which is not favorable for exploiting the complex and structured representation relationship of endmembers implied in HSIs. In order to overcome such an issue, we propose a novel two-stage Deep Nonnegative Dictionary Factorization (DNDF) approach with a sparseness constraint and self-supervised regularization for HSI unmixing. Beyond simply extending one-layer factorization to multi-layer, DNDF conducts fuzzy clustering to tackle the mixed endmembers of HSIs. Moreover, self-supervised regularization is integrated into our DNDF model to impose an effective constraint on the endmember matrix. Experimental results on three real HSIs demonstrate the superiority of DNDF over several state-of-the-art methods.

关键词： deep nonnegative dictionary factorization hyperspectral unmixing self-supervised learning sparse coding

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Re-evaluating Circuit Mechanisms Underlying Pattern Separation

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NEURON 2019年第4期101卷 584-602页

作者： Cayco-Gajic, N. Alex Silver, R. Angus UCL Dept Neurosci Physiol & Pharmacol Gower St London WC1E 6BT England

When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability.

关键词： pattern separation neural circuits cerebellum hippocampus insect mushroom body sparse coding sparse connectivity decorrelation dimensionality sensorimotor processing

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Multimodal classification of drug-naive first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging

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NEUROSCIENCE LETTERS 2019年第0期705卷 87-93页

作者： Zhuang, Huixiang Liu, Ruihao Wu, Chaowei Meng, Ziyu Wang, Danni Liu, Dengtang Liu, Manhua Li, Yao Shanghai Jiao Tong Univ Sch EIEE Dept Instrument Sci & Engn Shanghai 200240 Peoples R China Columbia Univ Fu Fdn Sch Engn & Appl Sci Dept Biomed Engn New York NY 10027 USA Shanghai Jiao Tong Univ Sch Biomed Engn Inst Med Imaging Technol 1954 Huashan Rd Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sch Med Shanghai Mental Hlth Ctr Shanghai Peoples R China

The accurate diagnosis in the early stage of schizophrenia (SZ) is of great importance yet remains challenging. The classification between SZ and control groups based on magnetic resonance imaging (MRI) data using machine learning method could be helpful for SZ diagnosis. Increasing evidence showed that the combination of multimodal MRI data might further improve the classification performance However, medication effect has a profound influence on patients' anatomical and functional features and may reduce the classification efficiency. In this paper, we proposed a multimodal classification method to discriminate drug-naive first-episode schizophrenia patients from healthy controls (HCs) by a combined structural MRI, diffusion tensor imaging (DTI) and resting state-functional MRI data. To reduce the feature dimension of multimodal data, we applied sparse coding (SC) for feature selection and multi-kernel support vector machine (SVM) for feature combination and classification. The best classification performance with the classification accuracy of 84.29% and area under the receiver operating characteristic (ROC) curve (AUC) of 81.64% was achieved when all modality data were combined. Interestingly, the identified functional markers were mainly found in default mode network (DMN) and cerebellar connections, while the structural markers were within limbic system and prefrontal-thalamo-hippocampal circuit.

关键词： Schizophrenia Multimodal MRI Classification Multi-kernel Linear SVM sparse coding

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EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability

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JOURNAL OF NEURAL ENGINEERING 2020年第2期17卷 026007-026007页

作者： Nishimoto, Takashi Higashi, Hiroshi Morioka, Hiroshi Ishii, Shin Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan ATR Neural Informat Anal Labs Kyoto 6190288 Japan ATR Cognit Mech Labs Kyoto 6190288 Japan Univ Tokyo Int Res Ctr Neurointelligence Tokyo 1130033 Japan OMRON Co Ltd Technol & Intellectual Property HQ Kyoto Japan

Objective. Brain activity signals are possible biomarkers for personal authentication. However, they are inherently variable due to measurement-environment factors and subject-dependent factors;electroencephalography (EEG) signals could be different in days even for the same task, subject, and experimental settings. This variability could cause loss of consistency of the signals across multiple measurements of a single subject, and hence decrease the performance of EEG-based personal identification. In this study, we evaluated the influence of the variability on personal EEG features by using our original EEG dataset. Approach. We collected EEG signals in twenty subjects across four rounds (morning and afternoon daily for two days). At each round, we reinstalled an EEG cap on the subjects' scalps. To extract personal EEG features that were invariant across the sessions, we proposed unsupervised learning methods;common dictionary learning and t-distributed stochastic neighbor embedding. To assess the performance of personal identification, we compared two different experimental settings;test data recorded in the same round as the training data (Setting SR) and test data recorded in different rounds (Setting DR). Main results. The performance in SR was better than that in DR, suggesting that features dependent on the rounds were dominant. However, the 40% accuracy rate in DR, which is significantly higher than the chance level, suggests that our proposed method robustly extracted the personal features against the variability, in most cases. Furthermore, we also evaluated the performance of a problem, which involved detecting individuals who were not registered in the authentication system. In this problem, we obtained a similar result that the variability for the rounds influenced the performance. However, we obtained a good performance in the detection of some unknown subjects even in DR. Significance. We found the variability in EEG data actually affected the person

关键词： personal identification biometric authentication electroencephalography dictionary learning sparse coding resting-state brain activity spatial attention

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Stimulus selectivity and brain state underlie sparse activity in mouse vibrissal cortex

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JOURNAL OF PHYSIOLOGY-LONDON 2019年第16期597卷 4127-4129页

作者： Buchan, Matthew J. Gothard, Gemma van Rheede, Joram J. Univ Oxford Dept Pharmacol Mansfield Rd Oxford OX1 3QT England Univ Oxford Dept Expt Psychol Woodstock Rd Oxford OX2 6GG England

The authors account for this by plotting sparseness against the fraction of neurons which respond to a given stimulus; however, there was not always a negative correlation between the fraction of responsive neurons and sparseness. The response distribution of a population may prove important, for example, when considering the response properties of FS neurons which exhibit similar sparseness values during spontaneous activity and in response to whisker stimulation. It would be particularly interesting to see if the trial-to-trial variability is lower for certain stimulus categories indicating whether such types of stimulation are better suited to the response tunings of neurons in the barrel cortex. [Extracted from the article]

关键词： sparse coding Somatosensory Systems Electrophysiology

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Patch-wise label propagation for MR brain segmentation based on multi-atlas images

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MULTIMEDIA SYSTEMS 2019年第2期25卷 73-81页

作者： Wang, Yan Zu, Chen Ma, Zongqing Luo, Yong He, Kun Wu, Xi Zhou, Jiliu Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350121 Fujian Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Sichuan Univ West China Hosp Dept Head & Neck & Mammary Oncol Chengdu Sichuan Peoples R China Chengdu Univ Informat Technol Coll Comp Sci Chengdu Sichuan Peoples R China

In many neuroscience and clinical studies, accurate and automatic segmentation of subcortical structures is an important and difficult task. Multi-atlas-based segmentation method has been focus of considerable research due to its promising performance. In general, this technique first employs deformable image registration to construct the correspondences between pre-labeled atlas images and the target image. Then, using the acquired deformation field, labels in the atlas are propagated to the target image space. Obviously, anatomical differences between the target image and atlas images possibly affect the image registration accuracy, thus influencing the final segmentation performance. Another limitation is that the label propagation in most conventional multi-atlas based methods is implemented under a voxel-wise strategy, which cannot adequately utilize the local label information to determine the final label of the target sample. In this paper, we propose a patch-wise label propagation method based on multiple atlases for MR brain segmentation. First, each image patch is characterized by patch intensities and abundant texture features, to increase the accuracy of the patch-based similarity measurement. To determine the weights of the training patches for representing the test sample, a patch-based sparse coding procedure is employed. In the label propagation stage, to alleviate possible misalignment from the registration stage, we perform a patch-wise label propagation strategy in a nonlocal manner to predict the final label for each target sample. To evaluate the proposed segmentation method, we comprehensively implement our proposed method by conducting hippocampus segmentation on the ADNI data set. Experimental results demonstrate the effectiveness of the proposed method and show that the proposed method outperforms two conventional multi-atlas-based methods.

关键词： MR segmentation Multi-atlas based method sparse coding Patch-wise label propagation

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Dictionary Learning-Based Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems with a Lens Antenna Array 15

Dictionary Learning-Based Beamspace Channel Estimation in Mi...

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15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： Nazzal, Mahmoud Aygul, Mehmet Ali Gorcin, Ali Arslan, Huseyin Istanbul Medipol Univ Istanbul Turkey Yildiz Tech Univ Istanbul Turkey TUBITAK Informat & Informat Secur Res Ctr BILGEM Kocaeli Turkey Univ S Florida Tampa FL USA

ISBN: (纸本)9781538677476

Recent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.

关键词： Beamspace channel estimation beam selection millimeter-waves massive MIMO sparse coding dictionary learning

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