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On the role of sparseness in the evolution of modularity in gene regulatory networks.
PLoS Computational Biology, 2018 Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system.
Carlos Espinosa-Soto
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Analytica Chimica Acta, 2018 The use of sparseness in chemometrics is a concept that has increased in popularity. The advantage is, above all, a better interpretability of the results obtained. In this work, sparseness is implemented as a constraint in multivariate curve resolution -
Cyril Ruckebusch +2 more
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ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks [PDF]
SensorsAccurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study
Oliver W. Layton +2 more
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Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex. [PDF]
PLoS Computational BiologyMany experimental and computational studies deal with sparseness, balance, and decorrelation in neural networks and explain the presence of these properties as fulfilling requirements related to optimum energy efficiency, network stability, and ...
Marko A Ruslim +5 more
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Mutation rules and the evolution of sparseness and modularity in biological systems. [PDF]
PLoS ONE, 2013 Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules ...
Tamar Friedlander +3 more
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Sparseness facilitates image encoding across visuo-frontal networks in freely moving macaque [PDF]
Nature CommunicationsSparseness is a quantitative measure of coding efficiency that characterizes the extent to which information is represented by the selective activation of a small subset of neurons.
Arun Parajuli +2 more
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Long memory lifetimes require complex synapses and limited sparseness
Frontiers in Computational Neuroscience, 2007 Theoretical studies have shown that memories last longer if the neural representations are sparse, that is, when each neuron is selective for a small fraction of the events creating the memories.
Stefano Fusi
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Direct construction of sparse suffix arrays with Libsais [PDF]
BMC BioinformaticsBackground Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets.
Simon Van de Vyver +4 more
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Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. [PDF]
PLoS Computational Biology, 2013 The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive
Joel Zylberberg, Michael Robert DeWeese
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Frontiers in Applied Mathematics and StatisticsTemporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time.
Deanna Needell
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