Abstract:
:Independent component analysis (ICA) aims at separating a multivariate signal into independent nongaussian signals by optimizing a contrast function with no knowledge on the mixing mechanism. Despite the availability of a constellation of contrast functions, a Hartley-entropy-based ICA contrast endowed with the discriminacy property makes it an appealing choice as it guarantees the absence of mixing local optima. Fueled by an outstanding source separation performance of this contrast function in practical instances, a succession of optimization techniques has recently been adopted to solve the ICA problem. Nevertheless, the nondifferentiability of the considered contrast restricts the choice of the optimizer to the class of derivative-free methods. On the contrary, this letter concerns a Riemannian quasi-Newton scheme involving an explicit expression for the gradient to optimize the contrast function that is differentiable almost everywhere. Furthermore, the inexact line search insisting on the weak Wolfe condition and a terminating criterion befitting the partly smooth function optimization have been generalized to manifold settings, leaving the previous results intact. The investigations with diversified images and the electroencephalographic (EEG) data acquired from 45 focal epileptic subjects demonstrate the efficacy of our approach in terms of computational savings and reliable EEG source localization, respectively. Additional experimental results are available in the online supplement.
journal_name
Neural Computjournal_title
Neural computationauthors
Selvan SE,George ST,Balakrishnan Rdoi
10.1162/NECO_a_00700subject
Has Abstractpub_date
2015-03-01 00:00:00pages
628-71issue
3eissn
0899-7667issn
1530-888Xjournal_volume
27pub_type
杂志文章abstract::In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p. d. f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e. g., Bermond & ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766052530866
更新日期:2005-01-01 00:00:00
abstract::The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks tha...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015286
更新日期:2000-07-01 00:00:00
abstract::A representational scheme under which the ranking between represented similarities is isomorphic to the ranking between the corresponding shape similarities can support perfectly correct shape classification because it preserves the clustering of shapes according to the natural kinds prevailing in the external world. ...
journal_title:Neural computation
pub_type: 杂志文章,评审
doi:10.1162/neco.1997.9.4.701
更新日期:1997-05-15 00:00:00
abstract::Several integrate-to-threshold models with differing temporal integration mechanisms have been proposed to describe the accumulation of sensory evidence to a prescribed level prior to motor response in perceptual decision-making tasks. An experiment and simulation studies have shown that the introduction of time-varyi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.07-08-817
更新日期:2009-08-01 00:00:00
abstract::Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01129
更新日期:2018-11-01 00:00:00
abstract::Particular levels of partial fault tolerance (PFT) in feedforward artificial neural networks of a given size can be obtained by redundancy (replicating a smaller normally trained network), by design (training specifically to increase PFT), and by a combination of the two (replicating a smaller PFT-trained network). Th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053723096
更新日期:2005-07-01 00:00:00
abstract::We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00631
更新日期:2014-09-01 00:00:00
abstract::We have created a network that allocates a new computational unit whenever an unusual pattern is presented to the network. This network forms compact representations, yet learns easily and rapidly. The network can be used at any time in the learning process and the learning patterns do not have to be repeated. The uni...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1991.3.2.213
更新日期:1991-07-01 00:00:00
abstract::This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrati...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00947
更新日期:2017-05-01 00:00:00
abstract::In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high ra...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00090
更新日期:2011-03-01 00:00:00
abstract::We formulate an equivalence between machine learning and the formulation of statistical data assimilation as used widely in physical and biological sciences. The correspondence is that layer number in a feedforward artificial network setting is the analog of time in the data assimilation setting. This connection has b...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01094
更新日期:2018-08-01 00:00:00
abstract::Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration gen...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2008.09-06-342
更新日期:2008-07-01 00:00:00
abstract::We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00306
更新日期:2012-08-01 00:00:00
abstract::Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binar...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00889
更新日期:2016-11-01 00:00:00
abstract::The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This a...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660252741194
更新日期:2002-02-01 00:00:00
abstract::Visual navigation requires the estimation of self-motion as well as the segmentation of objects from the background. We suggest a definition of local velocity gradients to compute types of self-motion, segment objects, and compute local properties of optical flow fields, such as divergence, curl, and shear. Such veloc...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00479
更新日期:2013-09-01 00:00:00
abstract::The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a biological neuron. Criteria that tell us when a perceptron can implement (or learn to implement) all possible dichotomies over a given set of ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.20.1.288
更新日期:2008-01-01 00:00:00
abstract::Primary visual cortical complex cells are thought to serve as invariant feature detectors and to provide input to higher cortical areas. We propose a single model for learning the connectivity required by complex cells that integrates two factors that have been hypothesized to play a role in the development of invaria...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00743
更新日期:2015-07-01 00:00:00
abstract::Energy-efficient information transmission may be relevant to biological sensory signal processing as well as to low-power electronic devices. We explore its consequences in two different regimes. In an "immediate" regime, we argue that the information rate should be maximized subject to a power constraint, and in an "...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976601300014358
更新日期:2001-04-01 00:00:00
abstract::Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can g...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01117
更新日期:2018-10-01 00:00:00
abstract::Nondeclarative memory and novelty processing in the brain is an actively studied field of neuroscience, and reducing neural activity with repetition of a stimulus (repetition suppression) is a commonly observed phenomenon. Recent findings of an opposite trend-specifically, rising activity for unfamiliar stimuli-questi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00569
更新日期:2014-04-01 00:00:00
abstract::A simple associationist neural network learns to factor abstract rules (i.e., grammars) from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars. The neural network is shown to have the ability to transfer grammatical kno...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602320264079
更新日期:2002-09-01 00:00:00
abstract::The Nyström method is a well-known sampling-based technique for approximating the eigensystem of large kernel matrices. However, the chosen samples in the Nyström method are all assumed to be of equal importance, which deviates from the integral equation that defines the kernel eigenfunctions. Motivated by this observ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.11-07-651
更新日期:2009-01-01 00:00:00
abstract::We consider the effect of the effective timing of a delayed feedback on the excitatory neuron in a recurrent inhibitory loop, when biological realities of firing and absolute refractory period are incorporated into a phenomenological spiking linear or quadratic integrate-and-fire neuron model. We show that such models...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.8.2124
更新日期:2007-08-01 00:00:00
abstract::We present a model of visual computation based on tightly inter-connected cliques of pyramidal cells. It leads to a formal theory of cell assemblies, a specific relationship between correlated firing patterns and abstract functionality, and a direct calculation relating estimates of cortical cell counts to orientation...
journal_title:Neural computation
pub_type: 杂志文章,评审
doi:10.1162/089976699300016782
更新日期:1999-01-01 00:00:00
abstract::We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero corre...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321192068
更新日期:2003-03-01 00:00:00
abstract::We study the expressive power of positive neural networks. The model uses positive connection weights and multiple input neurons. Different behaviors can be expressed by varying the connection weights. We show that in discrete time and in the absence of noise, the class of positive neural networks captures the so-call...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00789
更新日期:2015-12-01 00:00:00
abstract::A mathematical model, of general character for the dynamic description of coupled neural oscillators is presented. The population approach that is employed applies equally to coupled cells as to populations of such coupled cells. The formulation includes stochasticity and preserves details of precisely firing neurons....
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.03-07-482
更新日期:2008-05-01 00:00:00
abstract::Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action pote...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015682
更新日期:2000-03-01 00:00:00
abstract::In this letter, we develop a gaussian process model for clustering. The variances of predictive values in gaussian processes learned from a training data are shown to comprise an estimate of the support of a probability density function. The constructed variance function is then applied to construct a set of contours ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.11.3088
更新日期:2007-11-01 00:00:00