Competition between synaptic depression and facilitation in attractor neural networks.

abstract：:Humans have the ability to learn novel motor tasks while manipulating the environment. Several models of motor learning have been proposed in the literature, but few of them address the problem of retention and interference of motor memory. The modular selection and identification for control (MOSAIC) model, originall...

journal_title：Neural computation

authors： Lonini L,Dipietro L,Zollo L,Guglielmelli E,Krebs HI

更新日期：2009-07-01 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

authors： Tchernev EB,Mulvaney RG,Phatak DS

更新日期：2005-07-01 00:00:00

abstract：:Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that feature...

journal_title：Neural computation

authors： König P,Engel AK,Roelfsema PR,Singer W

更新日期：1995-05-01 00:00:00

abstract：:A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input sig...

journal_title：Neural computation

authors： Leugering J,Pipa G

更新日期：2018-04-01 00:00:00

abstract：:Real classification problems involve structured data that can be essentially grouped into a relatively small number of clusters. It is shown that, under a local clustering condition, a set of points of a given class, embedded in binary space by a set of randomly parameterized surfaces, is linearly separable from other...

journal_title：Neural computation

authors： Baram Y

更新日期：2001-11-01 00:00:00

abstract：:The important task of generating the minimum number of sequential triangle strips (tristrips) for a given triangulated surface model is motivated by applications in computer graphics. This hard combinatorial optimization problem is reduced to the minimum energy problem in Hopfield nets by a linear-size construction. I...

journal_title：Neural computation

authors： Síma J,Lnĕnicka R

更新日期：2009-02-01 00:00:00

abstract：:Binocular fusion takes place over a limited region smaller than one degree of visual angle (Panum's fusional area), which is on the order of the range of preferred disparities measured in populations of disparity-tuned neurons in the visual cortex. However, the actual range of binocular disparities encountered in natu...

journal_title：Neural computation

authors： Tsang EK,Shi BE

更新日期：2008-10-01 00:00:00

abstract：:Synaptically generated subthreshold membrane potential (Vm) fluctuations can be characterized within the framework of stochastic calculus. It is possible to obtain analytic expressions for the steady-state Vm distribution, even in the case of conductance-based synaptic currents. However, as we show here, the analytic ...

journal_title：Neural computation

authors： Rudolph M,Destexhe A

更新日期：2005-11-01 00:00:00

abstract：:Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the ...

journal_title：Neural computation

authors： Huys QJ,Zemel RS,Natarajan R,Dayan P

更新日期：2007-02-01 00:00:00

abstract：:Observable operator models (OOMs) are a class of models for stochastic processes that properly subsumes the class that can be modeled by finite-dimensional hidden Markov models (HMMs). One of the main advantages of OOMs over HMMs is that they admit asymptotically correct learning algorithms. A series of learning algor...

journal_title：Neural computation

authors： Zhao MJ,Jaeger H,Thon M

更新日期：2009-12-01 00:00:00

abstract：:We perform a detailed fixed-point analysis of two-unit recurrent neural networks with sigmoid-shaped transfer functions. Using geometrical arguments in the space of transfer function derivatives, we partition the network state-space into distinct regions corresponding to stability types of the fixed points. Unlike in ...

journal_title：Neural computation

authors： Tino P,Horne BG,Giles CL

更新日期：2001-06-01 00:00:00

abstract：:Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this ...

journal_title：Neural computation

authors： Cheng S,Sabes PN

更新日期：2006-04-01 00:00:00

abstract：:We considered a gamma distribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape p...

journal_title：Neural computation

authors： Miura K,Okada M,Amari S

更新日期：2006-10-01 00:00:00

abstract：:Humans learn categories of complex objects quickly and from a few examples. Random projection has been suggested as a means to learn and categorize efficiently. We investigate how random projection affects categorization by humans and by very simple neural networks on the same stimuli and categorization tasks, and how...

journal_title：Neural computation

authors： Arriaga RI,Rutter D,Cakmak M,Vempala SS

更新日期：2015-10-01 00:00:00

abstract：:Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using...

journal_title：Neural computation

authors： Harrison MT

更新日期：2013-02-01 00:00:00

abstract：:We examined how the depression of intracortical inhibition due to a reduction in ambient GABA concentration impairs perceptual information processing in schizophrenia. A neural network model with a gliotransmission-mediated ambient GABA regulatory mechanism was simulated. In the network, interneuron-to-glial-cell and ...

journal_title：Neural computation

authors： Hoshino O

更新日期：2013-12-01 00:00:00

abstract：:The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, conve...

journal_title：Neural computation

authors： Hahnloser RH,Seung HS,Slotine JJ

更新日期：2003-03-01 00:00:00

abstract：:In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtu...

journal_title：Neural computation

authors： Zheng CH,Huang DS,Li K,Irwin G,Sun ZL

更新日期：2007-09-01 00:00:00

abstract：:To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple...

journal_title：Neural computation

authors： Troyer TW,Miller KD

更新日期：1997-07-01 00:00:00

abstract：:Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the cons...

journal_title：Neural computation

authors： Franz MO,Chahl JS,Krapp HG

更新日期：2004-11-01 00:00:00

abstract：:We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converges to equilibrium p...

journal_title：Neural computation

authors： Galtier MN,Faugeras OD,Bressloff PC

更新日期：2012-09-01 00:00:00

abstract：:In this work, we propose a two-layered descriptive model for motion processing from retina to the cortex, with an event-based input from the asynchronous time-based image sensor (ATIS) camera. Spatial and spatiotemporal filtering of visual scenes by motion energy detectors has been implemented in two steps in a simple...

journal_title：Neural computation

authors： Khoei MA,Ieng SH,Benosman R

更新日期：2019-06-01 00:00:00

abstract：:Current research on discrete and rhythmic movements differs in both experimental procedures and theory, despite the ubiquitous overlap between discrete and rhythmic components in everyday behaviors. Models of rhythmic movements usually use oscillatory systems mimicking central pattern generators (CPGs). In contrast, m...

journal_title：Neural computation

authors： Ronsse R,Sternad D,Lefèvre P

更新日期：2009-05-01 00:00:00

abstract：:Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challen...

journal_title：Neural computation

authors： Tangkaratt V,Xie N,Sugiyama M

更新日期：2015-01-01 00:00:00

abstract：:Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing it...

journal_title：Neural computation

authors： Haslinger R,Klinkner KL,Shalizi CR

更新日期：2010-01-01 00:00:00

abstract：:We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (a...

journal_title：Neural computation

authors： Lu Z,Leen TK,Kaye J

更新日期：2011-09-01 00:00:00

abstract：:Modeling stereo transparency with physiologically plausible mechanisms is challenging because in such frameworks, large receptive fields mix up overlapping disparities, whereas small receptive fields can reliably compute only small disparities. It seems necessary to combine information across scales. A coarse-to-fine ...

journal_title：Neural computation

authors： Li Z,Qian N

更新日期：2015-05-01 00:00:00

abstract：:Recently we presented a stochastic, ensemble-based model of spike-timing-dependent plasticity. In this model, single synapses do not exhibit plasticity depending on the exact timing of pre- and postsynaptic spikes, but spike-timing-dependent plasticity emerges only at the temporal or synaptic ensemble level. We showed...

journal_title：Neural computation

authors： Appleby PA,Elliott T

更新日期：2007-05-01 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

authors： Legenstein R,Maass W

更新日期：2008-01-01 00:00:00

abstract：:This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class label...

journal_title：Neural computation

authors： Kameoka H,Li L,Inoue S,Makino S

更新日期：2019-09-01 00:00:00