Estimation and marginalization using the Kikuchi approximation methods.

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：:Most conventional policy gradient reinforcement learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the policy parameter. That term involves the derivative of the stationary state distribution that corresponds to the sensitivity of its distributio...

journal_title：Neural computation

authors： Morimura T,Uchibe E,Yoshimoto J,Peters J,Doya K

更新日期：2010-02-01 00:00:00

abstract：:Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches...

journal_title：Neural computation

authors： Chandar S,Khapra MM,Larochelle H,Ravindran B

更新日期：2016-02-01 00:00:00

abstract：:Natural gradient learning is known to be efficient in escaping plateau, which is a main cause of the slow learning speed of neural networks. The adaptive natural gradient learning method for practical implementation also has been developed, and its advantage in real-world problems has been confirmed. In this letter, w...

journal_title：Neural computation

authors： Park H,Murata N,Amari S

更新日期：2004-02-01 00:00:00

abstract：:Many different types of integrate-and-fire models have been designed in order to explain how it is possible for a cortical neuron to integrate over many independent inputs while still producing highly variable spike trains. Within this context, the variability of spike trains has been almost exclusively measured using...

journal_title：Neural computation

authors： Jackson BS

更新日期：2004-10-01 00:00:00

abstract：:Durbin and Willshaw's elastic net algorithm can find good solutions to the TSP. The purpose of this paper is to point out that for certain ranges of parameter values, the algorithm converges into local minima that do not correspond to valid tours. The key parameter is the ratio governing the relative strengths of the ...

journal_title：Neural computation

authors： Simmen MW

更新日期：1991-10-01 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

authors： Ma J,Wu J

更新日期：2007-08-01 00:00:00

abstract：:The Hebbian paradigm is perhaps the best-known unsupervised learning theory in connectionism. It has inspired wide research activity in the artificial neural network field because it embodies some interesting properties such as locality and the capability of being applicable to the basic weight-and-sum structure of ne...

journal_title：Neural computation

authors： Fiori S

更新日期：2005-04-01 00:00:00

abstract：:Although the commonly used quadratic Hebbian-anti-Hebbian rules lead to successful models of plasticity and learning, they are inconsistent with neurophysiology. Other rules, more physiologically plausible, fail to specify the biological mechanism of bidirectionality and the biological mechanism that prevents synapses...

journal_title：Neural computation

authors： Grzywacz NM,Burgi PY

更新日期：1998-04-01 00:00:00

abstract：:Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter...

journal_title：Neural computation

authors： Chen R,Li L

更新日期：2020-12-01 00:00:00

abstract：:When subjects adapt their reaching movements in the setting of a systematic force or visual perturbation, generalization of adaptation can be assessed psychophysically in two ways: by testing untrained locations in the work space at the end of adaptation (slow postadaptation generalization) or by determining the influ...

journal_title：Neural computation

authors： Tanaka H,Krakauer JW,Sejnowski TJ

更新日期：2012-04-01 00:00:00

abstract：:We present an integrative formalism of mutual information expansion, the general Poisson exact breakdown, which explicitly evaluates the informational contribution of correlations in the spike counts both between and within neurons. The formalism was validated on simulated data and applied to real neurons recorded fro...

journal_title：Neural computation

authors： Scaglione A,Moxon KA,Foffani G

更新日期：2010-06-01 00:00:00

abstract：:In considering a statistical model selection of neural networks and radial basis functions under an overrealizable case, the problem of unidentifiability emerges. Because the model selection criterion is an unbiased estimator of the generalization error based on the training error, this article analyzes the expected t...

journal_title：Neural computation

authors： Hagiwara K

更新日期：2002-08-01 00:00:00

abstract：:Izhikevich (2003) proposed a new canonical neuron model of spike generation. The model was surprisingly simple yet able to accurately replicate the firing patterns of different types of cortical cell. Here, we derive a solution method that allows efficient simulation of the model. ...

journal_title：Neural computation

authors： Humphries MD,Gurney K

更新日期：2007-12-01 00:00:00

abstract：:We derive solutions for the problem of missing and noisy data in nonlinear time&hyphenseries prediction from a probabilistic point of view. We discuss different approximations to the solutions &hyphen in particular, approximations that require either stochastic simulation or the substitution of a single estimate for t...

journal_title：Neural computation

authors： Tresp V V,Hofmann R

更新日期：1998-03-23 00:00:00

abstract：:We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of inte...

journal_title：Neural computation

authors： van Vreeswijk C,Hansel D

更新日期：2001-05-01 00:00:00

abstract：:This article presents new procedures for multisite spatiotemporal neuronal data analysis. A new statistical model - the diffusion model - is considered, whose parameters can be estimated from experimental data thanks to mean-field approximations. This work has been applied to optical recording of the guinea pig's audi...

journal_title：Neural computation

authors： François O,Abdallahi LM,Horikawa J,Taniguchi I,Hervé T

更新日期：2000-08-01 00:00:00

abstract：:The expected free energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to the balance of exploration and exploitation that active inference...

journal_title：Neural computation

authors： Millidge B,Tschantz A,Buckley CL

更新日期：2021-01-05 00:00:00

abstract：:We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchan...

journal_title：Neural computation

authors： Takenouchi T,Eguchi S,Murata N,Kanamori T

更新日期：2008-06-01 00:00:00

abstract：:This article addresses the topic of extracting logical rules from data by means of artificial neural networks. The approach based on piecewise linear neural networks is revisited, which has already been used for the extraction of Boolean rules in the past, and it is shown that this approach can be important also for t...

journal_title：Neural computation

authors： Holena M

更新日期：2006-11-01 00:00:00

abstract：:A single-layered Hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure (e.g., a straight line). The network is able to learn adaptively the parametric forms of the linear segm...

journal_title：Neural computation

authors： Basak J

更新日期：2001-03-01 00:00:00

abstract：:Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding perfo...

journal_title：Neural computation

authors： Pagan M,Simoncelli EP,Rust NC

更新日期：2016-11-01 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：: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

authors： Golden RM

更新日期：2018-10-01 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

authors： Ameloot TJ,Van den Bussche J

更新日期：2015-12-01 00:00:00

abstract：:We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is giv...

journal_title：Neural computation

authors： Friedrich J,Urbanczik R,Senn W

更新日期：2010-07-01 00:00:00

abstract：:We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moder...

journal_title：Neural computation

authors： Sarpeshkar R,O'Halloran M

更新日期：2002-09-01 00:00:00

abstract：:Robust coding has been proposed as a solution to the problem of minimizing decoding error in the presence of neural noise. Many real-world problems, however, have degradation in the input signal, not just in neural representations. This generalized problem is more relevant to biological sensory coding where internal n...

journal_title：Neural computation

authors： Doi E,Lewicki MS

更新日期：2011-10-01 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：:Changes in GABA modulation may underlie experimentally observed changes in the strength of synaptic transmission at different phases of the theta rhythm (Wyble, Linster, & Hasselmo, 1997). Analysis demonstrates that these changes improve sequence disambiguation by a neural network model of CA3. We show that in the fra...

journal_title：Neural computation

authors： Sohal VS,Hasselmo ME

更新日期：1998-05-15 00:00:00