A graphical model framework for decoding in the visual ERP-based BCI speller.

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：: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

abstract：:For the paradigmatic case of bimanual coordination, we review levels of organization of behavioral dynamics and present a description in terms of modes of behavior. We briefly review a recently developed model of spatiotemporal brain activity that is based on short- and long-range connectivity of neural ensembles. Thi...

journal_title：Neural computation

authors： Jirsa VK,Fuchs A,Kelso JA

更新日期：1998-11-15 00:00:00

abstract：:A spiking neuron "computes" by transforming a complex dynamical input into a train of action potentials, or spikes. The computation performed by the neuron can be formulated as dimensional reduction, or feature detection, followed by a nonlinear decision function over the low-dimensional space. Generalizations of the ...

journal_title：Neural computation

authors： Agüera y Arcas B,Fairhall AL,Bialek W

更新日期：2003-08-01 00:00:00

abstract：:For gradient descent learning to yield connectivity consistent with real biological networks, the simulated neurons would have to include more realistic intrinsic properties such as frequency adaptation. However, gradient descent learning cannot be used straightforwardly with adapting rate-model neurons because the de...

journal_title：Neural computation

authors： Fortier PA,Guigon E,Burnod Y

更新日期：2005-09-01 00:00:00

abstract：:We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Th...

journal_title：Neural computation

authors： Helson P

更新日期：2020-07-01 00:00:00

abstract：:We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used wit...

journal_title：Neural computation

authors： Ventura V

更新日期：2008-04-01 00:00:00

abstract：:The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose ...

journal_title：Neural computation

authors： Isomura T,Sakai K,Kotani K,Jimbo Y

更新日期：2016-09-01 00:00:00

abstract：:This article presents a reinforcement learning framework for continuous-time dynamical systems without a priori discretization of time, state, and action. Based on the Hamilton-Jacobi-Bellman (HJB) equation for infinite-horizon, discounted reward problems, we derive algorithms for estimating value functions and improv...

journal_title：Neural computation

authors： Doya K

更新日期：2000-01-01 00:00:00

abstract：:Many neurons that initially respond to a stimulus stop responding if the stimulus is presented repeatedly but recover their response if a different stimulus is presented. This phenomenon is referred to as stimulus-specific adaptation (SSA). SSA has been investigated extensively using oddball experiments, which measure...

journal_title：Neural computation

authors： Mill R,Coath M,Wennekers T,Denham SL

更新日期：2011-02-01 00:00:00

abstract：:The pyloric network of the stomatogastric ganglion in crustacea is a central pattern generator that can produce the same basic rhythm over a wide frequency range. Three electrically coupled neurons, the anterior burster (AB) neuron and two pyloric dilator (PD) neurons, act as a pacemaker unit for the pyloric network. ...

journal_title：Neural computation

authors： Abbott LF,Marder E,Hooper SL

更新日期：1991-01-01 00:00:00

abstract：:We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing stra...

journal_title：Neural computation

authors： Gulcehre C,Chandar S,Cho K,Bengio Y

更新日期：2018-04-01 00:00:00

abstract：:A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers,...

journal_title：Neural computation

authors： Wang J,Hoogeboom HJ,Pan L,Păun G,Pérez-Jiménez MJ

更新日期：2010-10-01 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

authors： Hanson SJ,Negishi M

更新日期：2002-09-01 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：:Ramping neuronal activity refers to spiking activity with a rate that increases quasi-linearly over time. It has been observed in multiple cortical areas and is correlated with evidence accumulation processes or timing. In this work, we investigated the downstream effect of ramping neuronal activity through synapses t...

journal_title：Neural computation

authors： Wei W,Wang XJ

更新日期：2016-04-01 00:00:00

abstract：:The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features that contains all of the information about the output values that the input features possess. In this letter, we propose a novel sufficient dimension-reduction method using a squared-loss variant...

journal_title：Neural computation

authors： Suzuki T,Sugiyama M

更新日期：2013-03-01 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

authors： Zhang K,Kwok JT

更新日期：2009-01-01 00:00:00

abstract：:It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing...

journal_title：Neural computation

authors： Liu S,Grosmark AD,Chen Z

更新日期：2018-08-01 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

authors： Platt J

更新日期：1991-07-01 00:00:00

abstract：:How do multiple feature maps that coexist in the same region of cerebral cortex align with each other? We hypothesize that such alignment is governed by temporal correlations: features in one map that are temporally correlated with those in another come to occupy the same spatial locations in cortex over time. To exam...

journal_title：Neural computation

authors： Chen Y,Reggia JA

更新日期：1996-05-15 00:00:00

abstract：:For any memoryless communication channel with a binary-valued input and a one-dimensional real-valued output, we introduce a probabilistic lower bound on the mutual information given empirical observations on the channel. The bound is built on the Dvoretzky-Kiefer-Wolfowitz inequality and is distribution free. A quadr...

journal_title：Neural computation

authors： VanderKraats ND,Banerjee A

更新日期：2011-07-01 00:00:00

abstract：:GABAergic synapse reversal potential is controlled by the concentration of chloride. This concentration can change significantly during development and as a function of neuronal activity. Thus, GABA inhibition can be hyperpolarizing, shunting, or partially depolarizing. Previous results pinpointed the conditions under...

journal_title：Neural computation

authors： Jeong HY,Gutkin B

更新日期：2007-03-01 00:00:00

abstract：:High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we descr...

journal_title：Neural computation

authors： Lainscsek C,Weyhenmeyer J,Cash SS,Sejnowski TJ

更新日期：2017-12-01 00:00:00

abstract：:We argue that when faced with big data sets, learning and inference algorithms should compute updates using only subsets of data items. We introduce algorithms that use sequential hypothesis tests to adaptively select such a subset of data points. The statistical properties of this subsampling process can be used to c...

journal_title：Neural computation

authors： Korattikara A,Chen Y,Welling M

更新日期：2016-01-01 00:00:00

abstract：:In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward...

journal_title：Neural computation

authors： Wörgötter F,Porr B

更新日期：2005-02-01 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

authors： Sterne P

更新日期：2012-08-01 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：:A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amp...

journal_title：Neural computation

authors： Elliott T

更新日期：2010-01-01 00:00:00

abstract：:Ohshiro, Hussain, and Weliky (2011) recently showed that ferrets reared with exposure to flickering spot stimuli, in the absence of oriented visual experience, develop oriented receptive fields. They interpreted this as refutation of efficient coding models, which require oriented input in order to develop oriented re...

journal_title：Neural computation

authors： Hunt JJ,Ibbotson M,Goodhill GJ

更新日期：2012-09-01 00:00:00