Spikernels: predicting arm movements by embedding population spike rate patterns in inner-product spaces.

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：:Recent advances in the technology of multiunit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblies. This has created the need for statistical approaches to detecting the presence of spatiotemporal patterns of more than two neurons in neuron sp...

journal_title：Neural computation

authors： Martignon L,Deco G,Laskey K,Diamond M,Freiwald W,Vaadia E

更新日期：2000-11-01 00:00:00

abstract：:A necessary ingredient for a quantitative theory of neural coding is appropriate "spike kinematics": a precise description of spike trains. While summarizing experiments by complete spike time collections is clearly inefficient and probably unnecessary, the most common probabilistic model used in neurophysiology, the ...

journal_title：Neural computation

authors： Wójcik DK,Mochol G,Jakuczun W,Wypych M,Waleszczyk WJ

更新日期：2009-08-01 00:00:00

abstract：:Recent experiments indicate that the calcium store (e.g., endoplasmic reticulum) is involved in electrical bursting and [Ca2+]i oscillation in bursting neuronal cells. In this paper, we formulate a mathematical model for bursting neurons, which includes Ca2+ in the intracellular Ca2+ stores and a voltage-independent c...

journal_title：Neural computation

authors： Chay TR

更新日期：1996-07-01 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：: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 discri...

journal_title：Neural computation

authors： Selvan SE,George ST,Balakrishnan R

更新日期：2015-03-01 00:00:00

abstract：:We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated ...

journal_title：Neural computation

authors： Lücke J,von der Malsburg C

更新日期：2004-03-01 00:00:00

abstract：:This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of k -many skip connections into network architectures, such as residual networks and additive dense n...

journal_title：Neural computation

authors： Hauser M,Gunn S,Saab S Jr,Ray A

更新日期：2019-03-01 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 letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem,...

journal_title：Neural computation

authors： Pakzad P,Anantharam V

更新日期：2005-08-01 00:00:00

abstract：:The ability to achieve high swimming speed and efficiency is very important to both the real lamprey and its robotic implementation. In previous studies, we used evolutionary algorithms to evolve biologically plausible connectionist swimming controllers for a simulated lamprey. This letter investigates the robustness ...

journal_title：Neural computation

authors： Or J

更新日期：2007-06-01 00:00:00

abstract：:Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. Fo...

journal_title：Neural computation

authors： Akaho S,Kappen HJ

更新日期：2000-06-01 00:00:00

abstract：:We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also dep...

journal_title：Neural computation

authors： Boegerhausen M,Suter P,Liu SC

更新日期：2003-02-01 00:00:00

abstract：:A firing rate map, also known as a tuning curve, describes the nonlinear relationship between a neuron's spike rate and a low-dimensional stimulus (e.g., orientation, head direction, contrast, color). Here we investigate Bayesian active learning methods for estimating firing rate maps in closed-loop neurophysiology ex...

journal_title：Neural computation

authors： Park M,Weller JP,Horwitz GD,Pillow JW

更新日期：2014-08-01 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：:In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important ...

journal_title：Neural computation

authors： Vehtari A,Lampinen J

更新日期：2002-10-01 00:00:00

abstract：:The nu-support vector machine (nu-SVM) for classification proposed by Schölkopf, Smola, Williamson, and Bartlett (2000) has the advantage of using a parameter nu on controlling the number of support vectors. In this article, we investigate the relation between nu-SVM and C-SVM in detail. We show that in general they a...

journal_title：Neural computation

authors： Chang CC,Lin CJ

更新日期：2001-09-01 00:00:00

abstract：:We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing ...

journal_title：Neural computation

authors： Banerjee A

更新日期：2016-05-01 00:00:00

abstract：:In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system. Motivated by EEG recordings of normal brain activity, Chvátal and Goldsmith asked whether these dynamical systems can be engineered to produce trajectories that are irregular, disorderly, and apparently unpredict...

journal_title：Neural computation

authors： Chvátal V,Goldsmith M,Yang N

更新日期：2016-06-01 00:00:00

abstract：:We propose that replication (with mutation) of patterns of neuronal activity can occur within the brain using known neurophysiological processes. Thereby evolutionary algorithms implemented by neuro- nal circuits can play a role in cognition. Replication of structured neuronal representations is assumed in several cog...

journal_title：Neural computation

authors： Fernando C,Goldstein R,Szathmáry E

更新日期：2010-11-01 00:00:00

abstract：:This article presents a new theoretical framework to consider the dynamics of a stochastic spiking neuron model with general membrane response to input spike. We assume that the input spikes obey an inhomogeneous Poisson process. The stochastic process of the membrane potential then becomes a gaussian process. When a ...

journal_title：Neural computation

authors： Amemori KI,Ishii S

更新日期：2001-12-01 00:00:00

abstract：:The relationship between a neuron's complex inputs and its spiking output defines the neuron's coding strategy. This is frequently and effectively modeled phenomenologically by one or more linear filters that extract the components of the stimulus that are relevant for triggering spikes and a nonlinear function that r...

journal_title：Neural computation

authors： Famulare M,Fairhall A

更新日期：2010-03-01 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：: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：: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：: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：:Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. ...

journal_title：Neural computation

authors： Tetzlaff T,Rotter S,Stark E,Abeles M,Aertsen A,Diesmann M

更新日期：2008-09-01 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：: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

authors： Kim HC,Lee J

更新日期：2007-11-01 00:00:00

abstract：:A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a s...

journal_title：Neural computation

authors： Weber AI,Pillow JW

更新日期：2017-12-01 00:00:00