Connecting cortical and behavioral dynamics: bimanual coordination.

abstract：:We study active learning (AL) based on gaussian processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging because local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivatives cannot be ...

journal_title：Neural computation

authors： Inatsu Y,Sugita D,Toyoura K,Takeuchi I

更新日期：2020-10-01 00:00:00

abstract：:A neurocomputational model based on emergent massively overlapping neural cell assemblies (CAs) for resolving prepositional phrase (PP) attachment ambiguity is described. PP attachment ambiguity is a well-studied task in natural language processing and is a case where semantics is used to determine the syntactic struc...

journal_title：Neural computation

authors： Nadh K,Huyck C

更新日期：2012-07-01 00:00:00

abstract：:A study of a general central pattern generator (CPG) is carried out by means of a measure of the gain of information between the number of available topology configurations and the output rhythmic activity. The neurons of the CPG are chaotic Hindmarsh-Rose models that cooperate dynamically to generate either chaotic o...

journal_title：Neural computation

authors： Stiesberg GR,Reyes MB,Varona P,Pinto RD,Huerta R

更新日期：2007-04-01 00:00:00

abstract：:A mathematical theory of interacting hypercolumns in primary visual cortex (V1) is presented that incorporates details concerning the anisotropic nature of long-range lateral connections. Each hypercolumn is modeled as a ring of interacting excitatory and inhibitory neural populations with orientation preferences over...

journal_title：Neural computation

authors： Bressloff PC,Cowan JD

更新日期：2002-03-01 00:00:00

abstract：:We investigate the approximation ability of a multilayer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville...

journal_title：Neural computation

authors： Kim T,Adali T

更新日期：2003-07-01 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：:Mild traumatic brain injury (mTBI) presents a significant health concern with potential persisting deficits that can last decades. Although a growing body of literature improves our understanding of the brain network response and corresponding underlying cellular alterations after injury, the effects of cellular disru...

journal_title：Neural computation

authors： Gabrieli D,Schumm SN,Vigilante NF,Meaney DF

更新日期：2021-01-01 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：: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：: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：: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：: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：: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：:In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed α-integration as a principled way of blending multiple positive measures (e.g.,...

journal_title：Neural computation

authors： Choi H,Choi S,Choe Y

更新日期：2013-06-01 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

authors： Christodoulou C,Cleanthous A

更新日期：2011-03-01 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：: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 present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to ...

journal_title：Neural computation

authors： Avnimelech R,Intrator N

更新日期：1999-02-15 00:00:00

abstract：:To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an i...

journal_title：Neural computation

authors： Isomura T,Parr T,Friston K

更新日期：2019-12-01 00:00:00

abstract：:Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks o...

journal_title：Neural computation

authors： Sanger TD

更新日期：2011-08-01 00:00:00

abstract：:In this letter, we investigate the impact of choosing different loss functions from the viewpoint of statistical learning theory. We introduce a convexity assumption, which is met by all loss functions commonly used in the literature, and study how the bound on the estimation error changes with the loss. We also deriv...

journal_title：Neural computation

authors： Rosasco L,De Vito E,Caponnetto A,Piana M,Verri A

更新日期：2004-05-01 00:00:00

abstract：:In a recent paper, Poggio and Girosi (1990) proposed a class of neural networks obtained from the theory of regularization. Regularized networks are capable of approximating arbitrarily well any continuous function on a compactum. In this paper we consider in detail the learning problem for the one-dimensional case. W...

journal_title：Neural computation

authors： Corradi V,White H

更新日期：1995-11-01 00:00:00

abstract：:Due to many experimental reports of synchronous neural activity in the brain, there is much interest in understanding synchronization in networks of neural oscillators and its potential for computing perceptual organization. Contrary to Hopfield and Herz (1995), we find that networks of locally coupled integrate-and-f...

journal_title：Neural computation

authors： Campbell SR,Wang DL,Jayaprakash C

更新日期：1999-10-01 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：:Decision trees and neural networks are widely used tools for pattern classification. Decision trees provide highly localized representation, whereas neural networks provide a distributed but compact representation of the decision space. Decision trees cannot be induced in the online mode, and they are not adaptive to ...

journal_title：Neural computation

authors： Basak J

更新日期：2004-09-01 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：:A hippocampal prosthesis is a very large scale integration (VLSI) biochip that needs to be implanted in the biological brain to solve a cognitive dysfunction. In this letter, we propose a novel low-complexity, small-area, and low-power programmable hippocampal neural network application-specific integrated circuit (AS...

journal_title：Neural computation

authors： Qiao Z,Han Y,Han X,Xu H,Li WXY,Song D,Berger TW,Cheung RCC

更新日期：2018-09-01 00:00:00

abstract：:Complexity of one-hidden-layer networks is studied using tools from nonlinear approximation and integration theory. For functions with suitable integral representations in the form of networks with infinitely many hidden units, upper bounds are derived on the speed of decrease of approximation error as the number of n...

journal_title：Neural computation

authors： Kainen PC,Kůrková V

更新日期：2009-10-01 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 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