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 rates (Bugmann, Christodoulou, & Taylor, 1997; Christodoulou & Bugmann, 2001), enhances learning. More specifically, it enhances reward-modulated spike-timing-dependent plasticity with eligibility trace when used in spiking neural networks, as shown by the results when tested in the simple benchmark problem of XOR, as well as in a complex multiagent setting task.
journal_name
Neural Computjournal_title
Neural computationauthors
Christodoulou C,Cleanthous Adoi
10.1162/NECO_a_00090subject
Has Abstractpub_date
2011-03-01 00:00:00pages
656-63issue
3eissn
0899-7667issn
1530-888Xjournal_volume
23pub_type
杂志文章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
pub_type: 杂志文章
doi:10.1162/NECO_a_00262
更新日期:2012-04-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00164
更新日期:2011-09-01 00:00:00
abstract::Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00917
更新日期:2017-02-01 00:00:00
abstract::Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectori...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00012
更新日期:2010-09-01 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
pub_type: 信件
doi:10.1162/NECO_a_00769
更新日期:2015-10-01 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
pub_type: 杂志文章
doi:10.1162/0899766054323017
更新日期:2005-09-01 00:00:00
abstract::Neuroscience is progressing vigorously, and knowledge at different levels of description is rapidly accumulating. To establish relationships between results found at these different levels is one of the central challenges. In this simulation study, we demonstrate how microscopic cellular properties, taking the example...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016377
更新日期:1999-07-01 00:00:00
abstract::In this letter, we propose a noisy nonlinear version of independent component analysis (ICA). Assuming that the probability density function (p. d. f.) of sources is known, a learning rule is derived based on maximum likelihood estimation (MLE). Our model involves some algorithms of noisy linear ICA (e. g., Bermond & ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766052530866
更新日期:2005-01-01 00:00:00
abstract::We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero corre...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321192068
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
pub_type: 杂志文章
doi:10.1162/neco.2007.19.9.2557
更新日期:2007-09-01 00:00:00
abstract::In learning theory, the training and test sets are assumed to be drawn from the same probability distribution. This assumption is also followed in practical situations, where matching the training and test distributions is considered desirable. Contrary to conventional wisdom, we show that mismatched training and test...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00697
更新日期:2015-02-01 00:00:00
abstract::The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for model...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco_a_01275
更新日期:2020-05-01 00:00:00
abstract::Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976606776240995
更新日期:2006-05-01 00:00:00
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
pub_type: 杂志文章
doi:10.1162/neco.2009.03-08-721
更新日期:2009-07-01 00:00:00
abstract::Recently there has been great interest in sparse representations of signals under the assumption that signals (data sets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many algorithms have been developed to find such representations for one-dimensional signals (vectors...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00385
更新日期:2013-01-01 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
pub_type: 杂志文章
doi:10.1162/089976602317250870
更新日期:2002-03-01 00:00:00
abstract::The successor representation was introduced into reinforcement learning by Dayan ( 1993 ) as a means of facilitating generalization between states with similar successors. Although reinforcement learning in general has been used extensively as a model of psychological and neural processes, the psychological validity o...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00282
更新日期:2012-06-01 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
pub_type: 杂志文章
doi:10.1162/neco.2008.11-07-651
更新日期:2009-01-01 00:00:00
abstract::Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concen...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00906
更新日期:2017-01-01 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
pub_type: 杂志文章
doi:10.1162/089976600300015439
更新日期:2000-06-01 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
pub_type: 杂志文章
doi:10.1162/089976601750399335
更新日期:2001-09-01 00:00:00
abstract::We describe an analytical framework for the adaptations of neural systems that adapt its internal structure on the basis of subjective probabilities constructed by computation of randomly received input signals. A principled approach is provided with the key property that it defines a probability density model that al...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015862
更新日期:2000-02-01 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
pub_type: 杂志文章
doi:10.1162/089976699300016737
更新日期:1999-02-15 00:00:00
abstract::Event-driven simulation strategies were proposed recently to simulate integrate-and-fire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating large-scale networks of neurons; most important, such approaches are more precise than traditional clock-driven numerical ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2006.18.9.2146
更新日期:2006-09-01 00:00:00
abstract::We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766054322991
更新日期:2005-09-01 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
pub_type: 杂志文章
doi:10.1162/neco.2009.12-08-922
更新日期:2010-02-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00683
更新日期:2015-01-01 00:00:00
abstract::Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attracto...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1996.8.6.1135
更新日期:1996-08-15 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
pub_type: 杂志文章
doi:10.1162/neco.2010.05-09-1010
更新日期:2010-07-01 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
pub_type: 杂志文章
doi:10.1162/0899766041941899
更新日期:2004-11-01 00:00:00