Abstract:
:In a previous article, we considered game trees as graphical models. Adopting an evaluation function that returned a probability distribution over values likely to be taken at a given position, we described how to build a model of uncertainty and use it for utility-directed growth of the search tree and for deciding on a move after search was completed. In some games, such as chess and Othello, the same position can occur more than once, collapsing the game tree to a directed acyclic graph (DAG). This induces correlations among the distributions at sibling nodes. This article discusses some issues that arise in extending our algorithms to a DAG. We give a simply described algorithm for correctly propagating distributions up a game DAG, taking account of dependencies induced by the DAG structure. This algorithm is exponential time in the worst case. We prove that it is #P complete to propagate distributions up a game DAG correctly. We suggest how our exact propagation algorithm can yield a fast but inexact heuristic.
journal_name
Neural Computjournal_title
Neural computationauthors
Baum EB,Smith WDdoi
10.1162/089976699300016881subject
Has Abstractpub_date
1999-01-01 00:00:00pages
215-27issue
1eissn
0899-7667issn
1530-888Xjournal_volume
11pub_type
杂志文章abstract::We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behav...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.10.2739
更新日期:2007-10-01 00:00:00
abstract::We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In thi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00533
更新日期:2014-01-01 00:00:00
abstract::Firing rates and synchronous firing are often simultaneously relevant signals, and they independently or cooperatively represent external sensory inputs, cognitive events, and environmental situations such as body position. However, how rates and synchrony comodulate and which aspects of inputs are effectively encoded...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976606774841521
更新日期:2006-01-01 00:00:00
abstract::Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00399
更新日期:2013-02-01 00:00:00
abstract::In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed r...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660360581930
更新日期:2003-04-01 00:00:00
abstract::We consider the problem of training a linear feedforward neural network by using a gradient descent-like LMS learning algorithm. The objective is to find a weight matrix for the network, by repeatedly presenting to it a finite set of examples, so that the sum of the squares of the errors is minimized. Kohonen showed t...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1991.3.2.226
更新日期:1991-07-01 00:00:00
abstract::Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration gen...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2008.09-06-342
更新日期:2008-07-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00322
更新日期:2012-09-01 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
pub_type: 杂志文章
doi:10.1162/089976698300016954
更新日期:1998-11-15 00:00:00
abstract::Independent component analysis (ICA) finds a linear transformation to variables that are maximally statistically independent. We examine ICA and algorithms for finding the best transformation from the point of view of maximizing the likelihood of the data. In particular, we discuss the way in which scaling of the unmi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016043
更新日期:1999-11-15 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
pub_type: 杂志文章
doi:10.1162/neco.2009.12-07-678
更新日期:2010-01-01 00:00:00
abstract::Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a s...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00665
更新日期:2014-12-01 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
pub_type: 杂志文章
doi:10.1162/0899766053723096
更新日期:2005-07-01 00:00:00
abstract::Synchronized firings in the networks of class 1 excitable neurons with excitatory and inhibitory connections are investigated, and their dependences on the forms of interactions are analyzed. As the forms of interactions, we treat the double exponential coupling and the interactions derived from it: pulse coupling, ex...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053630387
更新日期:2005-06-01 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::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
pub_type: 杂志文章
doi:10.1162/NECO_a_00818
更新日期:2016-04-01 00:00:00
abstract::Field models provide an elegant mathematical framework to analyze large-scale patterns of neural activity. On the microscopic level, these models are usually based on either a firing-rate picture or integrate-and-fire dynamics. This article shows that in spite of the large conceptual differences between the two types ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660260028656
更新日期:2002-07-01 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
pub_type: 杂志文章
doi:10.1162/neco_a_01286
更新日期:2020-07-01 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
pub_type: 杂志文章
doi:10.1162/089976600300014872
更新日期:2000-11-01 00:00:00
abstract::We address the problem of detecting the presence of a recurring stimulus by monitoring the voltage on a multiunit electrode located in a brain region densely populated by stimulus reactive neurons. Published experimental results suggest that under these conditions, when a stimulus is present, the measurements are gaus...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00257
更新日期:2012-04-01 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
pub_type: 杂志文章
doi:10.1162/neco.2007.19.12.3216
更新日期:2007-12-01 00:00:00
abstract::Attractor networks are widely believed to underlie the memory systems of animals across different species. Existing models have succeeded in qualitatively modeling properties of attractor dynamics, but their computational abilities often suffer from poor representations for realistic complex patterns, spurious attract...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2010.02-09-957
更新日期:2010-05-01 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
pub_type: 杂志文章
doi:10.1162/0899766054026693
更新日期:2005-08-01 00:00:00
abstract::The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for class...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602753633411
更新日期:2002-05-01 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::Recent experimental findings have shown the presence of robust and cell-type-specific intraburst firing patterns in bursting neurons. We address the problem of characterizing these patterns under the assumption that the bursts exhibit well-defined firing time distributions. We propose a method for estimating these dis...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.07-07-571
更新日期:2009-04-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00700
更新日期:2015-03-01 00:00:00
abstract::Pharmacologically isolated GABAergic irregular spiking and stuttering interneurons in the mouse visual cortex display highly irregular spike times, with high coefficients of variation approximately 0.9-3, in response to a depolarizing, constant current input. This is in marked contrast to cortical pyramidal cells, whi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.20.1.44
更新日期:2008-01-01 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
pub_type: 杂志文章
doi:10.1162/neco_a_01021
更新日期:2017-12-01 00:00:00
abstract::Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action pote...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015682
更新日期:2000-03-01 00:00:00