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 observation, we extend the Nyström method to a more general, density-weighted version. We show that by introducing the probability density function as a natural weighting scheme, the approximation of the eigensystem can be greatly improved. An efficient algorithm is proposed to enforce such weighting in practice, which has the same complexity as the original Nyström method and hence is notably cheaper than several other alternatives. Experiments on kernel principal component analysis, spectral clustering, and image segmentation demonstrate the encouraging performance of our algorithm.
journal_name
Neural Computjournal_title
Neural computationauthors
Zhang K,Kwok JTdoi
10.1162/neco.2008.11-07-651subject
Has Abstractpub_date
2009-01-01 00:00:00pages
121-46issue
1eissn
0899-7667issn
1530-888Xjournal_volume
21pub_type
杂志文章abstract::We study both analytically and numerically the effect of presynaptic noise on the transmission of information in attractor neural networks. The noise occurs on a very short timescale compared to that for the neuron dynamics and it produces short-time synaptic depression. This is inspired in recent neurobiological find...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976606775623342
更新日期:2006-03-01 00:00:00
abstract::The ability to encode and transmit a signal is an essential property that must demonstrate many neuronal circuits in sensory areas in addition to any processing they may provide. It is known that an appropriate level of lateral inhibition, as observed in these areas, can significantly improve the encoding ability of a...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00100
更新日期:2011-04-01 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 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
pub_type: 杂志文章,评审
doi:10.1162/0899766053011555
更新日期:2005-02-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00290
更新日期:2012-07-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00151
更新日期:2011-08-01 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 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00031
更新日期:2010-11-01 00:00:00
abstract::Memory models that store new memories by forgetting old ones have memory lifetimes that are rather short and grow only logarithmically in the number of synapses. Attempts to overcome these deficits include "complex" models of synaptic plasticity in which synapses possess internal states governing the expression of syn...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00956
更新日期:2017-06-01 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::Physiological signals such as neural spikes and heartbeats are discrete events in time, driven by continuous underlying systems. A recently introduced data-driven model to analyze such a system is a state-space model with point process observations, parameters of which and the underlying state sequence are simultaneou...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2010.07-09-1047
更新日期:2010-08-01 00:00:00
abstract::The visual systems of many mammals, including humans, are able to integrate the geometric information of visual stimuli and perform cognitive tasks at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at the single cell level ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00738
更新日期:2015-06-01 00:00:00
abstract::We have studied some of the design trade-offs governing visual representations based on spatially invariant conjunctive feature detectors, with an emphasis on the susceptibility of such systems to false-positive recognition errors-Malsburg's classical binding problem. We begin by deriving an analytical model that make...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015574
更新日期:2000-04-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00144
更新日期:2011-07-01 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::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
pub_type: 杂志文章
doi:10.1162/neco.2007.19.3.706
更新日期:2007-03-01 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::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::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::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
pub_type: 杂志文章
doi:10.1162/NECO_a_00829
更新日期:2016-05-01 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 propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00631
更新日期:2014-09-01 00:00:00
abstract::The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This a...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660252741194
更新日期:2002-02-01 00:00:00
abstract::We develop theoretical foundations of resonator networks, a new type of recurrent neural network introduced in Frady, Kent, Olshausen, and Sommer (2020), a companion article in this issue, to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures. Given a composite vector formed...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01329
更新日期:2020-12-01 00:00:00
abstract::A fast and accurate computational scheme for simulating nonlinear dynamic systems is presented. The scheme assumes that the system can be represented by a combination of components of only two different types: first-order low-pass filters and static nonlinearities. The parameters of these filters and nonlinearities ma...
journal_title:Neural computation
pub_type: 信件
doi:10.1162/neco.2008.04-07-506
更新日期:2008-07-01 00:00:00
abstract::We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating brain signals conditioned on the stimulus events....
journal_title:Neural computation
pub_type: 信件
doi:10.1162/NECO_a_00066
更新日期:2011-01-01 00:00:00
abstract::Intracortical brain computer interfaces can enable individuals with paralysis to control external devices through voluntarily modulated brain activity. Decoding quality has been previously shown to degrade with signal nonstationarities-specifically, the changes in the statistics of the data between training and testin...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01129
更新日期:2018-11-01 00:00:00
abstract::Neural associative memories are perceptron-like single-layer networks with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous work optimized the memory capacity for various models of synaptic learning: linear Hopfield-type rules, the Willshaw model employ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00127
更新日期:2011-06-01 00:00:00
abstract::Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00558
更新日期:2014-03-01 00:00:00
abstract::We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.06-07-548
更新日期:2009-06-01 00:00:00