Abstract:
:Durbin and Willshaw's elastic net algorithm can find good solutions to the TSP. The purpose of this paper is to point out that for certain ranges of parameter values, the algorithm converges into local minima that do not correspond to valid tours. The key parameter is the ratio governing the relative strengths of the two competing terms in the elastic net energy function. Based on recent work by Durbin, Szeliski and Yuille, the parameter regime in which the net may visit some cities twice is examined. Further analysis predicts the regime in which the net may fail to visit some cities at all. Understanding these limitations allows one to select the parameter value most likely to avoid either type of problem. Simulation data support the theoretical work.
journal_name
Neural Computjournal_title
Neural computationauthors
Simmen MWdoi
10.1162/neco.1991.3.3.363subject
Has Abstractpub_date
1991-10-01 00:00:00pages
363-374issue
3eissn
0899-7667issn
1530-888Xjournal_volume
3pub_type
杂志文章abstract::The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300014773
更新日期:2000-12-01 00:00:00
abstract::Previous studies have combined analytical models of stochastic neural responses with signal detection theory (SDT) to predict psychophysical performance limits; however, these studies have typically been limited to simple models and simple psychophysical tasks. A companion article in this issue ("Evaluating Auditory P...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976601750541813
更新日期:2001-10-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00841
更新日期:2016-06-01 00:00:00
abstract::We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00072
更新日期:2011-02-01 00:00:00
abstract::A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input sig...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01057
更新日期:2018-04-01 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::We present a comprehensive framework of search methods, such as simulated annealing and batch training, for solving nonconvex optimization problems. These methods search a wider range by gradually decreasing the randomness added to the standard gradient descent method. The formulation that we define on the basis of th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01089
更新日期:2018-07-01 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
pub_type: 杂志文章
doi:10.1162/neco.1996.8.5.951
更新日期:1996-07-01 00:00:00
abstract::We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moder...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976602320263971
更新日期:2002-09-01 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::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 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00445
更新日期:2013-06-01 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
pub_type: 杂志文章
doi:10.1162/NECO_a_00801
更新日期:2016-02-01 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::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::The pyloric network of the stomatogastric ganglion in crustacea is a central pattern generator that can produce the same basic rhythm over a wide frequency range. Three electrically coupled neurons, the anterior burster (AB) neuron and two pyloric dilator (PD) neurons, act as a pacemaker unit for the pyloric network. ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.1991.3.4.487
更新日期:1991-01-01 00:00:00
abstract::Modeling stereo transparency with physiologically plausible mechanisms is challenging because in such frameworks, large receptive fields mix up overlapping disparities, whereas small receptive fields can reliably compute only small disparities. It seems necessary to combine information across scales. A coarse-to-fine ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00722
更新日期:2015-05-01 00:00:00
abstract::This letter deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01165
更新日期:2019-03-01 00:00:00
abstract::A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers,...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00022
更新日期:2010-10-01 00:00:00
abstract::Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automat...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01039
更新日期:2018-02-01 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
pub_type: 杂志文章
doi:10.1162/089976699300016160
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
pub_type: 杂志文章
doi:10.1162/NECO_a_00789
更新日期:2015-12-01 00:00:00
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
pub_type: 杂志文章
doi:10.1162/neco_a_01009
更新日期:2017-12-01 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::Several integrate-to-threshold models with differing temporal integration mechanisms have been proposed to describe the accumulation of sensory evidence to a prescribed level prior to motor response in perceptual decision-making tasks. An experiment and simulation studies have shown that the introduction of time-varyi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2009.07-08-817
更新日期:2009-08-01 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::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::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::As neural activity is transmitted through the nervous system, neuronal noise degrades the encoded information and limits performance. It is therefore important to know how information loss can be prevented. We study this question in the context of neural population codes. Using Fisher information, we show how informat...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00227
更新日期:2012-02-01 00:00:00
abstract::Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponential...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00852
更新日期:2016-08-01 00:00:00