Network's Ability (network + ability)

Distribution by Scientific Domains

Selected Abstracts

Dynamic Self-Organization and Early Lexical Development in Children

Ping Li
Abstract In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in a non-monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early lexical development. [source]

A learning rule for place fields in a cortical model: Theta phase precession as a network effect

HIPPOCAMPUS, Issue 7 2005
Silvia Scarpetta
Abstract We show that a model of the hippocampus introduced recently by Scarpetta et al. (2002, Neural Computation 14(10):2371,2396) explains the theta phase precession phenomena. In our model, the theta phase precession comes out as a consequence of the associative-memory-like network dynamics, i.e., the network's ability to imprint and recall oscillatory patterns, coded both by phases and amplitudes of oscillation. The learning rule used to imprint the oscillatory states is a natural generalization of that used for static patterns in the Hopfield model, and is based on the spike-time-dependent synaptic plasticity, experimentally observed. In agreement with experimental findings, the place cells' activity appears at consistently earlier phases of subsequent cycles of the ongoing theta rhythm during a pass through the place field, while the oscillation amplitude of the place cells' firing rate increases as the animal approaches the center of the place field and decreases as the animal leaves the center. The total phase precession of the place cell is lower than 360, in agreement with experiments. As the animal enters a receptive field, the place cells' activity comes slightly less than 180 after the phase of maximal pyramidal cell population activity, in agreement with the findings of Skaggs et al. (1996, Hippocampus 6:149,172). Our model predicts that the theta phase is much better correlated with location than with time spent in the receptive field. Finally, in agreement with the recent experimental findings of Zugaro et al. (2005, Nature Neuroscience 9(1):67,71), our model predicts that theta phase precession persists after transient intrahippocampal perturbation. 2005 Wiley-Liss, Inc. [source]

Nonlinear experimental design using Bayesian regularized neural networks

AICHE JOURNAL, Issue 6 2007
Matthew C Coleman
Abstract Novel criteria for designing experiments for nonlinear processes are presented. These criteria improve on a previous methodology in that they can be used to suggest a batch of new experiments to perform (as opposed to a single new experiment) and are also optimized for discovering improved optima of the system response. This is accomplished by using information theoretic criterion, which also heuristically penalize experiments that are likely to result in low (nonoptimal) results. While the methods may be applied to any type of nonlinear-nonparametric model (radial basis functions and generalized linear regression), they are here exclusively considered in conjunction with Bayesian regularized feedforward neural networks. A focus on the application of rapid process development, and how to use repeated experiments to optimize the training procedures of Bayesian regularized neural networks is shown. The presented methods are applied to three case studies. The first two case studies involve simulations of one and two-dimensional (2-D) nonlinear regression problems. The third case study involves real historical data from bench-scale fermentations generated in our laboratory. It is shown that using the presented criteria to design new experiments can greatly increase a feedforward neural network's ability to predict global optima. 2007 American Institute of Chemical Engineers AIChE J, 2007 [source]

Learning for sustainable development in tourism networks

Minna Halme
The present study investigates learning toward sustainable development in multi-stakeholder public,private networks. The evidence is grounded in the data from six tourism networks in four European countries. The process of cooperation appears more important vis--vis achievements regarding sustainable tourism than the structure of networks. This process will determine the network's ability to become adept at explicating tacit knowledge among its actors, and to develop the network so it can facilitate the creation of sustainability outcomes. A leading public actor may assume a ,teacher's' role in the network. In these instances, the network runs a risk of becoming merely an information dissemination tool. This involves a trap of one-way communication and under-used knowledge utilization opportunities. Receptivity of the teacher actor is low and the partners do not really collaborate. The teacher actor should make a special effort to create feedback loops leading to two-way communication, so that a learning strategy of collaboration can take place. The findings also imply that in some networks with a public leader there is an overly high belief in the ability of information dissemination and classroom education to promote learning about sustainable development although learning about sustainability in the practical level requires concrete results and joint action. Copyright 2001 John Wiley & Sons, Ltd and ERP Environment [source]

Content and Its Vehicles in Connectionist Systems

MIND & LANGUAGE, Issue 3 2007
The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some classificatory networks' ability to generalise to novel samples their correct classification of the samples on which they were trained. [source]