Computing with structured neural networks

Feldman, Jerome A.; Fanty, Mark; Goodard, N.H.

doi:10.1109/2.34

Cited by 81 publications

(25 citation statements)

References 5 publications

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“…Therefore, the model makes it possible to study the interaction of cognitive and linguistic abilities. The aim of the simulation is to study the consequences of architectural and functional constraints (Feldman et al, 1988) in evolutionary neural networks that learn to process (understand) different classes of words (nouns and verbs). The model uses synthetic neuroimaging techniques to examine the internal organization of the neural networks and compares the results with data reported in the literature on language processing in the brain.…”

Section: Language Processing In Natural and Artificial Neural Networkmentioning

confidence: 99%

The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging

Cangelosi

Parisi

2004

Brain and Language

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The paper presents a computational model of language in which linguistic abilities evolve in organisms that interact with an environment. Each individual's behavior is controlled by a neural network and we study the consequences in the network's internal functional organization of learning to process different classes of words. Agents are selected for reproduction according to their ability to manipulate objects and to understand nouns (objects' names) and verbs (manipulation tasks). The weights of the agents' neural networks are evolved using a genetic algorithm. Synthetic brain imaging techniques are then used to examine the functional organization of the neural networks. Results show that nouns produce more integrated neural activity in the sensory processing hidden layer, while verbs produce more integrated synaptic activity in the layer where sensory information is integrated with proprioceptive input. Such findings are qualitatively compared with human brain imaging data that indicate that nouns activate more the posterior areas of the brain related to sensory and associative processing while verbs activate more the anterior motor areas.

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Section: Language Processing In Natural and Artificial Neural Networkmentioning

confidence: 99%

The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging

Cangelosi

Parisi

2004

Brain and Language

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“…Three-layer networks are sufficient to design any nonlinear network [21,[26][27][28][29][30][31][32][33][34][35][36][37][38]. Therefore, in the present study we have used three-layered feed-forward network with one input layer, one hidden and one output layer.…”

Section: Back-propagation Algorithm Of Annmentioning

confidence: 99%

“…In the present case, for convergence of training of the network, a gradient descent algorithm with momentum is used. The details of the ANN theory is explained elsewhere [21,[26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Back-propagation Algorithm Of Annmentioning

confidence: 99%

A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

Kamath

D'souza

Mathew

et al. 2008

Journal of Chemometrics

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Pulsed laser-induced autofluorescence spectra of pathologically certified normal and malignant colonic mucosal tissues were recorded at 325 nm excitation. The spectra were analysed using three different methods for discrimination purposes. First, all the spectra were subjected to the principal component analysis (PCA) and the discrimination between normal and malignant cases were achieved using parameters like, spectral residuals, Mahalanobis distance and scores of factors. Second, to understand the changes in tissue composition between the two classes (normal, and malignant), difference spectrum was constructed by subtracting mean spectrum of calibration set samples from simulated mean of all spectra of any one class (normal/malignant) and in third, artificial neural network (ANN) analysis was carried out on the same set of spectral data by training the network with spectral features like, mean, median, spectral residual, energy, standard deviation, number of peaks for different thresholds (100, 250 and 500) after carrying out 1st-order differentiation of the training set samples and discrimination between normal and malignant conditions were achieved. The specificity and sensitivity were determined in PCA and ANN analyses and they were found to be 100 and 91.3% in PCA, and 100 and 93.47% in ANN, respectively.

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“…Terry Regier [12] has proposed a computational model for spatial prepositions using a method called "constrained connectionism" [6]. The model is trained on the use of various spatial prepositions for static (e.g.…”

Section: Neural Network Models Of Spatial Languagementioning

confidence: 99%

A hybrid neural network and virtual reality system for spatial language processing

Climent-Martínez

Cangelosi

Coventry

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

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Computing with structured neural networks

Cited by 81 publications

References 5 publications

The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging

The processing of verbs and nouns in neural networks: Insights from synthetic brain imaging

A pilot study on colonic mucosal tissues by fluorescence spectroscopy technique: Discrimination by principal component analysis (PCA) and artificial neural network (ANN) analysis

A hybrid neural network and virtual reality system for spatial language processing

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