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At least 1,279 records · Page 71Linked to original sources
PubMed1992

Some tools for the diffusion of biomedical information using research networks.

Research and academic computer networks provide e-mail and other services to all members of participating institutions. Their usage by biomedical researchers and clinicians is still limited because of several reasons, including limited awareness of the available network resources. An increased use of these networks within the biomedical community would allow fast, effective communications and convenient remote access to information sources. As an example and pilot study, we prepared two network tools to make some information services maintained by our institution also accessible through e-mail. Both tools were implemented using PMDF e-mail software on a DEC MicroVAX connected to the Italian academic and research network (GARR), which is linked to the U.S. Internet. A network server takes care of automatic distribution of documents (files) reporting results of an oncology research/education project. An information server provides for semiautomated support of a consulting service on use of drugs. The feasibility of implementing these tools, based on existing software, further illustrates the potential usefulness of research computer networks for the dissemination of biomedical information.

Computer Communication Networks↗
PubMed1996

Neural network computer simulation of medical aerosols.

Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols.

Aerosols↗
PubMedDate not supplied

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks.

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals.

Algorithms↗
PubMed1995

Neural network modeling of a dolphin's sonar discrimination capabilities.

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral-only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise-free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

Animals↗
PubMed2006

Neuronal computations with stochastic network states.

Neuronal networks in vivo are characterized by considerable spontaneous activity, which is highly complex and intrinsically generated by a combination of single-cell electrophysiological properties and recurrent circuits. As seen, for example, during waking compared with being asleep or under anesthesia, neuronal responsiveness differs, concomitant with the pattern of spontaneous brain activity. This pattern, which defines the state of the network, has a dramatic influence on how local networks are engaged by inputs and, therefore, on how information is represented. We review here experimental and theoretical evidence of the decisive role played by stochastic network states in sensory responsiveness with emphasis on activated states such as waking. From single cells to networks, experiments and computational models have addressed the relation between neuronal responsiveness and the complex spatiotemporal patterns of network activity. The understanding of the relation between network state dynamics and information representation is a major challenge that will require developing, in conjunction, specific experimental paradigms and theoretical frameworks.

Anesthesia↗
PubMed1996

Neural network differentiation of optic neuritis and anterior ischaemic optic neuropathy.

AIMS: The efficacy of an artificial intelligence technique, neural network analysis, was examined in differentiating two optic neuropathies with overlapping clinical profiles-idiopathic optic neuritis (ON) and non-arteritic anterior ischaemic optic neuropathy (AION). METHODS: A neural network was trained with data from 116 patients with 'gold standard' diagnoses of ON or AION. It was then tested with data from 128 patients with presumed ON or AION, and the correlation of the network's diagnosis with that of expert clinicians tabulated. RESULTS: The network agreed with the clinicians on 97.8% (88 of 90) of the patients with presumed ON and 94.7% (36 of 38) of the patients with presumed AION. Youth, female sex, better initial acuity, a central scotoma, subsequent improvement in acuity, or progressive disease biased the network towards a diagnosis of ON, while advanced age, male sex, presence of hypertension, poor initial acuity, an altitudinal field defect, disc oedema, or less improvement in acuity biased the network towards a diagnosis of AION. CONCLUSION: Neural network analysis is a useful technique for classification of optic neuropathies, particularly where there is overlap of clinical findings.

Adult↗
PubMed2000

VLSI implementation of neural networks.

Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.

Computer Simulation↗
PubMed2002

Selecting neural networks for a committee decision.

To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.

Artificial Intelligence↗
PubMed1994

Optimization dynamics for partitioned neural networks.

Given a relaxation-based neural network and a desired partition of the neurons in the network into modules with relatively slow communication between modules, we investigate relaxation dynamics for the resulting partitioned neural network. In particular, we show how the slow inter-module communication channels can be modeled by means of certain transformations of the original objective function which introduce new state variables for the inter-module communication links. We report on a parallel implementation of the resulting relaxation dynamics, for a two-dimensional image segmentation network, using a network of workstations. Experiments demonstrate a functional and efficient parallelization of this neural network algorithm. We also discuss implications for analog hardware implementations of relaxation networks.

Kinetics↗
PubMed1995

Recognition of Telugu characters using neural networks.

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Algorithms↗
PubMed1996

Nonlinear time series analysis by neural networks: a case study.

This paper presents a neural network approach to time-series analysis of a univariate nonlinear system. Feedforward networks are studied, and an appropriate network size is determined by different criteria computed on the basis of the performance of the models on the training and test sets. The analysis and conclusions drawn are supported by studies of the phase portraits of the models. By a proper choice of network size, the problems of over-parameterization are demonstrated to be avoided. The overfitting observed for larger networks is analyzed and the underlying reasons for their worse generalization capabilities are explained. Finally, some observations are made on the approximation provided by an oversized network with weights determined by an incomplete (interrupted) training and that of the optimal-sized network.

Cybernetics↗
PubMed1997

Radar image segmentation using self-adapting recurrent networks.

This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.

Doppler Effect↗
PubMed1997

Understanding time series networks: a case study in rule extraction.

A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar-Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.

Artificial Intelligence↗
PubMed1999

A neural network methodology of quadratic optimization.

According to the basic optimization principle of artificial neural networks, a novel kind of neural network model for solving the quadratic programming problem is presented. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. The stability and convergency of the neural network is investigated and the strategy of the neural optimization is discussed. The feasibility of the neural network method is verified with the computation examples. Results of the simulation of the neural network to solve optimum problems are presented to illustrate the computational power of the neural network method.

Computer Simulation↗
PubMed1999

Sequential RAM-based neural networks: learnability, generalisation, knowledge extraction, and grammatical inference.

A fundamental question in the field of artificial neural networks is what set of problems a given class of networks can perform (computability). Such a problem can be made less general, but no less important, by asking what these networks could learn by using a given training procedure (learnability). The basic purpose of this paper is to address the learnability problem. Specifically, it analyses the learnability of sequential RAM-based neural networks. The analytical tools used are those of Automata Theory. In this context, this paper establishes which class of problems and under what conditions such networks, together with their existing learning rules, can learn and generalize. This analysis also yields techniques for both extracting knowledge from and inserting knowledge into the networks. The results presented here, besides helping in a better understanding of the temporal behaviour of sequential RAM-based networks, could also provide useful insights for the integration of the symbolic/connectionist paradigms.

Algorithms↗
PubMed1996

Neural networks in ventilation-perfusion imaging.

PURPOSE: To optimize the performance of artificial neural networks in the prediction of pulmonary embolism from ventilation-perfusion (V-P) scans. MATERIALS AND METHODS: Neural networks were constructed with a set of V-P scan criteria that included sharpness and completeness of perfusion defects and involved quantification of abnormalities by using a continuous numeric scale. Several network parameters were systematically varied. Networks were trained with 150 cases and tested with 30 different cases. Findings were compared with those of pulmonary angiography. RESULTS: Networks capable of performing as well as experienced nuclear medicine physicians could be constructed with few V-P scan features. A brief training period was optimal (50-100 iterations). Further training diminished network performance. CONCLUSION: Effective neural networks can be constructed by using a limited number of unconventional V-P scan features. Several parameters can be adjusted to optimize performance.

Adolescent↗
PubMed1996

Neural networks in ventilation-perfusion imaging. Part II. Effects of interpretive variability.

PURPOSE: To evaluate the usefulness of a neural network developed by one physician and used by another. MATERIALS AND METHODS: Intra- and interobserver variability were analyzed in image categorization of ventilation-perfusion (V-P) scans. This information was used to estimate network performance when it was used by a physician who did not train the network. RESULTS: Network training was optimized by using input parameters that demonstrated both individually high correlations with pulmonary embolism and good reproducibility in multiple interpretations. CONCLUSION: Potential variability exists in the performance of a network when it is supplied with input data by different physicians. The clinical usefulness of a network depends heavily on the similarity of interpretive styles between the network trainer and the user.

Humans↗
PubMed1993

The role of interneurons in controlling the tail-withdrawal reflex in Aplysia: a network model.

1. The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation. 2. A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. These interneurons had properties mimicking those of the recently described interneuron LP117, receiving excitatory input from pleural sensory neurons and evoking a biphasic excitatory postsynaptic potential (EPSP) in pedal motor neurons (Cleary and Byrne 1993). The three-layer model could account for long-duration responses in motor neurons. 3. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity was incorporated into the three-layer model by altering the magnitudes of conductance changes evoked in motor neurons and interneurons by presynaptic sensory neurons. In these simulations the excitatory interneurons converted an amplitude-coded input into an amplitude- and duration-coded output, allowing the three-layer network to support a large range of output amplitudes and durations. 4. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia. Multiple sites of control over the response of the network would likely allow a more diverse repertoire of responses.

Animals↗