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PubMed1993

An application of mapping neural networks and a digital signal processor for cochlear neuroprostheses.

Cochlear neuroprostheses strive to restore the sensation of hearing to patients with a profound sensorineural deafness. They exhibit a stimulation of the surviving auditory nerve neurons by electrical currents delivered through electrodes placed on or within the cochlea. The present article describes a new method for an efficient derivation of the required information from the incoming speech signal necessary for the implant stimulation. Also some realization aspects of the new approach are addressed. In the new strategy, a multilayer neural network is employed in the formant frequency estimation having some suitable speech signal descriptors as particular input signals. The proposed method allows us a fast formant frequency estimation necessary for the implant stimulation. With the developed strategy, the prosthesis can be adjusted to the environment which the patient is supposed to live in. Moreover, the neural network concept offers us an alternative for dealing with the areas of neural loss or "holes" in the frequency map of the patient's ear.

Cochlear Implants↗

PubMed1998

Modelling behavioral syndromes using Bayesian networks.

In this paper Bayesian networks modelling is applied to a multidimensional model of depression. The characterization of the probabilistic model exploits expert knowledge to associate latent concentrations of neurotransmitters and symptoms. An evolution perspective is also considered. Specific criteria are introduced to detect the influence of the latent variable on the observation of symptoms. The Bayesian analysis is carried out using Gibbs sampling technique which is implemented in the BUGS software. The estimation phase leads to the selection of symptoms entering into the definition of behavioral syndromes. Results on real data are discussed. The last section deals with simulation experiments. Simulation results confirm our methodological choices. Results of the paper can enlarge to the central problem of the management of latent variables in Bayesian networks modelling.

Artificial Intelligence↗

PubMed2000

Intrinsic dynamics in neuronal networks. I. Theory.

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

Action Potentials↗

PubMed2003

Global asymptotic stability of a class of dynamical neural networks.

The dynamics of cortical cognitive maps developed by self-organization must include the aspects of long and short-term memory. The behavior of the network is such characterized by an equation of neural activity as a fast phenomenon and an equation of synaptic modification as a slow part of the neural biologically relevant system. We present new stability conditions for analyzing the dynamics of a biological relevant system with different time scales based on the theory of flow invariance. We prove the existence and uniqueness of the equilibrium, and give a quadratic-type Lyapunov function for the flow of a competitive neural system with fast and slow dynamic variables and thus prove the global stability of the equilibrium point.

Algorithms↗

PubMed2003

Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality.

Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.

Animals↗

PubMed2002

NETASA: neural network based prediction of solvent accessibility.

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.

Algorithms↗

PubMed1994

Neural networks and psychiatry: candidate applications in clinical decision making.

Neural networks comprise a fundamentally new type of computer system inspired by the functioning of neurons in the brain. Such networks are good at solving problems that involve pattern recognition and categorisation. An important difference between a neural network and a traditional computer system is that in developing an application, a neural network is not programmed; instead, it is trained to solve a particular type of problem. This ability to learn to solve a problem makes neural networks adaptable to solving a wide variety of problems, some of which have proved intractable using a traditional computing approach. Neural networks are particularly suited to tasks involving the categorisation of patterns of information, such as is required in diagnosis and clinical decision making. In the last three years reports of applications involving neural networks have begun to appear in the medical literature, and these are described in this paper. However, a comprehensive search of the literature has shown that there have not as yet been reports of any applications in psychiatry. This paper discusses the nature of clinical decision making, outlines the sorts of problems in psychiatry which neural networks applications might be developed to address, and gives examples of candidate applications in clinical decision making.

Adult↗

PubMed1995

[Accuracy of artificial neural networks in radiological differential diagnosis of solitary bone lesions].

PURPOSE: To evaluate the usefulness of artificial neural networks (ANN) for differential diagnosis of bone tumours and tumour-like lesions. MATERIAL AND TECHNIQUE: Different ANN were designed to distinguish between 23 types of bone lesions and to classify these lesions as benign or malignant on the basis of 28 items of clinical and radiographic information. Training of the feed-forward networks with a back-propagation algorithm was performed using either 46 hypothetical examples or 115 real cases. The data base for testing the different ANN included 115 clinical cases (5 cases for each possible diagnosis), which were different from the examples used for training. The decision performance of the ANN was evaluated by means of ROC analysis. Results were compared to the performance of an experienced radiologist. RESULTS: The radiologist was significantly superior in finding the correct diagnosis (69/115 vs. 41/115; p < 0.001) and the area under his ROC curve (referring to the discrimination between benign and malignant lesions) was slightly larger than that of two different ANN (0.973 vs. 0.945 and 0.973 vs. 0.962 resp.; differences not significant). CONCLUSION: ANN may be potentially useful to distinguish between malignant and benign lesions, but their performance in differentiating between 23 diagnoses must be improved significantly.

Algorithms↗

PubMed2003

[Predict SARS infection with the small world network model].

We report of our numerical simulation of SARS infection dynamics with the small world network model. The negative feedback mechanism and the effect of information flow are added in the model. The simulation fits well with the observed data. The main results of our simulation are that the feedback mechanism can effectively slow down the SARS infecting rate, but it may cause sustained oscillations in number of infection cases. Moreover, keeping the transparency of information is a key factor to resist SARS in the society.

Computer Simulation↗

PubMed1998

A functional model of some Parkinson's disease symptoms using a Guided Propagation Network.

This paper presents a computational model of Parkinson's Disease (PD) symptoms. Based on psychophysiological data, the underlying system (Guided Propagation Network) implements coincidence detection between internal flows and stimuli, and can be dynamically controlled for representing the action of neuromodulators such as dopamine (DA). By modelling the DA deficit involved in PD through a decrease of response thresholds in the production modules of a GPN, four symptoms are observed in experiments carried out on a computer simulation, and then attributed to a lack of synchrony between 'proprioceptive stimuli' and internal flows: reduced intensity, increased rate, saccades and spontaneous repetitions.

Computer Simulation↗

PubMed2000

Synthesis of generalized algorithms for the fast computation of synaptic conductances with Markov kinetic models in large network simulations.

Markovkinetic models constitute a powerful framework to analyze patch-clamp data from single-channel recordings and model the dynamics of ion conductances and synaptic transmission between neurons. In particular, the accurate simulation of a large number of synaptic inputs in wide-scale network models may result in a computationally highly demanding process. We present a generalized consolidating algorithm to simulate efficiently a large number of synaptic inputs of the same kind (excitatory or inhibitory), converging on an isopotential compartment, independently modeling each synaptic current by a generic n-state Markov model characterized by piece-wise constant transition probabilities. We extend our findings to a class of simplified phenomenological descriptions of synaptic transmission that incorporate higher-order dynamics, such as short-term facilitation, depression, and synaptic plasticity.

Algorithms↗

PubMedDate not supplied

Variable site-occupancy classification of N-linked glycosylation using artificial neural networks.

A novel neural-network-based model has been developed for the prediction of N-linked glycosylation characteristics related to glycosylation site-occupancy. Intracellular oligosaccharide transfer to a polypeptide is known to be either robust or dependent upon culture conditions during pharmaceutical production. This glycan attachment is classified by the model as robust or variable and is based on an input of the polypeptide primary sequence around the site of glycosylation. The glycosylation model utilizes multiple recurrent neural networks followed by a perceptron classifier. The input length of the polypeptide chain around the site of glycosylation (glycosylation window) was optimized through multiple independent training sessions. Incorporation of five residues prior (n - 5) to the site of glycosylation (n) and four residues beyond (n + 4) the glycan attachment site led to optimal network performance. The size of the glycosylation window for site-occupancy determination is much larger than has been previously reported. This model was developed to evaluate the effects of theoretical polypeptide mutations on glycosylation site-occupancy characteristics. Following correct prediction of the model testing data set, 20 independent networks were used to predict site-occupancy characteristics of wild-type and mutants of the rabies virus glycoprotein (rgp). Simulation results strongly correlated with previously published experimental results (Kasturi, L.; Hegang, C.; Shakin-Eshleman, S. H. Regulation of N-linked core glycosylation: use of a site-directed mutagenesis approach to identify Asn-Xaa-Ser/Thr sequons that are poor oligosacchride acceptors. Biochem. J. 1997, 323, 415-419. Mellquist, J. L.; Kasturi, L.; Spitalnik, S. L.; Shakin-Eshleman, S. H. The amino acid following an Asn-X-Ser/Thr sequon is an important determinant of N-linked core glycosylation efficiency. Biochemistry 1998, 37, 6833-6837). Further simulations on purely theoretical sequences suggested that influences of charged residues were a subset of multiple mechanisms in the determination of glycosylation site-occupancy.

Binding Sites↗

PubMedDate not supplied

Phase dynamics of complex-valued neural networks and its application to traffic signal control.

Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. Then the phase dynamics is described as a coupled system of phase oscillators with a pair-wise sinusoidal interaction. Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.

Brain↗

PubMed2002

Computational antisense oligo prediction with a neural network model.

MOTIVATION: The expression of a gene can be selectively inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time consuming and costly. So far computational models have been based exclusively on RNA structure prediction or motif searches while ignoring information from other aspects of AO design into the model. RESULTS: We present a computational model for AO prediction based on a neural network approach using a broad range of input parameters. Collecting sequence and efficacy data from AO scanning experiments in the literature generated a database of 490 AO molecules. Using a set of derived parameters based on AO sequence properties we trained a neural network model. The best model, an ensemble of 10 networks, gave an overall correlation coefficient of 0.30 (p=10(-8)). This model can predict effective AOs (>50% inhibition of gene expression) with a success rate of 92%. Using these thresholds the model predicts on average 12 effective AOs per 1000 base pairs, making it a stringent yet practical method for AO prediction.

Algorithms↗

PubMed2003

A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis.

We develop a probabilistic network model over image spaces and demonstrate its broad utility in mammographic image analysis, particularly with respect to computer-aided diagnosis. The model employs a multi-scale pyramid decomposition to factor images across scale and a network of tree-structured hidden variables to capture long-range spatial dependencies. This factoring makes the computation of the density functions local and tractable. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters are found with maximum likelihood estimation using the expectation-maximization algorithm. The utility of the model is demonstrated for three applications: (1) detection of mammographic masses for computer-aided diagnosis; (2) qualitative assessment of model structure through mammographic synthesis; and (3) compression of mammographic regions of interest.

Female↗

PubMed1992

Relaxation network for Gabor image decomposition.

The so-called "simple cells" in layer IV of feline primary visual cortex have been shown to have Gabor function spatial receptive field profiles (RFP's). Since Gabor functions are not mutually orthogonal, the decomposition of an image into Gabor function coefficients is usually performed by minimising some measure of the error between the original image and that reconstructed from the coefficients. A cortical relaxation model is proposed which performs this minimisation implicitly, and is used to examine the biological relevance and feasibility of reconstruction error minimisation.

Animals↗

PubMed2003

Understanding complex signaling networks through models and metaphors.

Signaling networks are complex both in terms of the chemical and biophysical events that underlie them, and in the sheer number of interactions. Computer models are powerful tools to deal with both aspects of complexity, but their utility goes beyond simply replicating signaling events in silicon. Their great advantage is as a tool to understanding. The completeness of the description demanded by computer models highlights gaps in knowledge. The quantitative description in models facilitates a mapping between different kinds of analysis methods for complex systems. Systems analysis methods can highlight stable states of signaling networks and describe the transitions between them. Modeling also reveals functional similarities between signaling network properties and other well-understood systems such as electronic devices and neural networks. These suggest various metaphors as a tool to understanding. Based on such descriptions, it is possible to regard signaling networks as systems that decode complex inputs in time, space and chemistry into combinatorial output patterns of signaling activity. This would provide a natural interface to the combinatorial input patterns required by genetic circuits. Thus, a combination of computer modeling methods to capture the complexity and details, and useful abstractions revealed by these models, is necessary to achieve both rigorous description as well as human understanding.

Algorithms↗

PubMed2004

A robust method for distinguishing between learned and spurious attractors.

Hopfield/constraint satisfaction type networks can be used to learn (autoassociate) patterns. Random inputs to the network will sometimes converge on states which are learned patterns, and sometimes converge on states which are unlearned/spurious. It would be useful for many reasons to be able to tell whether or not a given state was learned or spurious. In this paper we present a robust and general method, based on 'energy profiles', which allows us to make this distinction. We briefly describe related research, and note links with the study of recall, recognition and familiarity in the psychological literature.

Algorithms↗