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PubMed2005

An analytical study of the number of steady states in gene regulatory networks.

Herein, I proposed a model for gene networks and studied the steady states in the dynamics both by numerical and analytical methods. In this model, mRNA and protein levels change continuously with time; each gene alters transcriptional regulation depending on the concentration of transcription factors. The dynamical behavior of continuous model is quite complex and different from that of the discrete model, the Boolean network. Large portion of steady states of this model can be classified into three types. The rough structure of gene interactions, which corresponds to Boolean function, is sufficient to predict the expression level of each gene in these types of steady states. I also determined the expected numbers of two major types of steady states observed in a randomly generated gene network. The results obtained from these formulae contradict previously accepted belief. The results are that neither gene number nor connectivity between genes increases the expected number of steady states in a random gene network. The number of steady states is very small. The number of self-regulatory genes, however, effectively increases the number of steady states in a network. These results imply that increases in gene number may not be the direct driving force for the evolution of a variety of different cell types within organisms. Instead, the number of self-regulatory genes may significantly increase cellular diversity.

Animals↗
PubMed1996

Artificial neural network to assist psychiatric diagnosis.

BACKGROUND: Artificial Neural Network (ANN), as a potential powerful classifier, was explored to assist psychiatric diagnosis of the Composite International Diagnostic Interview (CIDI). METHOD: Both Back-Propagation (BP) and Kohonen networks were developed to fit psychiatric diagnosis and programmed (using 60 cases) to classify neurosis, schizophrenia and normal people. The programmed networks were cross-tested using another 222 cases. All subjects were randomly selected from two mental hospitals in Beijing. RESULTS: Compared to ICD-10 diagnosis by psychiatrists, the overall kappa of BP network was 0.94 and that of Kohonen was 0.88 (both P < 0.01). In classifying patients who were difficult to diagnose, the kappa of BP was 0.69 (P < 0.01). ANN-assisted CIDI was compared with expert system assisted CIDI (kappa = 0.72-0.76); ANN was more powerful than a traditional expert system. CONCLUSION: ANN might be used to improve psychiatric diagnosis.

Adult↗
PubMed2000

A computer simulation of recurrent, excitatory networks of sensory neurons of the gut in guinea-pig.

Intrinsic sensory neurons of the intestine are connected together to form a recurrent network. They interact by slow excitatory post synaptic potentials (EPSPs), which have a complex dependence on the pattern of input. These networks are unstable and unable to give graded responses to sensory input without some form of inhibition, but inhibitory synaptic potentials are rare in this system. Intrinsic sensory neurons have a characteristic after-hyperpolarization (AHP), but this is depressed during slow EPSPs. To test whether AHPs can provide the inhibition necessary for stability, AHPs, slow EPSPs and their interactions were included in a computer simulation of realistic sensory neuron networks. Residual AHPs as small as 1% of control were found to lead to stable networks capable of giving graded responses.

Action Potentials↗
PubMed1998

Knowledge acquisition, accessibility, and use in person perception and stereotyping: simulation with a recurrent connectionist network.

Connectionist models contrast in many ways with the symbolic models that have traditionally been applied within social psychology. In this article the authors apply an autoassociative connectionist model originally developed by J. L. McClelland and D. E. Rumelhart (1986) to reproduce several well-replicated and theoretically important phenomena related to person perception and stereotyping. These phenomena are exemplar-based inference, group-based stereotyping, the simultaneous application of several stereotypes to generate emergent characteristics, and the effects of recency and frequency of prior exposures on accessibility (the probability of a representation's use). Though many of these phenomena are explained by current theories in social psychology, the simulation contributes to parsimony and theoretical integration by showing that a single, very simple mechanism can generate them all. The model also predicts a new phenomenon--rapid recovery of accessibility after it has declined to zero.

Computer Simulation↗
PubMed2005

Blind equalization using a predictive radial basis function neural network.

In this paper, we propose a novel blind equalization approach based on radial basis function (RBF) neural networks. By exploiting the short-term predictability of the system input, a RBF neural net is used to predict the inverse filter output. It is shown here that when the prediction error of the RBF neural net is minimized, the coefficients of the inverse system are identical to those of the unknown system. To enhance the identification performance in noisy environments, the improved least square (ILS) method based on the concept of orthogonal distance to red the estimation bias caused by additive measurement noise is proposed here to perform the training. The convergence rate of the ILS learning is analyzed, and the asymptotic mean square error (MSE) of the proposed predictive RBF identification method is derived theoretically. Monte Carlo simulations show that the proposed method is effective for blind system identification. The new blind technique is then applied to two practical applications: equalization of real-life radar sea clutter collected at the east coast of Canada and deconvolution of real speech signals. In both cases, the proposed blind equalization technique is found to perform satisfactory even when the channel effects and measurement noise are strong.

Algorithms↗
PubMed2002

Combining neural network predictions for medical diagnosis.

We present our results from combining the predictions of an ensemble of neural networks for the diagnosis of hepatobiliary disorders. To improve the accuracy of the diagnosis, we train the second level networks using the outputs of the first level networks as input data. The second level networks achieve an accuracy that is higher than that of the individual networks in the first level. Compared to the simple method which averages the outputs of the first level networks, the second level networks are also more accurate. We discuss how the overall predictive accuracy can be improved by introducing bias during the training of the level one networks.

Bias↗
PubMed2005

Growing Bayesian network models of gene networks from seed genes.

MOTIVATION: For the last few years, Bayesian networks (BNs) have received increasing attention from the computational biology community as models of gene networks, though learning them from gene-expression data is problematic. Most gene-expression databases contain measurements for thousands of genes, but the existing algorithms for learning BNs from data do not scale to such high-dimensional databases. This means that the user has to decide in advance which genes are included in the learning process, typically no more than a few hundreds, and which genes are excluded from it. This is not a trivial decision. We propose an alternative approach to overcome this problem. RESULTS: We propose a new algorithm for learning BN models of gene networks from gene-expression data. Our algorithm receives a seed gene S and a positive integer R from the user, and returns a BN for the genes that depend on S such that less than R other genes mediate the dependency. Our algorithm grows the BN, which initially only contains S, by repeating the following step R + 1 times and, then, pruning some genes; find the parents and children of all the genes in the BN and add them to it. Intuitively, our algorithm provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance. We prove that our algorithm is correct under the faithfulness assumption. We evaluate our algorithm on simulated and biological data (Rosetta compendium) with satisfactory results.

Algorithms↗
PubMed2004

A negentropy minimization approach to adaptive equalization for digital communication systems.

In this paper, we introduce and investigate a new adaptive equalization method based on minimizing approximate negentropy of the estimation error for a finite-length equalizer. We consider an approximate negentropy using nonpolynomial expansions of the estimation error as a new performance criterion to improve performance of a linear equalizer based on minimizing minimum mean squared error (MMSE). Negentropy includes higher order statistical information and its minimization provides improved converge, performance and accuracy compared to traditional methods such as MMSE in terms of bit error rate (BER). The proposed negentropy minimization (NEGMIN) equalizer has two kinds of solutions, the MMSE solution and the other one, depending on the ratio of the normalization parameters. The NEGMIN equalizer has best BER performance when the ratio of the normalization parameters is properly adjusted to maximize the output power(variance) of the NEGMIN equalizer. Simulation experiments show that BER performance of the NEGMIN equalizer with the other solution than the MMSE one has similar characteristics to the adaptive minimum bit error rate (AMBER) equalizer. The main advantage of the proposed equalizer is that it needs significantly fewer training symbols than the AMBER equalizer. Furthermore, the proposed equalizer is more robust to nonlinear distortions than the MMSE equalizer.

Algorithms↗
PubMed2000

Serial processing in reading aloud: no challenge for a parallel model.

K. Rastle and M. Coltheart (1999) challenged parallel models of reading by showing that the cost of irregularity in low-frequency exception words was modulated by the position of the irregularity in the word. This position-of-irregularity effect was taken as strong evidence of serial processing in reading. This article refutes Rastle and Coltheart's theoretical conclusions in 3 ways: First, a parallel model, the connectionist dual process model (M. Zorzi, G. Houghton, & B. Butterworth, 1998b), produces a position-of-irregularity effect. Second, the supposed serial effect can be reduced to a position-specific grapheme-phoneme consistency effect. Third, the position-of-irregularity effect vanishes when the experimental data are reanalyzed using grapheme-phoneme consistency as the covariate. This demonstration has broader implications for studies aiming at adjudicating between models: Strong inferences should be avoided until the computational models are actually tested.

Attention↗
PubMed2004

Efficient chemical kinetic modeling through neural network maps.

An approach to modeling nonlinear chemical kinetics using neural networks is introduced. It is found that neural networks based on a simple multivariate polynomial architecture are useful in approximating a wide variety of chemical kinetic systems. The accuracy and efficiency of these ridge polynomial networks (RPNs) are demonstrated by modeling the kinetics of H(2) bromination, formaldehyde oxidation, and H(2)+O(2) combustion. RPN kinetic modeling has a broad range of applications, including kinetic parameter inversion, simulation of reactor dynamics, and atmospheric modeling.

Algorithms↗
PubMed2006

Dynamics of moment neuronal networks.

A theoretical framework is developed for moment neuronal networks (MNNs). Within this framework, the behavior of the system of spiking neurons is specified in terms of the first- and second-order statistics of their interspike intervals, i.e., the mean, the variance, and the cross correlations of spike activity. Since neurons emit and receive spike trains which can be described by renewal--but generally non-Poisson--processes, we first derive a suitable diffusion-type approximation of such processes. Two approximation schemes are introduced: the usual approximation scheme (UAS) and the Ornstein-Uhlenbeck scheme. It is found that both schemes approximate well the input-output characteristics of spiking models such as the IF and the Hodgkin-Huxley models. The MNN framework is then developed according to the UAS scheme, and its predictions are tested on a few examples.

Action Potentials↗
PubMed2001

Efficient temporal processing with biologically realistic dynamic synapses.

Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadratic filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analyses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.

Algorithms↗
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↗
PubMed2005

Gabor wavelet associative memory for face recognition.

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.

Algorithms↗
PubMed2003

Modeling and production of robot trajectories using the Temporal Parametrized Self Organizing Maps.

In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.

Algorithms↗
PubMed2005

The role of VMPC in metamemorial judgments of content retrievability.

Making judgments about the retrievability of information is a critical part of the metamemory processes engaged during remembering. A recent study of patients with frontal lesions suggests that ventral medial prefrontal cortex (VMPC) plays a critical role in such judgments [Schnyer, D. M., Verfaellie, M., Alexander, M. P., Lafleche, G., Nicholls, L., & Kaszniak, A. W. A role for right medial prefrontal cortex in accurate feeling of knowing judgments: Evidence from patients with lesions to frontal cortex. Neuropsychologia, 42, 957-966, 2004]. The observed impairment was thought to reflect an inability to determine the accessibility of memory contents. To further examine the neuroanatomical basis of content accessibility assessment, we used fMRI in an episodic feeling-of-knowing (FOK) paradigm. Participants were asked to make trial-by-trial predictions about the retrievability of the final word that completed studied sentences and then to select the correct completion from among alternatives. Results indicated that the VMPC is engaged during accurate FOK judgments and its activation is modulated by retrieval rating. Structural equations modeling supported the notion that VMPC, as part of a broader left hemisphere network involved in memory retrieval, monitors the output of the retrieval process. More generally, VMPC may participate in metacognitive processes that allow for the comparison of available data against an internal model.

Adult↗
PubMed1999

Timing regulation in a network reduced from voltage-gated equations to a one-dimensional map.

We discuss a method by which the dynamics of a network of neurons, coupled by mutual inhibition, can be reduced to a one-dimensional map. This network consists of a pair of neurons, one of which is an endogenous burster, and the other excitable but not bursting in the absence of phasic input. The latter cell has more than one slow process. The reduction uses the standard separation of slow/fast processes; it also uses information about how the dynamics on the slow manifold evolve after a finite amount of slow time. From this reduction we obtain a one-dimensional map dependent on the parameters of the original biophysical equations. In some parameter regimes, one can deduce that the original equations have solutions in which the active phase of the originally excitable cell is constant from burst to burst, while in other parameter regimes it is not. The existence or absence of this kind of regulation corresponds to qualitatively different dynamics in the one-dimensional map. The computations associated with the reduction and the analysis of the dynamics includes the use of coordinates that parameterize by time along trajectories, and "singular Poincaré maps" that combine information about flows along a slow manifold with information about jumps between branches of the slow manifold.

Animals↗