Cardiothoracic databases: where are we headed?
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Bayes Theorem↗
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Results for “Neural Networks, Computer”
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Hybrid neural modeling of bioprocesses using functional link networks.
The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed, combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model, two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed.
Biology↗
Selective detection of abrupt input changes by integration of spike-frequency adaptation and synaptic depression in a computational network model.
Short-term synaptic depression (STD) and spike-frequency adaptation (SFA) are two basic physiological cortical mechanisms for reducing the system's excitability under repetitive stimulation. The computational implications of each one of these mechanisms on information processing have been studied in detail, but not so the dynamics arising from their combination in a realistic biological scenario. We show here, both experimentally with intracellular recordings from cortical slices of the ferret and computationally using a biologically realistic model of a feedforward cortical network, that STD combined with presynaptic SFA results in the resensitization of cortical synaptic efficacies in the course of sustained stimulation. This fundamental effect is then shown in the computational model to have important implications for the network response to time-varying inputs. The main findings are: (1) the addition of SFA to the model endowed with STD improves the network sensitivity to the degree of synchrony in the incoming inputs; (2) presynaptic SFA, whether slow or fast, combined with STD results in postsynaptic neurons responding briskly to abrupt changes in the presynaptic input current and ignoring sustained stimulation, much more effectively than either SFA or STD alone; (3) for slow presynaptic SFA postsynaptic responses to strong inputs decrease inversely to the input, whereas for weak input current to presynaptic neurons transient postsynaptic responses are strongly facilitated, thus enhancing the system's sensitivity for subtle changes in weak presynaptic inputs. Taken together, these results suggest that in systems designed to respond to temporal aspects of the input, SFA and STD might constitute two necessary, linked elements whose simultaneous interplay is important for the performance of the system.
Action Potentials↗
Invariance priors for Bayesian feed-forward neural networks.
Neural networks (NN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand, this flexibility can cause overfitting and can hamper the generalization of neural networks. Many approaches to regularizing NN have been suggested but most of them are based on ad hoc arguments. Employing the principle of transformation invariance, we derive a general prior in accordance with the Bayesian probability theory for feed-forward networks. An optimal network is determined by Bayesian model comparison, verifying the applicability of this approach. Additionally the prior presented affords cell pruning.
Algorithms↗
Noise-enhanced computation in a model of a cortical column.
Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as memory retrieval of arithmetical multiplication. We show, in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanced in the presence of a stochastic background. The performance is shown to be positively correlated to the susceptibility of the network, defined as its sensitivity to a variation of the mean of its inputs. For nontrivial arithmetic tasks such as multiplication, stochastic resonance is a collective property of the microcircuitry of the model network.
Cerebral Cortex↗
Convergence analysis of cascade error projection--an efficient learning algorithm for hardware implementation.
In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection(CEP) is proposed. The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.
Algorithms↗
Self-organization of topographic mixture networks using attentional feedback.
This article proposes a neural network model of supervised learning that employs biologically motivated constraints of using local, on-line, constructive learning. The model possesses two novel learning mechanisms. The first is a network for learning topographic mixtures. The network's internal category nodes are the mixture components, which learn to encode smooth distributions in the input space by taking advantage of topography in the input feature maps. The second mechanism is an attentional biasing feedback circuit. When the network makes an incorrect output prediction, this feedback circuit modulates the learning rates of the category nodes, by amounts based on the sharpness of their tuning, in order to improve the network's prediction accuracy. The network is evaluated on several standard classification benchmarks and shown to perform well in comparison to other classifiers.
Attention↗
Extracting rules from pruned networks for breast cancer diagnosis.
A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hidden unit activation values. The accuracy of the extracted rules is as high as the accuracy of the pruned network. For the breast cancer diagnosis problem, the concise rules extracted from the network achieve an accuracy rate of more than 95% on the training data set and on the test data set.
Algorithms↗
Neural network designs for poly-beta-hydroxybutyrate production optimization under simulated industrial conditions.
Improvement of the fermentation efficiency of poly-beta-hydroxybutyrate (PHB) may make it competitive with chemically synthesized petroleum-based polymers. One step toward this is optimization of fluid dispersion and the feed rates to a fed-batch bioreactor. In a recent study using a fermentation model, dispersion corresponding to a Peclet number of approximately 20 was shown to maximize the productivity of PHB. Here further improvement has been investigated using neural optimization. A comparison of seven neural topologies has shown that while feed-forward and radial basis neural networks are computationally efficient, recurrent networks generate higher concentrations of PHB. All networks enhanced the productivity by 16-93% over model-based optimization.
Bioreactors↗
An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation, and scatter compensation.
A spatially variant technique for quantitative single photon emission computed tomographic (SPECT) image reconstruction using an artificial neural network (ANN) is presented. This network was developed to simultaneously compensate for collimator, attenuation, and scatter effects during the reconstruction process. The network was trained using a supervised scheme which implemented the generalized delta rule. Training ended once the mean-squared error (MSE) between the ideal and reconstructed images converged to a minimum. After training, the ANN weights were held constant and could be used to reconstruct source distributions other than those used while training. In the absence of noise when only collimator effects were present, reconstruction of a Hoffman brain phantom had a 89% reduction in MSE compared to standard filtered backprojection. When collimator-and-attenuation and collimator-attenuation-and-scatter trials were tested against filtered backprojection with Chang attenuation compensation, the corresponding ANN reconstructions demonstrated 85% and 86% decreases in MSE, respectively. With noise present, and with standard noise reduction filters implemented prior to reconstruction, the ANN reconstructions displayed up to a 50% decrease in MSE compared to filtered backprojection reconstructions for 200,000 count data. These results demonstrate that an ANN can be used to reconstruct SPECT images with improved quantitative accuracy.
Biophysical Phenomena↗
Data-driven approaches to decision making in automated tumor grading. An example of astrocytoma grading.
OBJECTIVE: To compare four data-driven approaches to automated tumor grading based on morphometric data. Apart from the statistical procedure of linear discriminant analysis, three other approaches from the field of neural computing were evaluated. STUDY DESIGN: The numerical basis of this study was computed tomography-guided, stereotactically obtained astrocytoma biopsies from 86 patients colored with a combination of Feulgen and immunhistochemical Ki-67 (MIB1) staining. In these biopsies the cell nuclei in four consecutive fields of vision were evaluated morphometrically and the following parameters determined: relative nuclei area, secant lengths of the minimal spanning trees and relative volume-weighted mean nuclear volumes of the proliferating nuclei. RESULTS: Based on the analysis of these morphometric features, the multivariate-generated HOM grading system provides the highest correct grading rates (> 90%), whereas the two widely employed qualitative histologic grading systems for astrocytomas yield correct grading rates of about 60%. For automated tumor grading all approaches yield similar grading results; however, back-propagation networks provide reliable results only following an extensive training phase, which requires the use of a supercomputer. All other neurocomputing models can be run on simple UNIX workstations (AT&T, U.S.A). CONCLUSION: In contrast to discriminant analysis, backpropagation and Kohonen networks, the newly developed neural network architecture model of self-editing nearest neighbor nets (SEN3) provides incremental learning; i.e., the training phase does not need to be restarted each time when there is further information to learn. Trained SEN3 networks can be considered ready-to-use knowledge bases and are appropriate to integrating further morphometric data in a dynamic process that enhances the diagnostic power of such a network.
Algorithms↗
Mean-field analysis of selective persistent activity in presence of short-term synaptic depression.
Mean-Field theory is extended to recurrent networks of spiking neurons endowed with short-term depression (STD) of synaptic transmission. The extension involves the use of the distribution of interspike intervals of an integrate-and-fire neuron receiving a Gaussian current, with a given mean and variance, in input. This, in turn, is used to obtain an accurate estimate of the resulting postsynaptic current in presence of STD. The stationary states of the network are obtained requiring self-consistency for the currents-those driving the emission processes and those generated by the emitted spikes. The model network stores in the distribution of two-state efficacies of excitatory-to-excitatory synapses, a randomly composed set of external stimuli. The resulting synaptic structure allows the network to exhibit selective persistent activity for each stimulus in the set. Theory predicts the onset of selective persistent, or working memory (WM) activity upon varying the constitutive parameters (e.g. potentiated/depressed long-term efficacy ratio, parameters associated with STD), and provides the average emission rates in the various steady states. Theoretical estimates are in remarkably good agreement with data "recorded" in computer simulations of the microscopic model.
Action Potentials↗
A tighter bound for graphical models.
We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field. Additionally, we derive a third-order bound for the likelihood of sigmoid belief networks. Numerical experiments indicate that an error reduction of a factor of two is easily reached in the region where expansion-based approximations are useful.
Computer Simulation↗
Neuro-sliding mode control with its applications to seesaw systems.
This paper proposes an approach of cooperative control that is based on the concept of combining neural networks and the methodology of sliding mode control (SMC). The main purpose is to eliminate the chattering phenomenon. Next, the system performance can be improved by using the method of SMC. In the present approach, two parallel Neural Networks are utilized to realize a neuro-sliding mode control (NSMC), where the equivalent control and the corrective control are the outputs of neural network 1 and neural network 2, respectively. Based on expressions of the SMC, the weight adaptations of neural network can be determined. Furthermore, the gradient descent method is used to minimize the control force so that the chattering phenomenon can be eliminated. Finally, experimental results are given to show the effectiveness and feasibility of the approach.
Computers↗
Functional periodic intracortical couplings induced by structured lateral inhibition in a linear cortical network.
The spatial organization of cortical axon and dendritic fields could be an interesting structural paradigm to obtain a functional specificity without postulating highly specific feedforward connections. In this article, we investigate the functional implications of recurrent intracortical inhibition when it occurs through clustered medium-range interconnection schemes (Wörgötter & Koch, 1991; Somogyi, 1989; Kritzer, Cowey, & Somogyi, 1992). Moreover, the interaction between the inhibitory schemes and visual orientation maps is explored. Assuming linearity, we show that clustered inhibitory mechanisms can trigger a propagation process that allows the development of extra (i.e., induced) interactions among the cortical sites involved in the recurrent loops. In addition, we point out how these interactions functionally modify the response of cortical simple cells and yield to highly structured Gabor-like receptive fields. This study should be considered not as a realistic biological model of the primary visual cortex but as an attempt to explain possible computational principles related to intracortical connectivity and to the underlying single-cell properties.
Cerebral Cortex↗
An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro-in vivo correlation.
Quantitative correlations between in vivo and in vitro data (IVIVC) reduce the number of human in vivo studies, thus decreasing the overall time and expenses necessary for the development of optimal drug product formulation. Although linear regression analysis represents the simplest relationship, it is recognized that IVIVC should not be limited to linear relationship. With regards to the implementation of non-linear IVIVC models and the ability of artificial neural network (ANN) computing to cope with non-linear relationships, the usefulness of ANN analysis in the development of IVIVC merits further evaluation. The present paper is an attempt to develop an IVIVC for model sustained release paracetamol matrix tablet formulations employing various correlation approaches based on linear and non-linear modeling of in vitro and in vivo data. Currently accepted compendial methodology was compared with the alternative approaches, involving general mixed effects model and generalized regression neural network (GRNN) analysis, in order to evaluate their usefulness for predicting the in vivo behavior of drug products. Although based on analogous approaches, data generated by GRNN were closer to those observed in vivo, leading to the higher level of IVIVC than obtained by convolution. It can be assumed that GRNN analysis was able to generalize complex relations between the output and input parameters and could account for the differences in drug release kinetics observed under various conditions in vitro, thus offering potential as a reliable and robust estimate of drug products in vivo behavior.
Acetaminophen↗
The hierarchical backbone of complex networks.
Given any complex directed network, a set of acyclic subgraphs can be extracted that will provide valuable information about its hierarchical structure. This Letter presents how the interpretation of the network weight matrix as a transition matrix allows the hierarchical backbone to be identified and characterized in terms of the concepts of hierarchical degree, which expresses the total weights of virtual edges established along successive transitions. The potential of the proposed approach is illustrated with respect to simulated random and preferential-attachment networks as well as real data related to word associations and gene sequencing.
Biophysics↗
Maximum likelihood training of connectionist models: comparison with least squares back-propagation and logistic regression.
This paper presents maximum likelihood back-propagation (ML-BP), an approach to training neural networks. The widely reported original approach uses least squares back-propagation (LS-BP), minimizing the sum of squared errors (SSE). Unfortunately, least squares estimation does not give a maximum likelihood (ML) estimate of the weights in the network. Logistic regression, on the other hand, gives ML estimates for single layer linear models only. This report describes how to obtain ML estimates of the weights in a multi-layer model, and compares LS-BP to ML-BP using several examples. It shows that in many neural networks, least squares estimation gives inferior results and should be abandoned in favor of maximum likelihood estimation. Questions remain about the potential uses of multi-level connectionist models in such areas as diagnostic systems and risk-stratification in outcomes research.
Diagnosis, Computer-Assisted↗