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PubMed2000

Signal decoding and receiver evolution. An analysis using an artificial neural network.

We use a connectionist model, a recurrent artificial neural network, to investigate the evolution of species recognition in sympatric taxa. We addressed three questions: (1) Does the accuracy of artificial neural networks in discriminating between conspecifics and other sympatric heterospecifics depend on whether the networks were trained only to recognize conspecifics, as opposed to being trained to discriminate between conspecifics and sympatric heterospecifics? (2) Do artificial neural networks weight most heavily those signal features that differ most between conspecifics and sympatric heterospecifics, or those features that vary less within conspecifics? (3) Does selection for species recognition generate sexual selection? We find that: (1) Neural networks trained only on self recognition do not classify species as accurately as networks trained to discriminate between conspecifics and heterospecifics. (2) Neural networks weight signal features in a manner suggesting that the total sound environment as opposed to the relative variation of signals within the species is more important in the evolution of recognition mechanisms. (3) Selection for species recognition generates substantial variation in the relative attractiveness of signals within the species and thus can result in sexual selection.

Biological Evolution↗
PubMed1996

Recognition of rapid-eye-movement sleep from single-channel EEG data by artificial neural networks: a study in depressive patients with and without amitriptyline treatment.

An automatic procedure for the online recognition of REM sleep appears to be a necessary tool for selective REM sleep deprivation in depressive patients. To develop such a procedure we applied an artificial neural network to preprocessed single-channel EEG activity. EOG and EMG information was purposely not provided as input to the network. A generalized back-propagation algorithm was used for computer simulation. The sleep profile scored manually according to Rechtschaffen and Kales served as the desired output during the training period and as standard for the judgement of the network output during working mode. Polysomnographic recordings from 5 healthy subjects were pooled to train the network, whereas second-night EEG recordings from the same subjects were used as independent working data sets. We further applied the network to the data of 5 depressive patients without medication and 6 depressive patients treated with amitriptyline. For these groups between 84.9 and 88.6% out of all time periods consisting of 20 s of continuous EEG activity were correctly classified. The indicator function of REM sleep was well approximated by the network output in the course of the night. Especially the REM onset was excellently recognized. The inclusion of patient data in the training set yielded a different network, which was evaluated and compared.

Amitriptyline↗
PubMed2000

The number of synaptic inputs and the synchrony of large, sparse neuronal networks.

The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the average number of synapses, M, that a cell receives is larger than a critical value, Mc. Below Mc, the system is in an asynchronous state. In the limit of weak coupling, assuming identical neurons, we reduce the model to a system of phase oscillators that are coupled via an effective interaction, gamma. In this framework, we develop an approximate theory for sparse networks of identical neurons to estimate Mc analytically from the Fourier coefficients of gamma. Our approach relies on the assumption that the dynamics of a neuron depend mainly on the number of cells that are presynaptic to it. We apply this theory to compute Mc for a model of inhibitory networks of integrate-and-fire (I&F) neurons as a function of the intrinsic neuronal properties (e.g., the refractory period Tr), the synaptic time constants, and the strength of the external stimulus, Iext. The number Mc is found to be nonmonotonous with the strength of Iext. For Tr = 0, we estimate the minimum value of Mc over all the parameters of the model to be 363.8. Above Mc, the neurons tend to fire in smeared one-cluster states at high firing rates and smeared two-or-more-cluster states at low firing rates. Refractoriness decreases Mc at intermediate and high firing rates. These results are compared to numerical simulations. We show numerically that systems with different sizes, N, behave in the same way provided the connectivity, M, is such that 1/Meff = 1/M - 1/N remains constant when N varies. This allows extrapolating the large N behavior of a network from numerical simulations of networks of relatively small sizes (N = 800 in our case). We find that our theory predicts with remarkable accuracy the value of Mc and the patterns of synchrony above Mc, provided the synaptic coupling is not too large. We also study the strong coupling regime of inhibitory sparse networks. All of our simulations demonstrate that increasing the coupling strength reduces the level of synchrony of the neuronal activity. Above a critical coupling strength, the network activity is asynchronous. We point out a fundamental limitation for the mechanisms of synchrony relying on inhibition alone, if heterogeneities in the intrinsic properties of the neurons and spatial fluctuations in the external input are also taken into account.

Algorithms↗
PubMed2001

Patterns of synchrony in neural networks with spike adaptation.

We study the emergence of synchronized burst activity in networks of neurons with spike adaptation. We show that networks of tonically firing adapting excitatory neurons can evolve to a state where the neurons burst in a synchronized manner. The mechanism leading to this burst activity is analyzed in a network of integrate-and-fire neurons with spike adaptation. The dependence of this state on the different network parameters is investigated, and it is shown that this mechanism is robust against inhomogeneities, sparseness of the connectivity, and noise. In networks of two populations, one excitatory and one inhibitory, we show that decreasing the inhibitory feedback can cause the network to switch from a tonically active, asynchronous state to the synchronized bursting state. Finally, we show that the same mechanism also causes synchronized burst activity in networks of more realistic conductance-based model neurons.

Animals↗
PubMed2002

Selectively grouping neurons in recurrent networks of lateral inhibition.

Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, not much is known about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups--the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.

Brain↗
PubMed2004

Recurrent network with large representational capacity.

A recurrent network is proposed with the ability to bind image features into a unified surface representation within a single layer and without capacity limitations or border effects. A group of cells belonging to the same object or surface is labeled with the same activity amplitude, while cells in different groups are kept segregated due to lateral inhibition. Labeling is achieved by activity spreading through local excitatory connections. In order to prevent uncontrolled spreading, a separate network computes the intensity difference between neighboring locations and signals the presence of the surface boundary, which constrains local excitation. The quality of surface representation is not compromised due to the self-excitation. The model is also applied on gray-level images. In order to remove small, noisy regions, a feedforward network is proposed that computes the size of surfaces. Size estimation is based on the difference of dendritic inhibition in lateral excitatory and inhibitory pathways, which allows the network to selectively integrate signals only from cells with the same activity amplitude. When the output of the size estimation network is combined with the recurrent network, good segmentation results are obtained. Both networks are based on biophysically realistic mechanisms such as dendritic inhibition and multiplicative integration among different dendritic branches.

Action Potentials↗
PubMed2006

Memory capacity for sequences in a recurrent network with biological constraints.

The CA3 region of the hippocampus is a recurrent neural network that is essential for the storage and replay of sequences of patterns that represent behavioral events. Here we present a theoretical framework to calculate a sparsely connected network's capacity to store such sequences. As in CA3, only a limited subset of neurons in the network is active at any one time, pattern retrieval is subject to error, and the resources for plasticity are limited. Our analysis combines an analytical mean field approach, stochastic dynamics, and cellular simulations of a time-discrete McCulloch-Pitts network with binary synapses. To maximize the number of sequences that can be stored in the network, we concurrently optimize the number of active neurons, that is, pattern size, and the firing threshold. We find that for one-step associations (i.e., minimal sequences), the optimal pattern size is inversely proportional to the mean connectivity c, whereas the optimal firing threshold is independent of the connectivity. If the number of synapses per neuron is fixed, the maximum number P of stored sequences in a sufficiently large, nonmodular network is independent of its number N of cells. On the other hand, if the number of synapses scales as the network size to the power of 3/2, the number of sequences P is proportional to N. In other words, sequential memory is scalable. Furthermore, we find that there is an optimal ratio r between silent and nonsilent synapses at which the storage capacity alpha = P//[c(1 + r)N] assumes a maximum. For long sequences, the capacity of sequential memory is about one order of magnitude below the capacity for minimal sequences, but otherwise behaves similar to the case of minimal sequences. In a biologically inspired scenario, the information content per synapse is far below theoretical optimality, suggesting that the brain trades off error tolerance against information content in encoding sequential memories.

Hippocampus↗
PubMed1999

Discriminant component pruning. Regularization and interpretation of multi-layered back-propagation networks.

Neural networks are often employed as tools in classification tasks. The use of large networks increases the likelihood of the task's being learned, although it may also lead to increased complexity. Pruning is an effective way of reducing the complexity of large networks. We present discriminant components pruning (DCP), a method of pruning matrices of summed contributions between layers of a neural network. Attempting to interpret the underlying functions learned by the network can be aided by pruning the network. Generalization performance should be maintained at its optimal level following pruning. We demonstrate DCP's effectiveness at maintaining generalization performance, applicability to a wider range of problems, and the usefulness of such pruning for network interpretation. Possible enhancements are discussed for the identification of the optimal reduced rank and inclusion of nonlinear neural activation functions in the pruning algorithm.

Algorithms↗
PubMed2007

The road to chaos by time-asymmetric Hebbian learning in recurrent neural networks.

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides "on its own" the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more "respectful" of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as "frustrated chaos."

Algorithms↗
PubMed1996

Displacement field estimation and image segmentation using block matching enhanced by a neural network.

The block-matching method plays an important role in displacement field estimation due to its simplicity, achievement of long-range motion, and robustness to noise. In this paper, a single-layer feedback neural network model is proposed that enhances block matching, estimates the displacement field, and simultaneously performs image segmentation from consecutive images. In this paper, image segmentation is defined as partitioning each image into a set of moving objects and the background. For any two consecutive images, a neural network is created that learns the connection relationship of the pixels in an object from the displacement field and stores the relationship in the network. A modified block matching is used to compute a more accurate displacement field by utilizing the segmentation information embedded in the neural network. The displacement vector at the edge of an object or occluding boundary is hard to estimate, but the proposed model performs satisfactorily because it learns and uses the connection information. Furthermore, a flood-fill algorithm is used to compute the dense displacement field more efficiently and correctly than the exhaustive search does. The most important aspect of this paper is that image segmentation is performed simultaneously with the displacement-field estimation by the neural-network model. The novel idea of the work is to embed the segmentation information (connection relations) in the neural network and to perform the displacement-field estimation and image segmentation simultaneously. Two methods for retrieving segmentation information from the neural network with any two consecutive images are also presented.

Algorithms↗
PubMedDate not supplied

Evaluation of an artificial neural network to predict urea nitrogen appearance for critically ill multiple-trauma patients.

BACKGROUND: Computer-based simulated biologic neural network models have made significant strides in clinical medicine. METHODS: To determine the predictive performance of a conventional regression model and an artificial neural network for estimating urea nitrogen appearance (UNA) during critical illness, 125 adult patients admitted to the trauma intensive care unit who required specialized nutrition support were studied. The first 100 consecutive patients were used to develop the 2 models. The first model used stepwise multivariate regression analysis. The second model entailed the use of a feeding-forward, back-propagation, supervised neural network. Bias and precision of both methods were evaluated in 25 separate patients. RESULTS: Multivariate regression analysis revealed a significant highly correlative relationship (r(2) = .918, p < or = .01): Predicted UNA (g/d) = (0.29 x WT) + (1.20 x WBC) + (0.44 x SUN) with WT as current body weight in kg, WBC as white blood cell count in cells/mm(3), and SUN as serum urea nitrogen concentration (mg/dL). The regression method was biased toward overestimating measured UNA, whereas the neural network was unbiased. Precision (95% confidence interval) of the neural network was significantly better than the regression (3.3-7.2 g vs 7.3-11.6 g, respectively, p < .01). Regression analysis successfully predicted UNA within 3 g of measured UNA in 16% (4 of 25) of patients, whereas the neural network successfully predicted UNA in 44% (11 out of 25) of patients (p < .06). CONCLUSIONS: These preliminary data indicate that use of an artificial neural network may be superior to conventional regression modeling techniques for estimating UNA in critically ill adult multiple-trauma patients receiving specialized nutrition support.

Adult↗
PubMedDate not supplied

A neural-network approach to predicting admission decisions in a psychiatric emergency room.

Clinical decision making is based on recognizing complex patterns of patients' signs and symptoms. Neural networks have been shown to be very effective at this type of pattern recognition, and in this study a neural-network approach was used to predict which patients seen in a psychiatric emergency room required admission and which did not. Data from all walk-in patients (N = 658) evaluated during normal working hours in a psychiatric emergency room during a one-year period were used either to train a neural network or to test its performance. The network had 53 input nodes, one hidden layer, and an output layer with a single node. The back-propagation method was used to train the network. The neural network's admitting decisions were in substantial agreement with those of the clinicians (kappa coefficient = 0.63). When used as a diagnostic test for admission it had a specificity of 94%, a sensitivity of 70%, and an overall accuracy of 91%. The information gain was 35% of that of a perfect diagnostic test. These results show that a neural network can be trained to make clinical decisions that are in substantial agreement with those of experienced clinicians.

Adolescent↗
PubMed2004

Assessing interorganizational networks as a dimension of community capacity: illustrations from a community intervention to prevent lead poisoning.

Network analysis is often cited as a method for assessing collaboration among organizations as an indicator of community capacity. The purpose of this study was to (1) document patterns of collaboration in organizational networks related to lead poisoning prevention in a Native American community and (2) examine measurement issues in using organizational network analysis to assess community capacity. Interviews were conducted with representatives from 22 tribes, government agencies, schools, and community-based organizations in northeastern Oklahoma. Intensity, density, and reliability were assessed for several stages of collaboration. Intensity and density were greater for similar types of organizations than for the network as a whole and decreased as stage of collaboration increased. Network data were more reliable when responses were dichotomized than when intensities were compared. Mean reliability scores across two respondents from the same organization ranged from 60% to 90%. Results from network studies may help communities learn how to strengthen organizational networks to enhance community capacity.

Community Networks↗
PubMed2007

Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis.

BACKGROUND: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multi-network analysis of microarray data to identify molecular networks regulated by C and N in the Arabidopsis root system. RESULTS: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell molecular network is adjusted in response to the CN treatments, we constructed a qualitative multi-network model of the Arabidopsis metabolic and regulatory molecular network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multi-network, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. CONCLUSION: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of molecular machines in the plant cell.

Arabidopsis↗
PubMed2006

Understanding the conditions that lead to effective health services delivery networks.

This commentary addresses four of the paradoxes proposed by Huerta et al.--resourcing, synergy, defragmentation and evaluation--and uses recent evidence from the Ontario Regional Stroke Strategy and the Dementia Care Networks Study to explore the challenges identified in greater depth. Seven strategies are also proposed to advance the practice and research agendas related to network development and evaluation: developing a shared vision of care for particular groups of care recipients/clients, products and services that goes beyond a single sector (e.g., acute care only); identifying the aspects of care that will most likely benefit from a network structure; embedding networks within broader strategies; developing both clinical and management leadership and collaborations at the organizational and network levels; developing mechanisms to understand care-recipient flow and where gains can be achieved through interactions of key organizations and service providers; using administrative and information mechanisms to increase efficiencies within networks; and acknowledging that, even with a centralized strategy, variations will exist between similar networks.

Canada↗
PubMed2002

Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis.

OBJECTIVE: The purpose of our study was to use an artificial neural network to differentiate benign from malignant pulmonary nodules on high-resolution CT findings and to evaluate the effect of artificial neural network output on the performance of radiologists using receiver operating characteristic analysis. MATERIALS AND METHODS: We selected 155 cases with pulmonary nodules less than 3 cm (99 malignant nodules and 56 benign nodules). An artificial neural network was used to distinguish benign from malignant nodules on the basis of seven clinical parameters and 16 radiologic findings that were extracted by attending radiologists using subjective rating scales. In the observer test, 12 radiologists (four attending radiologists, four radiology fellows, and four radiology residents) were presented with high-resolution CT images, first without and then with the artificial neural network output. Observer performance was evaluated by means of receiver operating characteristic analysis using a continuous rating scale. RESULTS: The artificial neural network showed a high performance in differentiating benign from malignant pulmonary nodules (A(z) = 0.951). The average A(z) value for all radiologists increased by a statistically significant level, from 0.831 to 0.959, with the use of the artificial neural network output. CONCLUSION: Our computerized scheme using the artificial neural network can improve the diagnostic accuracy of radiologists who are differentiating benign from malignant pulmonary nodules on high-resolution CT.

Adult↗
PubMedDate not supplied

Feasibility of using neural networks to estimate minimum tumour temperature and perfusion values.

We examine the ability of neural networks to estimate the tissue perfusion values present and the minimum temperature in numerically calculated (Pennes, Bioheat Transfer Equation) steady-state hyperthermia temperature fields based on a limited number of measured temperatures within this field A hierarchical system of neural networks consisting of a first layer of pattern recognizing neural networks and a second layer of hypersurface reconstructing neural networks is shown to be capable of estimating these variables within a selected error tolerance. The results indicate that estimating the minimum tumour temperature directly with the system of neural networks may be more effective than using the indirect method of numerically recreating a temperature field with perfusion estimates and then obtaining the minimum tumour temperature from the estimated temperature field. Additional results indicate that if the locations of the measured temperatures within the temperature field are selected appropriately, the hierarchical system of neural networks can tolerate a moderate level of model mismatch. This model mismatch can come from errors in modelling the tumour boundaries, the sensor locations, or the magnitude of the power deposition. This paper is not intended to assess or demonstrate clinical applicability but to be a first step in investigating the feasibility of neural networks for parameter estimation related to hyperthermia studies.

Computer Simulation↗
PubMed1997

Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks.

Recent studies have explored characteristics of brain tumors by means of magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well as tumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy. Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) based only on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics, age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using the same information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.

Astrocytoma↗