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PubMed1996

Hardware prototypes of a Boolean neural network and the simulated annealing optimization method.

Boolean Neural Network is a neural network that operates with binary weight values of "1" and "0". Otherwise it is formally analogous to the Multilayer Perceptron (MLP). Simulated Annealing is a stochastic optimization methods that is suitable for performing nonlinear multivariable optimization tasks. Training a Boolean Neural Network is a well-suited problem to this algorithm. However, the Simulated Annealing method is computationally heavy, which makes the training procedure slow. The training speed can be improved by using custom designed hardware for the whole system including the optimization method and the neural network. Hardware prototypes of a Boolean Neural Network and the Simulated Annealing optimization method have been designed using discrete components. The Boolean Neural Network implementation is basically a dynamically configurable feedforward network of Boolean logic gates of two inputs. The Simulated Annealing implementation is a general purpose hardware tool for multivariable optimization tasks. Here it is applied to do supervised training of the Boolean Neural Network hardware.

Algorithms↗

PubMed1999

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification.

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.

Algorithms↗

PubMed1998

Acute pulmonary embolism: cost-effectiveness analysis of the effect of artificial neural networks on patient care.

PURPOSE: To evaluate the cost-effectiveness of artificial neural networks for diagnosis in patients suspected of having acute pulmonary embolism who are typically referred for pulmonary angiography. MATERIALS AND METHODS: Four diagnostic strategies were explored to help define the diagnostic role of neural networks in patients suspected of having pulmonary embolism in whom nondiagnostic ventilation-perfusion lung scans were obtained. First, a network was used to determine which patients could be directly referred for treatment without angiography. Second, the network was applied to determine in which patients treatment could be withheld. Third, the network was used to distinguish patients in whom the network gave indeterminate responses and who should proceed to angiography. Each strategy was compared with use of angiography in terms of morbidity, mortality, and cost per life saved. RESULTS: The use of the neural network reduced the average cost per patient by more than one-half relative to the cost of angiography. Morbidity and mortality rates were also comparable to or lower than those associated with angiography. The results were consistent regardless of the prevalence of disease. CONCLUSION: The use of neural networks in the diagnosis of pulmonary embolism is a promising way to improve cost-effectiveness in the care of patients with nondiagnostic lung scans.

Acute Disease↗

PubMed1994

Mapping myocardial activation distributions using neural networks: 2-D simulation results.

The goal of this study was to explore the capabilities of neural networks to map with accuracy the sequence and location of myocardial activation using QRS complexes simulating normal and altered activation. A two-dimensional (2-D) fractal-based computer model of myocardial activation was used to develop training data for initial network learning. Two types of activation scenarios were used to evaluate network learning: 1) 450 training sets based on three activation foci per set using randomly chosen times and activation sites, and 2) 199 training sets based on a sequential, hierarchical blocking of the fractal-based model conduction network. Network learning was evaluated with training and test cases using trained weights. Network-calculated activation maps compared with the target activation maps had a mean error of < 5% in assigning the site and timing of activation. Pointwise mean correlation coefficients were > 0.98 for all conduction network cases and > 0.84 for the more demanding point foci cases. We conclude, based on these simulation results, that neural networks may be used to calculate activation maps using electrocardiogram lead data for a variety of activation patterns.

Animals↗

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↗

PubMed1997

Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks.

BACKGROUND: The 12-lead ECG, together with patient history and clinical findings, remains the most important method for early diagnosis of acute myocardial infarction. Automated interpretation of ECG is widely used as decision support for less experienced physicians. Recent reports have demonstrated that artificial neural networks can be used to improve selected aspects of conventional rule-based interpretation programs. The purpose of this study was to detect acute myocardial infarction in the 12-lead ECG with artificial neural networks. METHODS AND RESULTS: A total of 1120 ECGs from patients with acute myocardial infarction and 10,452 control ECGs, recorded at an emergency department with computerized ECGs, were studied. Artificial neural networks were trained to detect acute myocardial infarction by use of measurements from the 12 ST-T segments of each ECG, together with the correct diagnosis. After this training process, the performance of the neural networks was compared with that of a widely used ECG interpretation program and the classification of an experienced cardiologist. The neural networks showed higher sensitivities and discriminant power than both the interpretation program and cardiologist. The sensitivity of the neural networks was 15.5% (95% confidence interval [CI], 12.4 to 18.6) higher than that of the interpretation program compared at a specificity of 95.4% (P<.00001) and 10.5% (95% CI, 7.2 to 13.6) higher than the cardiologist at a specificity of 86.3% (P<.00001). CONCLUSIONS: Artificial neural networks can be used to improve automated ECG interpretation for acute myocardial infarction. The networks may be useful as decision support even for the experienced ECG readers.

Adult↗

PubMed2000

Geometric analysis of population rhythms in synaptically coupled neuronal networks.

We develop geometric dynamical systems methods to determine how various components contribute to a neuronal network's emergent population behaviors. The results clarify the multiple roles inhibition can play in producing different rhythms. Which rhythms arise depends on how inhibition interacts with intrinsic properties of the neurons; the nature of these interactions depends on the underlying architecture of the network. Our analysis demonstrates that fast inhibitory coupling may lead to synchronized rhythms if either the cells within the network or the architecture of the network is sufficiently complicated. This cannot occur in mutually coupled networks with basic cells; the geometric approach helps explain how additional network complexity allows for synchronized rhythms in the presence of fast inhibitory coupling. The networks and issues considered are motivated by recent models for thalamic oscillations. The analysis helps clarify the roles of various biophysical features, such as fast and slow inhibition, cortical inputs, and ionic conductances, in producing network behavior associated with the spindle sleep rhythm and with paroxysmal discharge rhythms. Transitions between these rhythms are also discussed.

Cerebral Cortex↗

PubMed2004

Rapid temporal modulation of synchrony by competition in cortical interneuron networks.

The synchrony of neurons in extrastriate visual cortex is modulated by selective attention even when there are only small changes in firing rate (Fries, Reynolds, Rorie, & Desimone, 2001). We used Hodgkin-Huxley type models of cortical neurons to investigate the mechanism by which the degree of synchrony can be modulated independently of changes in firing rates. The synchrony of local networks of model cortical interneurons interacting through GABA(A) synapses was modulated on a fast timescale by selectively activating a fraction of the interneurons. The activated interneurons became rapidly synchronized and suppressed the activity of the other neurons in the network but only if the network was in a restricted range of balanced synaptic background activity. During stronger background activity, the network did not synchronize, and for weaker background activity, the network synchronized but did not return to an asynchronous state after synchronizing. The inhibitory output of the network blocked the activity of pyramidal neurons during asynchronous network activity, and during synchronous network activity, it enhanced the impact of the stimulus-related activity of pyramidal cells on receiving cortical areas (Salinas & Sejnowski, 2001). Synchrony by competition provides a mechanism for controlling synchrony with minor alterations in rate, which could be useful for information processing. Because traditional methods such as cross-correlation and the spike field coherence require several hundred milliseconds of recordings and cannot measure rapid changes in the degree of synchrony, we introduced a new method to detect rapid changes in the degree of coincidence and precision of spike timing.

Action Potentials↗

PubMed2004

Orthogonality of decision boundaries in complex-valued neural networks.

This letter presents some results of an analysis on the decision boundaries of complex-valued neural networks whose weights, threshold values, input and output signals are all complex numbers. The main results may be summarized as follows. (1) A decision boundary of a single complex-valued neuron consists of two hypersurfaces that intersect orthogonally, and divides a decision region into four equal sections. The XOR problem and the detection of symmetry problem that cannot be solved with two-layered real-valued neural networks, can be solved by two-layered complex-valued neural networks with the orthogonal decision boundaries, which reveals a potent computational power of complex-valued neural nets. Furthermore, the fading equalization problem can be successfully solved by the two-layered complex-valued neural network with the highest generalization ability. (2) A decision boundary of a three-layered complex-valued neural network has the orthogonal property as a basic structure, and its two hypersurfaces approach orthogonality as all the net inputs to each hidden neuron grow. In particular, most of the decision boundaries in the three-layered complex-valued neural network inetersect orthogonally when the network is trained using Complex-BP algorithm. As a result, the orthogonality of the decision boundaries improves its generalization ability. (3) The average of the learning speed of the Complex-BP is several times faster than that of the Real-BP. The standard deviation of the learning speed of the Complex-BP is smaller than that of the Real-BP. It seems that the complex-valued neural network and the related algorithm are natural for learning complex-valued patterns for the above reasons.

Algorithms↗

PubMed1996

Neural network models of perceptual learning of angle discrimination.

We study neural network models of discriminating between stimuli with two similar angles, using the two-alternative forced choice (2AFC) paradigm. Two network architectures are investigated: a two-layer perceptron network and a gating network. In the two-layer network all hidden units contribute to the decision at all angles, while in the other architecture the gating units select, for each stimulus, the appropriate hidden units that will dominate the decision. We find that both architectures can perform the task reasonably well for all angles. Perceptual learning has been modeled by training the networks to perform the task, using unsupervised Hebb learning algorithms with pairs of stimuli at fixed angles theta and delta theta. Perceptual transfer is studied by measuring the performance of the network on stimuli with theta' not equal to theta. The two-layer perceptron shows a partial transfer for angles that are within a distance a from theta, where a is the angular width of the input tuning curves. The change in performance due to learning is positive for angles close to theta, but for magnitude of theta-theta' approximately a it is negative, i.e., its performance after training is worse than before. In contrast, negative transfer can be avoided in the gating network by limiting the effects of learning to hidden units that are optimized for angles that are close to the trained angle.

Discrimination, Psychological↗

PubMed2003

Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases.

BACKGROUND: Appropriate definition of neural network architecture prior to data analysis is crucial for successful data mining. This can be challenging when the underlying model of the data is unknown. The goal of this study was to determine whether optimizing neural network architecture using genetic programming as a machine learning strategy would improve the ability of neural networks to model and detect nonlinear interactions among genes in studies of common human diseases. RESULTS: Using simulated data, we show that a genetic programming optimized neural network approach is able to model gene-gene interactions as well as a traditional back propagation neural network. Furthermore, the genetic programming optimized neural network is better than the traditional back propagation neural network approach in terms of predictive ability and power to detect gene-gene interactions when non-functional polymorphisms are present. CONCLUSION: This study suggests that a machine learning strategy for optimizing neural network architecture may be preferable to traditional trial-and-error approaches for the identification and characterization of gene-gene interactions in common, complex human diseases.

Algorithms↗

PubMed2005

Diagnosis of alcoholism based on neural network analysis of phenotypic risk factors.

BACKGROUND: Alcoholism is a serious public health problem. It has both genetic and environmental causes. In an effort to gain understanding of the underlying genetic susceptibility to alcoholism, a long-term study has been undertaken. The Collaborative Study on the Genetics of Alcoholism (COGA) provides a rich source of genetic and phenotypic data. One ongoing problem is the difficulty of reliably diagnosing alcoholism, despite many known risk factors and measurements. We have applied a well known pattern-matching method, neural network analysis, to phenotypic data provided to participants in Genetic Analysis Workshop 14 by COGA. The aim is to train the network to recognize complex phenotypic patterns that are characteristic of those with alcoholism as well as those who are free of symptoms. Our results indicate that this approach may be helpful in the diagnosis of alcoholism. RESULTS: Training and testing of input/output pairs of risk factors by means of a "feed-forward back-propagation" neural network resulted in reliability of about 94% in predicting the presence or absence of alcoholism based on 36 input phenotypic risk factors. Pruning the neural network to remove relatively uninformative factors resulted in a reduced network of 14 input factors that was still 95% reliable. Some of the factors selected by the pruning steps have been identified as traits that show either linkage or association to potential candidate regions. CONCLUSION: The complex, multivariate picture formed by known risk factors for alcoholism can be incorporated into a neural network analysis that reliably predicts the presence or absence of alcoholism about 94-95% of the time. Several characteristics that were identified by a pruned neural network have previously been shown to be important in this disease based on more traditional linkage and association studies. Neural networks therefore provide one less traditional approach to both identifying alcoholic individuals and determining the most informative risk factors.

Alcohol Drinking↗

PubMed2026

Effects of different training modalities on lower-limb explosive power, acceleration, 20-m sprint performance, and change-of-direction ability in youth soccer players: a systematic review and network meta-analysis.

BACKGROUND: Youth soccer players repeatedly perform explosive actions, short accelerations, linear sprints, decelerations, and multidirectional movements. However, the comparative effects of different structured physical-conditioning programmes remain uncertain. METHODS: Seven databases were searched from inception to 3 July 2026 using a final expanded search strategy encompassing plyometric, strength or resistance, sprint, acceleration, speed, change-of-direction, neuromuscular, multicomponent, and combined training. Randomised controlled trials involving healthy youth soccer players were eligible. Intervention arms were classified using operational, content-based node definitions. Construct-restricted primary networks and expanded sensitivity networks were analysed using frequentist random-effects network meta-analysis. Hedges' adjusted g was preferentially calculated from post-intervention or final-follow-up means, standard deviations, and sample sizes. Estimates were presented so that positive values indicated better performance. P-scores were treated as descriptive ranking summaries. Risk of bias was assessed using an adapted study-level application of the five-domain RoB 2 framework, and confidence in the evidence was assessed using CINeMA. A post hoc strict-age sensitivity analysis excluded two age-boundary studies. RESULTS: Eighty-nine studies were included in the expanded quantitative analysis, of which 74 contributed to at least one construct-restricted primary network. The primary lower-limb explosive-power, acceleration, 20-m sprint, and planned change-of-direction networks included 55, 20, 25, and 38 studies, respectively. Compared with usual soccer training, plyometric training combined with sprint and/or change-of-direction training showed favourable estimates for lower-limb explosive power (SMD 0.79, 95% CI 0.55 to 1.03), acceleration (1.19, 0.90 to 1.49), 20-m sprint performance (0.80, 0.33 to 1.28), and planned change-of-direction ability (1.46, 1.13 to 1.80). Corresponding I² values were 34.6%, 21.8%, 65.0%, and 41.0%. Between-design inconsistency was detected in the 20-m sprint (P = 0.0036) and change-of-direction (P = 0.0007) networks. CINeMA confidence for these four comparisons was low, low, very low, and low, respectively. Expanded sensitivity networks showed substantially greater heterogeneity. The highest-ranked intervention differed across outcome domains but remained consistent within each outcome across the three analysis sets. Excluding the two age-boundary studies did not materially alter the principal estimates. CONCLUSIONS: Plyometric training combined with sprint and/or planned change-of-direction training produced favourable comparative estimates across the four performance outcomes. However, evidence for several nodes and active-versus-active comparisons was sparse, heterogeneity in programmes and outcomes was present, inconsistency was detected in some networks, and confidence in the evidence was low or very low. These limitations do not support a conclusion that any training category is universally superior. The findings should be interpreted as provisional category-level signals rather than definitive training prescriptions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD420261347297, registered on 21 March 2026, https://www.crd.york.ac.uk/PROSPERO/view/CRD420261347297 .

Change-of-direction ability↗

PubMedDate not supplied

How primary care networks can help integrate academic and service initiatives in primary care.

PURPOSE: Theory of effective network operation in primary care is underdeveloped. This study aimed to identify how primary care networks can best integrate academic and service initiatives. METHODS: We performed a comparative case study of 4 primary care research networks in North London, England, for the years 1998-2002. Indicators were selected to assess changes in (1) research capacity, (2) multidisciplinary collaboration, and (3) research productivity. We compared the profiles of network outcome with descriptions of their contexts and organizational types from a previous evaluation. RESULTS: Together, the networks supported 133 viable projects and 30 others; 399 practitioners, managers, and academics participated in the research teams. How the networks organized themselves was influenced by the circumstances in which they were formed. Different ways of organizing were associated with different outcome profiles. Shared projects and learning spaces helped participants to develop trusted relationships. A top-down, hierarchical approach based on institutional alliances and academic expertise attracted more funding and appeared to be stable. The bottom-up, individualistic network with research practices was good at reflecting on practical primary care concerns. Whole-system methods brought together stakeholder contributions from all parts of the system. CONCLUSIONS: Networks can help integrate academic research and service development initiatives by facilitating interorganizational interactions and in shared leadership of projects. Researchers and practitioners stand to gain considerably from an integrated approach in both the short and the long term. Success requires agreement about a set of pathways, learning spaces, and feedback mechanisms to harness the insights and efforts of stakeholders throughout the whole system.

Community Networks↗

PubMedDate not supplied

Successful turnaround of a university-owned, community-based, multidisciplinary practice network.

PURPOSE: The University of Utah purchased a 100-clinician, 9-practice multi-specialty primary care network in 1998. The university projected the network to earn a profit the first year of its ownership in a market with growing capitation; however, capitation declined and the network incurred up to a 21 million dollars operating loss per year. This case study describes the financial turnaround of the network. METHODS: In 2001, the university reconfigured the practices for a fee-for-service environment while preserving the group's multidisciplinary clinical and ancillary services. Changes included reorganization under the existing University of Utah Hospitals and Clinics system, new governance and leadership, closure of practices, creation of a billing office, new financial reporting, implementation of electronic health records, revision of physician compensation, capture of referrals, leadership and staff training, and practice reengineering. RESULTS: The network as a whole became profitable in 2004-2005. Its primary care component is projected to become profitable in 2 to 3 years. The network is opening new sites strategically important to the health system. CONCLUSIONS: This turnaround required commitment from senior university leaders, management with knowledge of primary care practice, retention of ancillary revenues, and management and business services specific to the network with support from other units within the university. Culture change within the group was essential. Our experience suggests that an academic health center can successfully operate a primary care network by attending to the unique needs of this challenging business. Doing so can strengthen the institution's overall financial and clinical performance and provide an important setting for teaching and research.

Community Health Centers↗

PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗

PubMed2000

RBF networks for source localization in quantitative electrophysiology.

The backpropagation neural network methods have been proposed recently to solve the inverse problem in quantitative electrophysiology. A major advantage of the technique is that once a neural network is trained, it no longer requires iterations or access to sophisticated computations. We propose to use RBF networks for source localization in the brain, and systematically compare their performance to those of Levenberg-Marquardt (LM) algorithms. We show the use of two types of Radial Basis Function Networks (RBF) network: a classic network with fixed number of hidden layer neurons and an improved network, Minimal Resource Allocation Network (MRAN), recently proposed by one of the authors, capable for dynamically configuring its structure so as to obtain a compact topology to match the data presented to it.

Algorithms↗

PubMed1996

Pharmaceutical granulation and tablet formulation using neural networks.

Current-day pharmaceutical formulation may be trial and error in nature due to the absence of a clear relationship between the formulation characteristics (output variables) and the material and process variables (input variables). Neural networks are networks of adaptable nodes, which through a process of learning from task examples, store experiential knowledge and make it available for prediction. Prediction of a model granulation and tablet system characteristics from the knowledge of material and process variables utilizing neural networks is the basis of this presentation. The formulation design contained the following variables: granulation equipment, diluent, method of binder addition, and the binder concentration. The material, process, granulation evaluation, and tablet evaluation data of the formulations were used as the data set for training and testing of the neural network models. A comparison of the neural network prediction performance with that of regression models was also done. Both the granulation model and the tablet model converged fairly rapidly in the training step. In the testing step, the predictions for all granulation model variables (geometric mean particle size, flow value, bulk density, and tap density) were satisfactory. In the tablet model, the predictions for disintegration and thickness were also satisfactory. The predictions for hardness and friability were less than satisfactory. Two situations where the neural network may not perform adequately are discussed. The neural network prediction is better or comparable for all the predicted variables in this study compared to regression methods. The results clearly show the applicability of neural networks to formulation modeling.

Caffeine↗