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PubMedDate not supplied

A functional hypothesis for LGN-V1-TRN connectivities suggested by computer simulation.

We employ computer simulation to investigate the function of neural circuitries between thalamic sensory relay nuclei, primary sensory cortices, and the thalamic reticular nucleus (TRN). Computational similarities exist between these circuits and the architecture of a simple artificial neural network. We impose processing parameters on this network architecture in keeping with anatomical and physiological details of the mammalian geniculo-cortical visual pathway, and then run the simulation on a task involving multiple simultaneous inputs from the simulated visual field. After two to three loops through the simulation, activity in cortical and thalamic units whose receptive fields include the stronger stimulus remains constant, while activity in other cortical and thalamic units activated by weaker stimuli declines toward resting values. These results suggest that the modeled neural circuitry functions to "prime" selective attentional mechanisms further up the visual streams toward specific portions of the total visual stimulus. Besides extending existing models and evidence about the function of these neural circuits, our results also provide physiologists with predicted activity profiles of thalamic and cortical elements of the modeled neural system for a task not yet studied experimentally.

Animals↗
PubMed2005

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses.

RATIONALE AND OBJECTIVE: To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms. MATERIALS AND METHODS: Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis. RESULTS: The area under the ROC curve Az (+/-SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity. CONCLUSION: There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.

Breast Neoplasms↗
PubMed2000

Mean field theory for asymmetric neural networks.

The computation of mean firing rates and correlations is intractable for large neural networks. For symmetric networks one can derive mean field approximations using the Taylor series expansion of the free energy as proposed by Plefka. In asymmetric networks, the concept of free energy is absent. Therefore, it is not immediately obvious how to extend this method to asymmetric networks. In this paper we extend Plefka's approach to asymmetric networks and in fact to arbitrary probability distributions. The method is based on an information geometric argument. The method is illustrated for asymmetric neural networks with sequential dynamics. We compare our approximate analytical results with Monte Carlo simulations for a network of 100 neurons. It is shown that the quality of the approximation for asymmetric networks is as good as for symmetric networks.

Models, Neurological↗
PubMed1995

Computing with dynamic attractors in neural networks.

In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points. The advantages (if any) of such architectures are currently being investigated; but it seems reasonable that the much richer dynamics of recurrent networks, so obvious in recordings of brain activity, must be useful for something. On the other hand, the constraints of computing with biological wet-ware may make chaotic dynamics unavoidable in complex nervous systems. We hypothesize also that the as yet unrivaled capabilities of the human brain derive from an ability to integrate both analog intuitive pattern recognition operations, and digital symbolic logical operations at the ground level of its hardware. To investigate these possibilities, we have constructed a parallel distributed processing architecture inspired by the structure and dynamics of cerebral cortex. The construction assumes that cortex is a set of coupled associative memories with dynamic attractors. It is guided also by a particular concept of the physical structure required of macroscopic computational systems in general for reliable computation in the presence of noise. Our challenge is to accomplish real tasks that brains can do, using ordinary differential equations, in networks that are as faithful as possible to the known anatomy and dynamics of cortex.

Algorithms↗
PubMed1995

Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer.

A great deal of controversy exists in staging clinical stage I (CSI) nonseminomatous testicular germ cell tumors (NSGCT) because of the difficulty of distinguishing true stage I patients from those with occult retroperitoneal or distant metastases. The goal of this study was to quantitate primary tumor histologic factors and to apply these in a neural network computer analysis to determine if more accurate staging could be achieved. All available primary tumor histological slides from 93 CSI NSGCT patients were analyzed for vascular invasion (VI), lymphatic invasion (LI), tunical invasion (TI) and quantitative determination of percentage of the primary tumor composed of embryonal carcinoma (%EMB), yolk sac carcinoma (%YS), teratoma (%TER) and seminoma (%SEM). These patients had undergone retroperitoneal lymphadenectomy or follow-up such that final stage included 55 pathologic stage I and 38 stage II or higher lesions. Two investigators were provided identical datasets for neural network analysis; one experienced researcher used custom Kohonen and back propagation programs and one less experienced researcher used a commercially available program. For each experiment, a subset of data was used for training, and subsets were blindly used to test the accuracy of the networks. In the custom back propagation network, 86 of 93 patients were correctly staged for an overall accuracy of 92% (sensitivity 88%, specificity 96%). Using Neural Ware commercial software 74 of 93 (79.6%) were accurately staged when all 7 input variables were used; however, accuracy improved from 84.9 to 87.1% when 2, 4 and 5 of the variables were used. Quantitative histologic assessment of the primary tumor and neural network processing of data may provide clinically useful information in the CSI NSGCT population; however, the expertise of the network researcher appears to be important, and commercial software in general use may not be superior to standard regression analysis. Prospective testing of expert methodology should be instituted to confirm its utility.

Germinoma↗
PubMedDate not supplied

Neural network method to analyze data compression in DNA and RNA sequences.

Neural network computations on RNA sequences are used to demonstrate that data compression is possible in these sequences. The result implies that a certain discrimination should be achievable between structured vs random regions. The technique is illustrated by computing the compressibility of short RNA sequences such as tRNA. The method should be valuable in measuring the information content of DNA, including noncoding DNA, which has been shown to display certain properties resembling natural language attributes.

Base Sequence↗
PubMed1996

Automated systems for identification of heterotrophic marine bacteria on the basis of their Fatty Acid composition.

The fatty acid methyl ester composition of a total of 71 marine strains representing the genera Alteromonas, Deleya, Oceanospirillum, and Vibrio was determined by gas-liquid chromatographic analysis. Over 70 different fatty acids were found. The predominant fatty acids were 16:0, 16:1 cis 9, summed-in-feature (SIF) 4 (15:0 iso 2OH and/or 16:1 trans 9) and SIF 7 (18:1 cis 11, 18:1 trans 9, and/or 18:1 trans 6) for all the strains considered, but minor quantitative variations could be used to distinguish the different genera. In addition to a conventional statistical processing method to analyze the data and draw comparison between species and genera, an approach involving neutral network-based elaboration is applied. The statistical analysis and dendrogram representation gave a comparison of the species considered, while the neural network computation provided a more accurate assignment of species to their genera. Moreover, by using neural networks, it was possible to conclude that only 22 fatty acids were important for the identification of the marine genera considered. A database of Alteromonas, Deleya, Oceanospirillum, and Vibrio fatty acid methyl ester profiles was generated and is now routinely used to identify fresh marine isolates.

Journal Article↗
PubMed2001

Neural network models of schizophrenia.

There is considerable neurobiological evidence suggesting that schizophrenia is associated with reduced corticocortical connectivity. The authors describe two neural network computer simulations that explore functional consequences of these abnormalities. The first utilized an "attractor" neural network capable of content-addressable memory. Application of a pruning rule that eliminated weaker connections over longer distances produced functional fragmentation and the emergence of localized, "parasitic" attractors that intruded into network dynamics. These pathologies generally were expressed only when input information was ambiguous and provide models for delusions and cognitive disorganization. A second neural network simulation examined effects of corticocortical pruning in a speech perception network. Excessive pruning caused the network to produce percepts spontaneously, that is, in the absence of inputs, thereby simulating hallucinations. The "hallucinating" network also demonstrated subtle impairments in narrative speech perception. A parallel study of human patients found similar impairments when comparing hallucinating patients with nonhallucinating patients. In addition, the authors have used transcranial magnetic stimulation (TMS) to directly probe speech perception neurocircuitry in patients with these hallucinations. As predicted by the neural network model, the authors confirmed that "suppressive" low-frequency TMS reduces auditory hallucinations. Neural network simulations provide empirically testable concepts linking phenomenological, cognitive, and neurobiological findings in schizophrenia.

Brain↗
PubMed2001

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements.

Algorithms↗
PubMed2004

Artificial intelligence in medicine.

INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

Artificial Intelligence↗
PubMed1999

Selective speech perception alterations in schizophrenic patients reporting hallucinated "voices".

OBJECTIVE: The authors tested a model of hallucinated "voices" based on a neural network computer simulation of disordered speech perception. METHOD: Twenty-four patients with schizophrenia spectrum disorders who reported hallucinated voices were compared with 21 patients with schizophrenia spectrum disorders who did not report voices and 26 normal subjects. Narrative speech perception was assessed through use of a masked speech tracking task with three levels of superimposed phonetic noise. A sentence repetition task was used to assess grammar-dependent verbal working memory, and an auditory continuous performance task was used to assess nonlanguage attention. RESULTS: Masked speech tracking task and sentence repetition performance by hallucinating patients was impaired relative to both nonhallucinating patients and normal subjects. Although both hallucinating and nonhallucinating patients demonstrated auditory attention impairments when compared to normal subjects, the two patient groups did not differ with respect to these variables. CONCLUSIONS: Results support the hypothesis that hallucinated voices in schizophrenia arise from disrupted speech perception and verbal working memory systems rather than from nonlanguage cognitive or attentional deficits.

Adult↗
PubMed1998

Dopamine and the mechanisms of cognition: Part II. D-amphetamine effects in human subjects performing a selective attention task.

BACKGROUND: A neural network computer model described in a companion paper predicted the effects of increased dopamine transmission on selective attention under two different hypotheses. METHODS: To evaluate these predictions we conducted an empirical study in human subjects of D-amphetamine effects on performance of the Eriksen response competition task. Ten healthy volunteers were tested before and after placebo or D-amphetamine in a double-blind cross-over design. RESULTS: D-amphetamine induced a speeding of reaction time overall and an improvement of accuracy at fast reaction times but only in the task condition requiring selective attention. CONCLUSIONS: This pattern of results conforms to the prediction of the model under the hypothesis that D-amphetamine primarily affects dopamine transmission in cognitive rather than motor networks. This suggests that the principles embodied in parallel distributed processing models of task performance may be sufficient to predict and explain specific behavioral effects of some drug actions in the central nervous system.

Administration, Oral↗
PubMed1995

A neural model of memory impairment in diffuse cerebral atrophy.

BACKGROUND: Computer-supported neural network models have been subjected to diffuse, progressive deletion of synapses/neurons, to show that modelling cerebral neuropathological changes can predict the pattern of memory degradation in diffuse degenerative processes such as Alzheimer's disease. However, it has been suggested that neural models cannot account for more detailed aspects of memory impairment, such as the relative sparing of remote versus recent memories. METHOD: The latter claim is examined from a computational perspective, using a neural associative memory model. RESULTS: The neural network model not only demonstrates progressive memory deterioration as diffuse network damage occurs, but also exhibits differential sparing of remote versus recent memories. CONCLUSIONS: Our results show that neural models can account for a large variety of experimental phenomena characterising memory degradation in Alzheimer's patients. Specific testable predictions are generated concerning the relation between the neuraonatomical findings and the clinical manifestations of Alzheimer's disease.

Aged↗
PubMed2000

Analysis of pointing errors reveals properties of data representations and coordinate transformations within the central nervous system.

The execution of a simple pointing task invokes a chain of processing that includes visual acquisition of the target, coordination of multimodal proprioceptive signals, and ultimately the generation of a motor command that will drive the finger to the desired target location. These processes in the sensorimotor chain can be described in terms of internal representations of the target or limb positions and coordinate transformations between different internal reference frames. In this article we first describe how different types of error analysis can be used to identify properties of the internal representations and coordinate transformations within the central nervous system. We then describe a series of experiments in which subjects pointed to remembered 3D visual targets under two lighting conditions (dim light and total darkness) and after two different memory delays (0.5 and 5.0 s) and report results in terms of variable error, constant error, and local distortion. Finally, we present a set of simulations to help explain the patterns of errors produced in this pointing task. These analyses and experiments provide insight into the structure of the underlying sensorimotor processes employed by the central nervous system.

Central Nervous System↗
PubMed2007

Recurrent cerebellar loops simplify adaptive control of redundant and nonlinear motor systems.

We have described elsewhere an adaptive filter model of cerebellar learning in which the cerebellar microcircuit acts to decorrelate motor commands from their sensory consequences (Dean, Porrill, & Stone, 2002). Learning stability required the cerebellar microcircuit to be embedded in a recurrent loop, and this has been shown to lead to a simple and modular adaptive control architecture when applied to the linearized 3D vestibular ocular reflex (Porrill, Dean, & Stone, 2004). Here we investigate the properties of recurrent loop connectivity in the case of redundant and nonlinear motor systems and illustrate them using the example of kinematic control of a simulated two-joint robot arm. We demonstrate that (1) the learning rule does not require unavailable motor error signals or complex neural reference structures to estimate such signals (i.e., it solves the motor error problem) and (2) control of redundant systems is not subject to the nonconvexity problem in which incorrect average motor commands are learned for end-effector positions that can be accessed in more than one arm configuration. These properties suggest a central functional role for the closed cerebellar loops, which have been shown to be ubiquitous in motor systems (e.g., Kelly & Strick, 2003).

Adaptation, Physiological↗
PubMed2000

Visualizing the function computed by a feedforward neural network.

A method for visualizing the function computed by a feedforward neural network is presented. It is most suitable for models with continuous inputs and a small number of outputs, where the output function is reasonably smooth, as in regression and probabilistic classification tasks. The visualization makes readily apparent the effects of each input and the way in which the functions deviate from a linear function. The visualization can also assist in identifying interactions in the fitted model. The method uses only the input-output relationship and thus can be applied to any predictive statistical model, including bagged and committee models, which are otherwise difficult to interpret. The visualization method is demonstrated on a neural network model of how the risk of lung cancer is affected by smoking and drinking.

Alcohol Drinking↗
PubMed1995

The protective effects of education on simulated brain injury.

Disturbed intellectual function is an important determinant of long-term recovery after head injury. Residual behavioral disability depends largely upon which hemisphere is damaged and the site of injury within the hemisphere. Resulting neurobehavioral syndromes vary with the pattern of brain insult. While this correlation is important, it is still not well understood how damage to single neurons translates into abnormal behavior. In addition, no currently available technology permits in vivo evaluation of such cells in normal or injured states. While research into brain trauma traditionally emphasize single cell pathology, this level of study is insufficient to clarify how individual cell activity contributes to higher cognition. In contrast, neural network simulations may partially fill this void by predicting biological function. Here, we present data generated by a three-layer neural network that employs the ALOPEX algorithm, capable of learning 10 words. The addition of Gaussian distributed noise into connection weights strengths damaged the network's function; interestingly, the rate and extent of network impairment depended upon the level of learning that occurred during the training period, in agreement with human studies that demonstrate a protective effect of education on subsequent brain injury. Damage to input signals resulted in similar effects on network function. Accordingly, such simulations offer powerful evidence that human behavior may be considered in terms of neuronal cell populations and efforts to preserve brain function at the time of injury should emphasize maintaining neural connections.

Algorithms↗
PubMed2005

Autoassociative memory design using interconnected generalized brain-state-in-a-box neural networks.

A class of interconnected neural networks composed of generalized Brain-State-in-a-Box (gBSB) neural subnetworks is considered. Interconnected gBSB neural network architectures are proposed along with their stability conditions. The design of the interconnected neural networks is reduced to the problem of solving linear matrix inequalities (LMIs) to determine the interconnection parameters. A method for solving LMIs is devised generating the solutions that, in general, are further away from zero than the corresponding solutions obtained using MATLAB's LMI toolbox, thus resulting in stronger interconnections between the subnetworks. The proposed architectures are then used to construct neural associative memories. Simulations are performed to illustrate the results obtained.

Algorithms↗