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PubMed2005

Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface.

The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.

Artifacts↗

PubMed2007

Invariant global motion recognition in the dorsal visual system: a unifying theory.

The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only because of the aperture problem but also because they do not have invariant representations. In a unifying hypothesis with the design of the ventral cortical visual system, we propose that the dorsal visual system uses a hierarchical feedforward network architecture (V1, V2, MT, MSTd, parietal cortex) with training of the connections with a short-term memory trace associative synaptic modification rule to capture what is invariant at each stage. Simulations show that the proposal is computationally feasible, in that invariant representations of the motion flow fields produced by objects self-organize in the later layers of the architecture. The model produces invariant representations of the motion flow fields produced by global in-plane motion of an object, in-plane rotational motion, looming versus receding of the object, and object-based rotation about a principal axis. Thus, the dorsal and ventral visual systems may share some similar computational principles.

Animals↗

PubMed2006

The chaotic nature of temper in humans: a long short-term memory recurrent neural network model.

In mathematics and physics, chaos theory deals with the behavior of certain nonlinear dynamical systems that under certain conditions exhibit a phenomenon known as chaos, which is characterised by a sensitivity to initial conditions. Mathematicians paradoxically call such states of order chaos and distinguish them from randomness. New models for describing and predicting different aspects of behavior are being created which once seemed unpredictable. This is done by focusing on the overall patterns of behavior, showing how stable or unstable they are and identifying the circumstances that make them change. In this paper, we indicate why human temper and mood changes have a chaotic nature. Then, we develop a chaotic model based on a long short-term memory recurrent neural network with irregular embeddings derived by the gamma test to model temper tantrum. We finally use a feedback delay controller to stabilize its chaotic behavior, because it is a plausible method for stabilizing biological neural systems. A lot of aspects of this model are analogous to the human counterpart. The model might suggest, for example, that if a particular person had stronger ego defenses, and attended a little less vigilantly to the external world, he or she might find a stable attractor amidst of a broader landscape of chaotic attractors. A therapist or self-control would be analogous to the delayed feedback controller, by specifically encouraging those changes, he might help the person reach a stable behavior. Finally, some comments are proposed to facilitate the normal behavior.

Computer Simulation↗

PubMed1997

A neuro-mimetic dynamic scheduling algorithm for control: analysis and applications.

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanisms by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.

Algorithms↗

PubMed2004

A neural model of quantity discrimination.

A neural network model is proposed with the ability to extract abstract numerical representation from visual input. It simulates properties of a number detection system which is hypothesized to underlie simple language-independent numerical abilities. The network has three layers where the first layer computes the sum of the nearest neighbour inputs. The first layer is also augmented with multiplicative gating and gradient tonic activation which prevents interference. The second layer implements local lateral inhibition which enables a single node to represent a single object. The third layer exhibits number-tuning similar to recently described responses of neurons in the prefrontal cortex. Computer simulations showed that network response does not depend on visual attributes like the object's size, position or shape. The model is based on several biophysical mechanisms such as multiplicative interaction on dendrites, independent processing on different dendritic branches and disinhibition by glutamate spill-over on kainate receptors on inhibitory axons.

Animals↗

PubMed1998

Applications of neural networks in urologic oncology.

Artificial neural networks are computer-based statistical models that can be used to imitate biologic neural processes. They have been applied to a variety of problems in medicine, including diagnosis and outcomes predictions. In the area of urologic oncology, these neural networks have been used to assist in the diagnosis of prostate cancer, predicting response to therapy and recurrence in prostate cancer, predicting the presence of renal cell carcinoma in cystic renal lesions, and predicting the presence of occult metastatic disease in nonseminomatous testicular germ cell tumors. This article reviews the basic concepts of artificial neural networks and summarizes their application in urologic oncology. Neural network technology remains in its infancy in urologic oncology, and many more retrospective and prospective studies are needed to determine its clinical utility.

Computer Simulation↗

PubMed2002

Neural adaptive control of nonlinear multivariable systems with application to a class of inverted pendulums.

In this paper multilayer neural networks (MNNs) are used to control the balancing of a class of inverted pendulums. Unlike normal inverted pendulums, the pendulum discussed here has two degrees of rotational freedom and the base-point moves randomly in three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed position in spite of the random movement at the base-point. Since the inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, the design of the control system is a challenging task. A feedback control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of the system's nonlinearities. The weight parameters of neural networks updated on-line, according to a learning algorithm that guarantees the Lyapunov stability of the control system. Furthermore, since the base-point movement is considered unmeasurable, a neural inverse model is employed to estimate it from only measured state variables. The estimate is then utilized within the main control algorithm to produce compensating control signals. The examination of the proposed control system, through simulations, demonstrates the promise of the methodology and exhibits positive aspects, which cannot be achieved by the previously developed techniques on the same problem. These aspects include fast, yet well-maintained damped responses with reasonable control torques and no requirement for knowledge of the model or the model parameters. The work presented here can benefit practical problems such as the study of stable locomotion of human upper body and bipedal robots.

Algorithms↗

PubMed1996

A neural network for position invariant pattern recognition combining spiking neurons with the Fourier-transform.

We present an approach for position invariant recognition of individual objects in composite scenes, combining neural networks and algorithmic methods. A dynamic network of spiking neurons is used to generate object definition and figure/ground separation via temporal signal correlations. A shift invariant representation of the network spike activity distribution is subsequently realized via the amplitude spectrum of the Fourier-transform. Objects and their transformed representations are therefore linked in the time domain. The model segregates scenes and classifies individual patterns independent of their position in the input scene.

Algorithms↗

PubMed2006

Towards computing with proteins.

Can proteins be used as computational devices to address difficult computational problems? In recent years there has been much interest in biological computing, that is, building a general purpose computer from biological molecules. Most of the current efforts are based on DNA because of its ability to self-hybridize. The exquisite selectivity and specificity of complex protein-based networks motivated us to suggest that similar principles can be used to devise biological systems that will be able to directly implement any logical circuit as a parallel asynchronous computation. Such devices, powered by ATP molecules, would be able to perform, for medical applications, digital computation with natural interface to biological input conditions. We discuss how to design protein molecules that would serve as the basic computational element by functioning as a NAND logical gate, utilizing DNA tags for recognition, and phosphorylation and exonuclease reactions for information processing. A solution of these elements could carry out effective computation. Finally, the model and its robustness to errors were tested in a computer simulation.

Adenosine Triphosphate↗

PubMed2006

Language models based on Hebbian cell assemblies.

This paper demonstrates how associative neural networks as standard models for Hebbian cell assemblies can be extended to implement language processes in large-scale brain simulations. To this end the classical auto- and hetero-associative paradigms of attractor nets and synfire chains (SFCs) are combined and complemented by conditioned associations as a third principle which allows for the implementation of complex graph-like transition structures between assemblies. We show example simulations of a multiple area network for object-naming, which categorises objects in a visual hierarchy and generates different specific syntactic motor sequences ("words") in response. The formation of cell assemblies due to ongoing plasticity in a multiple area network for word learning is studied afterwards. Simulations show how assemblies can form by means of percolating activity across auditory and motor-related language areas, a process supported by rhythmic, synchronized propagating waves through the network. Simulations further reproduce differences in own EEG&MEG experiments between responses to word- versus non-word stimuli in human subjects.

Animals↗

PubMed2004

Coupling of radial-basis network and active contour model for multispectral brain MRI segmentation.

Magnetic resonance (MR) has been accepted as the reference image study in the clinical environment. The development of new sequences has allowed obtaining diverse images with high clinical importance and whose interpretation requires their joint analysis (multispectral MRI). Recent approaches to segment MRI point toward the definition of hybrid models, where the advantages of region and contour-based methods can be exploited to look for the integration or fusion of information, thus enhancing the performance of the individual approaches. Following this perspective, a hybrid model for multispectral brain MRI segmentation is presented. The model couples a segmenter, based on a radial basis network (RBFNNcc), and an active contour model, based on a cubic spline active contour (CSAC) interpolation. Both static and dynamic coupling of RBFNNcc and CSAC are proposed; the RBFNNcc stage provides an initial contour to the CSAC; the initial contour is optimally sampled with respect to its curvature variations; multispectral information and a restriction term are included into the CSAC energy equation. Segmentations were compared to a reference stack, indicating high-quality performance as measured by Tanimoto indexes of 0.74 +/- 0.08.

Algorithms↗

PubMed1997

Probabilistic independence networks for hidden Markov probability models.

Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas, including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper presents a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

Algorithms↗

PubMed2000

Errors in a nonlinear graphic-semantic mapping task resulting from lesions in Boltzmann machine: is it relevant to dyslexia?

One of the most fascinating aspects of brain research is the subject of language. As in many other cases, the malfunctions that occur in different persons for various reasons give us insight on the mechanisms that support our ability to talk, read and listen. Following the work of Plaut and associates, we deal with the dyslexia disorder, which is the overall name for a large number of reading disorders. A Boltzmann machine neural network scheme was trained to implement the nonlinear mapping task of graphic representation into semantic representation, which may model the brain sections responsible for the translation of a written word into meanings and syllables. After training, various types of lesions were applied and the performance of the network was tested in order to measure the effect of each lesion on the error rate and type distribution that were detected. The system's errors were classified into several categories and the distribution of errors between the categories was studied. Using the simulations, it is demonstrated that a finite scheduling process in the Boltzmann machine causes the distribution of the network's errors to be unique and different from its expected error distribution. The phenomenon is given a mathematical explanation rooted in the statistical mechanics basics of the Boltzmann machine. Test results suggest the localization of certain reading functions within the network. Comparison is made to relevant types of dyslexia and shows resemblance in major symptoms as well as in certain known side effects.

Algorithms↗

PubMed2005

Prediction of protein secondary structure using improved two-level neural network architecture.

In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.

Algorithms↗

PubMed2004

Local design principles of mammalian cortical networks.

To understand global and local design principles of mammalian cerebral cortical networks, we applied network-theoretical approaches to connectivity data from macaque and cat cortical networks. We first confirmed "small-world" properties and searched for the evidence of hierarchical modularity. To elucidate their local design principles, we then compared these cortical networks, based on the significance profile (SP) of network motifs in the real network compared to randomized networks. We found that SPs of different mammalian cortical networks are highly conserved and robust, suggesting constraints of neocortical development and evolution.

Animals↗

PubMed1997

Conductance-based integrate-and-fire models.

A conductance-based model of Na+ and K+ currents underlying action potential generation is introduced by simplifying the quantitative model of Hodgkin and Huxley (HH). If the time course of rate constants can be approximated by a pulse, HH equations can be solved analytically. Pulse-based (PB) models generate action potentials very similar to the HH model but are computationally faster. Unlike the classical integrate-and-fire (IAF) approach, they take into account the changes of conductances during and after the spike, which have a determinant influence in shaping neuronal responses. Similarities and differences among PB, IAF, and HH models are illustrated for three cases: high-frequency repetitive firing, spike timing following random synaptic inputs, and network behavior in the presence of intrinsic currents.

Action Potentials↗

PubMed2000

[Automated detection of spleen volume by spiral CT scans using neural networks and "fuzzy logic"].

PURPOSE: To assess spleen segmentation and volumentry in spiral CT scans with and without pathological changes of splenic tissue. METHODS: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. RESULTS: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding > 100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. CONCLUSIONS: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans.

Adult↗

PubMed1998

Detection of suspected malignant patterns in three-dimensional magnetic resonance breast images.

In this article, a Boolean Neural Network (BNN) is used for the detection of suspected malignant regions in 3D breast magnetic resonance (MR) images. The BNN is characterized by fast learning and classification, guaranteed convergence, and simple, integer weight calculations. The BNN learning algorithm is incremental, which allows the addition and deletion of training patterns without unlearning those already learned. The incremental learning algorithm automatically reduces the training set and trains the network only with those examples estimated to be useful. The architecture is suitable for parallel hardware implementation using available Very Large Scale Integration (VLSI) technology. The BNN was trained by using a set of malignant, benign, and false-positive patterns, extracted by experts, from selected MR studies, by using an incremental learning algorithm. After training, the network was tested by means of a consistency checking test, cross validation techniques, and patterns from actual MR breast images. During the consistency test, the BNN was tested by using the same patterns used for training. The BNN classification accuracy in this case was 99.75%, proving the ability of the BNN to select useful patterns from the training set. Then, a leave one out cross-validation (LOOCV) test was done by using patterns from the training set and the classification accuracy was 90%. Next, an extended training set was created by shifting the original patterns in different directions. A cross-validation test was then performed by dividing the set of patterns into a training and a test set. Classification accuracy was compared to the nearest neighbor classifier. Results showed that the BNN achieved an average of 77% classification accuracy while requiring only 34% of the original training set. On the other hand, the nearest neighbor classifier achieved an accuracy of 57.9% while retaining the whole training set. Another test using actual MR slices different from the training set was done and results compared favorably to a radiologist's findings. Test results show the BNN's capability to detect suspected malignant regions in 3D MR images of the breast. The proposed BNN architecture can save the radiologist a great deal of time browsing MR slices searching for suspected malignancies.

Algorithms↗