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PubMed1999

A framework for building cooperative software agents in medical applications.

Exploiting the information technology may have a great impact on improving cooperation and interoperability among the different professionals taking part to the process of delivering health care services. New paradigms are therefore being devised considering software systems as autonomous agents able to help professionals in accomplishing their duties. To this aim those systems should encapsulate the skills for solving a given set of tasks and possess the social ability to cooperate in order to fetch the required information and knowledge. This paper illustrates a methodology facilitating the development of interoperable intelligent software agents for medical applications and proposes a generic computational model for implementing them. That model may be specialized in order to support all the different information and knowledge related requirements of a Hospital Information System. The architecture is being tested for implementing a prototype system able to coordinate the joint efforts of the professionals involved in managing patients affected by Acute Myeloid Leukemia.

Computer Simulation↗
PubMed2000

Computational models of association cortex.

Recent computational models, or mathematical realizations of neurobiological theories, are providing insights into the organization and workings of the association cortex. Such models concern the construction of cortical maps, the neural basis of cognitive functions such as visual perception, reward-motivated learning and some aspects of consciousness.

Animals↗
PubMed1992

A psychophysically motivated model for two-dimensional motion perception.

A quantitative model is developed to predict the perceived direction of moving two-dimensional patterns. The model incorporates both a simple motion energy pathway and a "texture boundary motion" pathway that incorporates response squaring before the extraction of motion energy. These pathways correspond to Fourier and non-Fourier motion pathways and are hypothesized to reflect processing in the V1-MT and V1-V2-MT pathway, respectively. A cosine-weighted sum of these pathways followed by competitive feedback inhibition accurately predicts the perceived direction for patterns composed of two cosine gratings at different orientations ("plaids"). The model also predicts direction discrimination, differences between foveal and peripheral viewing, changes in perceived direction with exposure duration, motion masking, and motion transparency.

Computer Simulation↗
PubMedDate not supplied

An analysis of the reliability phenomenon in the FitzHugh-Nagumo model.

The reliability of single neurons on realistic stimuli has been experimentally confirmed in a wide variety of animal preparations. We present a theoretical study of the reliability phenomenon in the FitzHugh-Nagumo model on white Gaussian stimulation. The analysis of the model's dynamics is performed in three regimes-the excitable, bistable, and oscillatory ones. We use tools from the random dynamical systems theory, such as the pullbacks and the estimation of the Lyapunov exponents and rotation number. The results show that for most stimulus intensities, trajectories converge to a single stochastic equilibrium point, and the leading Lyapunov exponent is negative. Consequently, in these regimes the discharge times are reliable in the sense that repeated presentation of the same aperiodic input segment evokes similar firing times after some transient time. Surprisingly, for a certain range of stimulus intensities, unreliable firing is observed due to the onset of stochastic chaos, as indicated by the estimated positive leading Lyapunov exponents. For this range of stimulus intensities, stochastic chaos occurs in the bistable regime and also expands in adjacent parts of the excitable and oscillating regimes. The obtained results are valuable in the explanation of experimental observations concerning the reliability of neurons stimulated with broad-band Gaussian inputs. They reveal two distinct neuronal response types. In the regime where the first Lyapunov has negative values, such inputs eventually lead neurons to reliable firing, and this suggests that any observed variance of firing times in reliability experiments is mainly due to internal noise. In the regime with positive Lyapunov exponents, the source of unreliable firing is stochastic chaos, a novel phenomenon in the reliability literature, whose origin and function need further investigation.

Computer Simulation↗
PubMedDate not supplied

Annihilation of single cell neural oscillations by feedforward and feedback control.

Annihilation of neural oscillation by localized electrical stimulation has been shown to be a promising treatment modality in a number of neural diseases like Parkinson disease or epilepsy. The contributions presented in this manuscript comprise newly developed stimulation schemes to achieve annihilation of action potential generation and action potential propagation. The ability to achieve oscillation annihilation is demonstrated in computer simulations with a single compartment nerve cell model (annihilation of action potential generation), and with a multi-compartment nerve fiber model (annihilation of action potential propagation). Additionally, we show superiority of the new feedback based schemes over the feedforward schemes in terms of effectiveness, phase robustness, and reduced sensitivity to disturbances. At the end we propose a conditioned switched feedback control regime to be applied in actual applications, where oscillation annihilation is needed.

Action Potentials↗
PubMed2000

Occlusion, symmetry, and object-based attention: reply to Saiki (2000).

J. Saiki (2000) argued that, because the stimuli used by M. Behrmann, R. S. Zemel, and M. C. Mozer (1998) were confounded by symmetry, conclusions about whether amodally completed objects can benefit from object-based attention are unwarranted. Here, the authors examine J. Saiki's claim further and expand on their view of the mechanisms underlying object-based attention, suggesting that perceptual organization is the process whereby features from a single object are selectively attended. In light of this, they claim that heuristics such as symmetry and collinearity play an important role in the facilitation of features from a single object. In support of this claim, they present data from a further experiment using displays that exploit common fate, another grouping heuristic, and show that, under these conditions, the hallmark of object-based attention, a single-object advantage, is obtained for the occluded (amodally completed) shapes.

Adult↗
PubMed2006

Neurophysiological theory of kamin blocking in fear conditioning.

Kamin blocking in fear conditioning is thought to reflect diminished processing of the unconditional stimulus (US) in the presence of a conditional stimulus (CS-super(+)) that was previously paired with this US. According to Fanselow's (1998) hypothesis, the CS-super(+) drives output from the amygdala that ultimately produces analgesia by causing opiate release onto afferent pain circuits. This hypothesis was explored quantitatively through neurophysiological simulations. The results suggest that opiate-mediated, negative-feedback control of US processing is too slow for efficient blocking of cue conditioning. The reason is that conditioning-produced synaptic modifications can be induced before the opiate-mediated inhibition has any substantial effect on US processing. The results suggest the existence of an additional, faster-acting, inhibitory neurotransmitter in the blocking circuit.

Action Potentials↗
PubMed2002

Computer vision and digital imaging technology in melanoma detection.

With today's treatment options, melanoma cure rates can be improved only if the diagnosis is made early enough to allow for curative surgery. Since clinical signs of malignancy in a pigmented lesion are often ambiguous and even dermatology experts may misdiagnose melanoma, diagnostic tools and procedures have been developed to assist the clinician in the diagnostic workup. Epiluminescence microscopy or dermatoscopy is widely used to inspect the melanin reticulum at the epidermo-dermal junction zone for signs indicative of early tumor growth. With the help of computer technology, digital dermatoscopy systems have entered the diagnostic arena capable of accurately assessing skin surface features modeled along the ABCD criteria already used for clinical assessment of pigmented skin lesions. Today's technically refined computer-based systems provide sophisticated functionalities for automated feature extraction and lesion assessment for quantitative analysis, and may also be used for education and training purposes.

Dermis↗
PubMed2002

Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise.

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location.

Brain↗
PubMed2003

Towards optimal views of proteins.

MOTIVATION: Graphical representations of proteins in online databases generally give default views orthogonal to the PDB file coordinate system. These views are often uninformative in terms of protein structure and/or function. Here we discuss the development of a simple automatic algorithm to provide a 'good' view of a protein domain with respect to its structural features. RESULTS: We used dimension reduction with the preservation of topology (using Kohonen's self organising map) to map 3D carbon alpha coordinates into 2D. The original protein structure was then rotated to the view which corresponded most closely to the 2D mapping. This procedure, which we call OVOP, was evaluated in a public blind trial on the web against random views and a 'flattest' view. The OVOP views were consistently rated 'better' than the other views by our volunteers. AVAILABILITY: The source code is available from the OVOP homepage: http://www.sbc.su.se/~oscar/ovop.

Algorithms↗
PubMed2001

Neuro-fuzzy systems for computer-aided myocardial viability assessment.

This paper describes a multimodality framework for computer-aided myocardial viability assessment based on neuro-fuzzy techniques. The proposed approach distinguishes two main levels: the modality-independent inference level and the modality-dependent application level. This two-level distinction releases the hard constraint of multimodality image registration. An abstract description template is used to describe the different myocardial functions (contractile function, perfusion, metabolism). Parameters extracted from different image modalities are combined to derive a diagnostic image. The neuro-fuzzy techniques make our system transparent, adaptive and easily extendable. Its effectiveness and robustness are demonstrated in a positron emission tomography/magnetic resonance imaging data fusion application.

Artificial Intelligence↗
PubMed2004

Adaptive stochastic resonance in noisy neurons based on mutual information.

Noise can improve how memoryless neurons process signals and maximize their throughput information. Such favorable use of noise is the so-called "stochastic resonance" or SR effect at the level of threshold neurons and continuous neurons. This paper presents theoretical and simulation evidence that 1) lone noisy threshold and continuous neurons exhibit the SR effect in terms of the mutual information between random input and output sequences, 2) a new statistically robust learning law can find this entropy-optimal noise level, and 3) the adaptive SR effect is robust against highly impulsive noise with infinite variance. Histograms estimate the relevant probability density functions at each learning iteration. A theorem shows that almost all noise probability density functions produce some SR effect in threshold neurons even if the noise is impulsive and has infinite variance. The optimal noise level in threshold neurons also behaves nonlinearly as the input signal amplitude increases. Simulations further show that the SR effect persists for several sigmoidal neurons and for Gaussian radial-basis-function neurons.

Algorithms↗
PubMed2004

Hidden space support vector machines.

Hidden space support vector machines (HSSVMs) are presented in this paper. The input patterns are mapped into a high-dimensional hidden space by a set of hidden nonlinear functions and then the structural risk is introduced into the hidden space to construct HSSVMs. Moreover, the conditions for the nonlinear kernel function in HSSVMs are more relaxed, and even differentiability is not required. Compared with support vector machines (SVMs), HSSVMs can adopt more kinds of kernel functions because the positive definite property of the kernel function is not a necessary condition. The performance of HSSVMs for pattern recognition and regression estimation is also analyzed. Experiments on artificial and real-world domains confirm the feasibility and the validity of our algorithms.

Algorithms↗
PubMed2005

Incremental training of support vector machines.

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.

Algorithms↗