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PubMedDate not supplied

A comparison of semiempirical models for generating tungsten target x-ray spectra.

Models for the computer generation of tungsten target x-ray spectra proposed by Birch and Marshall [Phys. Med. Biol. 24, 505-517 (1979)] and recently by Tucker, Barnes, and Chakraborty [Med. Phys. 18 211-218 (1991)] are compared. Some basic differences in the equations for the number of bremsstrahlung photons of different energies in the spectra are discussed. The models are compared in terms of their ability to characterize x-ray spectra from constant potential clinical units using three parameter equivalent spectra (EQSPEC) determined from the fit of model generated transmission curves through aluminum to measured data. The Kramers model for x-ray generation is included for completeness. It is shown that two of the models generate very similar x-ray spectra from given transmission curves although the fitting parameters in the EQSPEC characterization differ.

Computer Simulation↗
PubMed1985

A two dimensional model for saccade generation.

A model for the generation of oblique saccades is constructed by extending and modifying the one dimensional local feedback model. It is proposed that the visual system stores target location in inertial coordinates, but that the feedback loop which guides saccades works in retinotopic coordinates. To achieve straight trajectories for centripetal and centrifugal saccades in all meridians, a comparator computes motor error as a vector and uses the vectorial error signal to drive two orthogonally-acting burst generators. The generation of straight saccade trajectories when the extraocular muscles are of unequal strengths requires the introduction of a burst-tonic cell input to motor neurons. The model accounts for the results of two-site stimulation of the superior colliculus and frontal eye fields by allowing simultaneous activation of more than one comparator. The postulated existence of multiple comparators suggests that motor error may be computed topographically.

Animals↗
PubMed1986

A spike generator mechanism model simulates utricular afferents response to sinusoidal vibrations.

Using a model of spike generator mechanism (SGM) with a variable threshold we simulate the responses of utricular afferents to sinusoidal vibrations. It reproduces the phase locking characteristics (bifurcations diagrams) and the stimulus frequency firing rate relationships of different types of utricular afferents. We estimate the model parameters selecting the values which best fit the experimental results and we compare them with those from basic mechanisms involved in utricular codification.

Afferent Pathways↗
PubMedDate not supplied

Semiempirical model for generating tungsten target x-ray spectra.

A semiempirical model for generating tungsten target x-ray spectra is presented. This model extends earlier work in two significant areas. First, both bremsstrahlung and characteristic x-ray production are assumed to occur at varying depths within the target. Second, optimal parameters for the model were determined from experimental spectra utilizing nonlinear least-squares techniques. As a result, good agreement is obtained between calculated and measured x-ray tube spectra and output for different target angles and a wide range of x-ray tube potentials. Such is not the case with previously published models.

Mathematics↗
PubMed1978

Sex-linked genes in age-structured populations.

We study the progress towards equilibrium of the frequencies of sex-linked genes in elementary discrete time models of age-structured, overlapping generation populations. It is found that, if a finite upper age limit is assumed, the difference in the frequencies of an allele in males and females will oscillate as in the familiar non-overlapping generation models, although the oscillations may be irregular. Monotonic convergence of that difference, as found by Nagylaki (1975) in continuous-time overlapping generation models without age-structure, occurs in the models considered here only when there is no upper age limit and when there is "sufficient" overlap of generations.

Age Factors↗
PubMed1992

Possible model for generation of P9 far-field potentials.

The distribution of P9 far-field somatosensory evoked potentials, after stimulation of the median nerve with a knee reference, was examined to determine the mechanism of the generation of P9 potentials. In addition to positive potentials (P9s), we found a negative potential (N9) recorded from the chest ipsilateral to the stimulation. The simulation of the distribution of these P9/N9 potentials by an electrical circuit diagram suggested the validity of this model for generation of the P9s.

Electric Stimulation↗
PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗
PubMed1986

Sequential state generation by model neural networks.

Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks.

Artificial Intelligence↗
PubMed1989

Probabilistic division systems modeling the generation of mosaic fields.

The explanation of mosaic pattern in chimeric organs analyzed by in situ methods requires modeling of specific hypotheses. The use of computer simulations to achieve this has led to the conclusion that finely variegated mixtures of cell lineage within chimeric tissues does not require extensive cell movement. Cell division models were used to determine the distribution of patch size as mosaic fields are generated. The results establish that these distributions are sensitive to the proportion of the two cell types which comprise the mosaic.

Animals↗
PubMed1992

Fast visual generation: its nature and chronometrics.

The fast-generation model for the matching of mixed-case letter pairs (e.g., Aa, Ab) states that one or both members of a pair activate visual representations in memory of the opposite case, supporting "same" or "different" responses through crossmatching to representations of the pair members themselves. Here the reaction time and error results of three experiments using simultaneous matches support a specific variant of the model in which generation proceeds from the uppercase letter. Furthermore, a manipulation of stimulus onset asynchrony in a fourth experiment using near-simultaneous matches indicates that fast generation produces a visual representation that occurs within 67 msec of initiation and that decays within 200 msec. A fifth experiment contrasts simultaneous and successive matches and in the case of successive matches finds evidence in support of a regeneration process acting after an initial decay. Models of mixed-case matching that are based on the phonetic representation of letter names, or on abstract-letter identities, completely fail to account for the results. Fast generation is distinguishable from slow generation in that it shows fast (vs. slow) dynamics, rapid decay (vs. maintainability), no imagery (vs. imagery), and (probably) automatic (vs. controlled) processing.

Attention↗