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Found 19 papers, 1 papers with code
Spatiotemporal modeling of grip forces captures proficiency in manual robot control
This paper builds on our previous work by exploiting Artificial Intelligence to predict individual grip force variability in manual robot control.
The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience
Two universal functional principles of Adaptive Resonance Theory simulate the brain code of all biological learning and adaptive intelligence.
Grip force as a functional window to somatosensory cognition
Analysis of grip force signals tailored to hand and finger movement evolution and changes in grip force control during task execution provide unprecedented functional insight into somatosensory cognition.
From Biological Synapses to Intelligent Robots
This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples.
Brain representation of perceptual stimuli at different levels of awareness
This article questions the widespread assumption that there are brain representations that will always remain unconscious in the sense of being inaccessible to individual awareness under any circumstances.
Consciousness beyond neural fields: expanding the possibilities of what has not yet happened
In the field theories in physics, any particular region of the presumed space-time continuum and all interactions between elementary objects therein can be objectively measured and/or accounted for mathematically.
The contribution of local variations in hue or contrast to symmetry of things in a thing
The stimuli are computer generated 2D shape configurations consisting of multiple elements, with and without systematic variations in local color, color saturation, or achromatic contrast producing variations in symmetry of things in a thing.
Unsupervised classification of cell imaging data using the quantization error in a Self Organizing Map
Across differences in relative luminance, the SOM QE exhibits consistently greater sensitivity to the smallest spatial increases in RED image pixels compared with smallest increases of identical spatial extents in GREEN image pixels.
Surgical task expertise detected by a self-organizing neural network map
Individual grip force profiling of bimanual simulator task performance of experts and novices using a robotic control device designed for endoscopic surgery permits defining benchmark criteria that tell true expert task skills from the skills of novices or trainee surgeons.
Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map
To this end, we exploit a neural network metric in the output of a biologically inspired Self Organizing Map, the Quantization Error (SOM QE).
Occams Razor for Big Data? On Detecting Quality in Large Unstructured Datasets
This new trend towards analytic complexity represents a severe challenge for the principle of parsimony or Occams Razor in science.
Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data
Cellular and molecular imaging techniques and models have been developed to characterize single stages of viral proliferation after focal infection of cells in vitro.
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images.
Children's health in the digital age
Environmental studies, metabolic research, and state of the art in neurobiology point towards the reduced amount of natural day and sunlight exposure of the developing childs organism, as a consequence of increasingly long hours spent indoors online, as the single unifying source of a whole set of health risks identified worldwide, as is made clear in this review of the current literature.
Computers and Society
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection
Results show quantization errors increased with the increase in lesions on the images.
Unsupervised automatic classification of Scanning Electron Microscopy (SEM) images of CD4+ cells with varying extent of HIV virion infection
Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell in a certain order, indicating progression or recession of a pathology, or the progressive response of a cell structure to treatment.
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years
Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers.
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute
The quantization error (QE) from SOM applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images, is proven a reliable indicator of potentially critical changes in image homogeneity.
On the detectability by novices, radiologists, and computer algorithms of smallest increases in local single dot size in random-dot images
Time-series of images may reveal important information about changes in medical or environmental conditions, depending on context.
Computers and Society
