The Experts below are selected from a list of 450852 Experts worldwide ranked by ideXlab platform
Wayne C Drevets - One of the best experts on this subject based on the ideXlab platform.
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Neural systems underlying voluntary and automatic emotion regulation toward a Neural Model of bipolar disorder
Molecular Psychiatry, 2008Co-Authors: M. L. Phillips, Cecile D. Ladouceur, Wayne C DrevetsAbstract:Neural systems underlying voluntary and automatic emotion regulation: toward a Neural Model of bipolar disorder
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A Neural Model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder
Molecular Psychiatry, 2008Co-Authors: M. L. Phillips, Cecile D. Ladouceur, Wayne C DrevetsAbstract:The ability to regulate emotions is an important part of adaptive functioning in society. Advances in cognitive and affective neuroscience and biological psychiatry have facilitated examination of Neural systems that may be important for emotion regulation. In this critical review we first develop a Neural Model of emotion regulation that includes Neural systems implicated in different voluntary and automatic emotion regulatory subprocesses. We then use this Model as a theoretical framework to examine functional Neural abnormalities in these Neural systems that may predispose to the development of a major psychiatric disorder characterized by severe emotion dysregulation, bipolar disorder.
Jacek Lubczonek - One of the best experts on this subject based on the ideXlab platform.
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ICAISC - Hybrid Neural Model of the Sea Bottom Surface
Lecture Notes in Computer Science, 2004Co-Authors: Jacek LubczonekAbstract:Paper presents method of construction Neural Model of the sea bottom. Constructed Model consisted of set of smaller Models (local approximators). For Model approximation was used set of RBF networks with various kernels, what enabled approximation of the entire Model by networks with different structure. Experimental results show that in this way we can obtain better results than applying Neural Model based on local approximators with the same structure.
M. L. Phillips - One of the best experts on this subject based on the ideXlab platform.
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Neural systems underlying voluntary and automatic emotion regulation toward a Neural Model of bipolar disorder
Molecular Psychiatry, 2008Co-Authors: M. L. Phillips, Cecile D. Ladouceur, Wayne C DrevetsAbstract:Neural systems underlying voluntary and automatic emotion regulation: toward a Neural Model of bipolar disorder
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A Neural Model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder
Molecular Psychiatry, 2008Co-Authors: M. L. Phillips, Cecile D. Ladouceur, Wayne C DrevetsAbstract:The ability to regulate emotions is an important part of adaptive functioning in society. Advances in cognitive and affective neuroscience and biological psychiatry have facilitated examination of Neural systems that may be important for emotion regulation. In this critical review we first develop a Neural Model of emotion regulation that includes Neural systems implicated in different voluntary and automatic emotion regulatory subprocesses. We then use this Model as a theoretical framework to examine functional Neural abnormalities in these Neural systems that may predispose to the development of a major psychiatric disorder characterized by severe emotion dysregulation, bipolar disorder.
M. Kuperstein - One of the best experts on this subject based on the ideXlab platform.
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ICRA - Generalized Neural Model for adaptive sensory-motor control of single postures
Proceedings. 1988 IEEE International Conference on Robotics and Automation, 1Co-Authors: M. KupersteinAbstract:A Neural-network Model has been developed that achieves adaptive visual-motor coordination of a multijoint arm, without a teacher. The Model has been applied to adaptively positioning an arm so that it reaches a cylinder arbitrarily positioned in space. The Model uses a Neural architecture and an algorithm for modifying Neural-connection strengths. Computer simulations show that the Model performs with an average position error of 4% of the arm's length and with an average orientation error of 4 degrees . The Model is designed to be generalized for coordinating any number of topographic sensory inputs with limbs of any number of joints. The general scheme of the Neural Model is proposed. >
Stephen Grossberg - One of the best experts on this subject based on the ideXlab platform.
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a Neural Model of how the brain computes heading from optic flow in realistic scenes
CAS CNS Technical Report Series, 2010Co-Authors: Andrew N Browning, Stephen Grossberg, Ennio MingollaAbstract:Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a Neural Model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The Model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The Model estimates heading to within 1.5deg; in random dot or photo-realistically rendered scenes and within 3deg; in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect Model performance, but faster simulated rotation rates deteriorate performance, as in humans. The Model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.
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a Neural Model of how the brain computes heading from optic flow in realistic scenes
Cognitive Psychology, 2009Co-Authors: Andrew N Browning, Stephen Grossberg, Ennio MingollaAbstract:Abstract Visually-based navigation is a key competence during spatial cognition. Animals avoid obstacles and approach goals in novel cluttered environments using optic flow to compute heading with respect to the environment. Most navigation Models try either explain data, or to demonstrate navigational competence in real-world environments without regard to behavioral and Neural substrates. The current article develops a Model that does both. The ViSTARS Neural Model describes interactions among neurons in the primate magnocellular pathway, including V1, MT + , and MST d . Model outputs are quantitatively similar to human heading data in response to complex natural scenes. The Model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes, and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1°/s do not affect heading estimates, but faster simulated rotation rates do, as in humans. The Model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.
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A Neural Model of surface perception: lightness, anchoring, and filling-in.
Spatial vision, 2006Co-Authors: Stephen Grossberg, Simon HongAbstract:A Neural Model is proposed of how the visual system processes natural images under variable illumination conditions to generate surface lightness percepts. Previous Models clarify how the brain can compute relative contrast. The anchored Filling-In Lightness Model (aFILM) clarifies how the brain 'anchors' lightness percepts to determine an absolute lightness scale that uses the full dynamic range of neurons. The Model quantitatively simulates lightness anchoring properties (Articulation, Insulation, Configuration, Area Effect) and other lightness data (discounting the illuminant, the double brilliant illusion, lightness constancy and contrast, Mondrian contrast constancy, Craik-O'Brien-Cornsweet illusion). The Model clarifies how retinal processing stages achieve light adaptation and spatial contrast adaptation, and how cortical processing stages fill-in surface lightness using long-range horizontal connections that are gated by boundary signals. The new filling-in mechanism runs 1000 times faster than diffusion mechanisms of previous filling-in Models.
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a Neural Model of saccadic eye movement control explains task specific adaptation
Vision Research, 1999Co-Authors: Gregory Gancarz, Stephen GrossbergAbstract:Multiple brain learning sites are needed to calibrate the accuracy of saccadic eye movements. This is true because saccades can be made reactively to visual cues, attentively to visual or auditory cues, or planned in response to memory cues using visual, parietal, and prefrontal cortex, as well as superior colliculus, cerebellum, and reticular formation. The organization of these sites can be probed by displacing a visual target during a saccade. The resulting adaptation typically shows incomplete and asymmetric transfer between different tasks. A Neural Model of saccadic system learning is developed to explain these data, as well as data about saccadic coordinate changes.
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a Neural Model of multimodal adaptive saccadic eye movement control by superior colliculus
The Journal of Neuroscience, 1997Co-Authors: Stephen Grossberg, Karen Roberts, Mario Aguilar, Daniel BullockAbstract:How does the saccadic movement system select a target when visual, auditory, and planned movement commands differ? How do retinal, head-centered, and motor error coordinates interact during the selection process? Recent data on superior colliculus (SC) reveal a spreading wave of activation across buildup cells the peak activity of which covaries with the current gaze error. In contrast, the locus of peak activity remains constant at burst cells, whereas their activity level decays with residual gaze error. A Neural Model answers these questions and simulates burst and buildup responses in visual, overlap, memory, and gap tasks. The Model also simulates data on multimodal enhancement and suppression of activity in the deeper SC layers and suggests a functional role for NMDA receptors in this region. In particular, the Model suggests how auditory and planned saccadic target positions become aligned and compete with visually reactive target positions to select a movement command. For this to occur, a transformation between auditory and planned head-centered representations and a retinotopic target representation is learned. Burst cells in the Model generate teaching signals to the spreading wave layer. Spreading waves are produced by corollary discharges that render planned and visually reactive targets dimensionally consistent and enable them to compete for attention to generate a movement command in motor error coordinates. The attentional selection process also helps to stabilize the map-learning process. The Model functionally interprets cells in the superior colliculus, frontal eye field, parietal cortex, mesencephalic reticular formation, paramedian pontine reticular formation, and substantia nigra pars reticulata.