Sensory Communication

Contains table of contents for Section 2, an introduction and reports on twelve research projects.National Institutes of Health Grant R01 DC00117National Institutes of Health Grant R01 DC02032National Institutes of Health/National Institute of Deafness and Other Communication Disorders Grant 2 R01 DC00126National Institutes of Health Grant 2 R01 DC00270National Institutes of Health Contract N01 DC-5-2107National Institutes of Health Grant 2 R01 DC00100U.S. Navy - Office of Naval Research Grant N61339-96-K-0002U.S. Navy - Office of Naval Research Grant N61339-96-K-0003U.S. Navy - Office of Naval Research Grant N00014-97-1-0635U.S. Navy - Office of Naval Research Grant N00014-97-1-0655U.S. Navy - Office of Naval Research Subcontract 40167U.S. Navy - Office of Naval Research Grant N00014-96-1-0379U.S. Air Force - Office of Scientific Research Grant F49620-96-1-0202National Institutes of Health Grant RO1 NS33778Massachusetts General Hospital, Center for Innovative Minimally Invasive Therapy Research Fellowship Gran

HIERARCHICAL NEURAL COMPUTATION IN THE MAMMALIAN VISUAL SYSTEM

Our visual system can efficiently extract behaviorally relevant information from ambiguous and noisy luminance patterns. Although we know much about the anatomy and physiology of the visual system, it remains obscure how the computation performed by individual visual neurons is constructed from the neural circuits. In this thesis, I designed novel statistical modeling approaches to study hierarchical neural computation, using electrophysiological recordings from several stages of the mammalian visual system. In Chapter 2, I describe a two-stage nonlinear model that characterized both synaptic current and spike response of retinal ganglion cells with unprecedented accuracy. I found that excitatory synaptic currents to ganglion cells are well described by excitatory inputs multiplied by divisive suppression, and that spike responses can be explained with the addition of a second stage of spiking nonlinearity and refractoriness. The structure of the model was inspired by known elements of the retinal circuit, and implies that presynaptic inhibition from amacrine cells is an important mechanism underlying ganglion cell computation. In Chapter 3, I describe a hierarchical stimulus-processing model of MT neurons in the context of a naturalistic optic flow stimulus. The model incorporates relevant nonlinear properties of upstream V1 processing and explained MT neuron responses to complex motion stimuli. MT neuron responses are shown to be best predicted from distinct excitatory and suppressive components. The direction-selective suppression can impart selectivity of MT neurons to complex velocity fields, and contribute to improved estimation of the three-dimensional velocity of moving objects. In Chapter 4, I present an extended model of MT neurons that includes both the stimulus-processing component and network activity reflected in local field potentials (LFPs). A significant fraction of the trial-to-trial variability of MT neuron responses is predictable from the LFPs in both passive fixation and a motion discrimination task. Moreover, the choice-related variability of MT neuron responses can be explained by their phase preferences in low-frequency band LFPs. These results suggest an important role of network activity in cortical function. Together, these results demonstrated that it is possible to infer the nature of neural computation from physiological recordings using statistical modeling approaches

Modular analysis of signal transduction networks

Modularity, signaling networks, sytems biologyMagdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2007von Julio Sáez RodríguezZsfassung in dt. Sprach

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis