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High-probability grants
According to our matching algorithm, Daniel Tranchina is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
1986 — 1988 |
Tranchina, Daniel A |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Light Adaptation in Cones and Horizontal Cells
One objective of this project is the elucidation of the mechanisms underlying light adaptation, a process in which retinal response properties adjust to variations in ambient illumination. The effects of light adaptation on the sensitivity and dynamics of visual responses of neurons in the turtle retina will be studied. This will involve recording intracellularly the temporal frequency responses of cones and horizontal cells (H-cells) to light that is modulated sinusoidally around various mean levels. One hypothesis to be tested is that changes in the sensitivity and dynamics of cone responses, as the mean light level is varied, can be accounted for by a simple phenomenological feedback model in which the strength of a feedback signal is adjusted to be proportional to the mean light level. This model has already been shown to account for the linear component of H-cell responses to modulation of light around a wide range of mean levels. There are two candidates for this alleged feedback signal: neural feedback from H-cells to cones and chemical feedback within the cone phototransduction mechanism. The role of neural feedback will be studied by controlling in various ways both the steady and the transient component of H-cell feedback, while monitoring the effects on cone adaptation. Another hypothesis to be tested is that a somewhat more elaborate feedback model, in which the dynamics of adaptation are included, can account for both linear and nonlinear components of cone and H-cell responses over a wide range of light levels. In light of recent progress in unraveling the molecular basis of phototransduction, an attempt will be made to make a connection between the phenomenological feedback model and biochemical mechanisms. The goal is to develop biochemical kinetic models for cone phototransduction that are consistent with the known biochemistry and also with the electrophysiological behavior of cones. The feedback model with its highly distinctive mathematical form will provide a powerful tool for testing such models. The effects of light adaptation on the receptive field properties of cones will also be studied; cone spatial frequency responses to drifting sinusoidal gratings superimposed on various background light levels will be measured.
|
0.958 |
1990 — 1994 |
Tranchina, Daniel Sakai, Hiroko Naka, Ken-Ichi |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Signal Processing in the Retinal Neuron Network: Quantitative Analysis and Mathematical Modeling @ New York University Medical Center
The vertebrate eye transduces visual information into a series of electrical impulses which are transmitted by nerve cells to higher brain centers. Therefore, it is important to understand how the retina of the eye takes images and converts them, through a series of nerve networks, into signals which the brain can process. This is a problem both of physiology and mathematics. This project will study the input-output relationships of the vertebrate retina, that is, light to nerve cell and nerve to nerve cell. This is the first stage of visual transduction and integration. The project will focus on the mathematical analysis of the input-output system in order to develop models of how light is transformed into nerve impulses. The mathematical and computer-modeling aspects will be done in conjunction with ongoing studies of retinal physiology. These studies will contribute to our understanding of light and image transduction and how retinal neural networks initially process visual information. The results should provide valuable information about the normal function of the eye and, possibly, what factors are involved in visual defects.
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1 |
2002 — 2005 |
Tranchina, Daniel |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Population Density Methods and Applications in Large-Scale Modeling of Neural Networks
Computational models of networks of neurons in the brain usually are based on following the details of activity patterns in large numbers of individually explicit neuronal elements and their synapses. Simulations of activity in functional units based on hundreds or even thousands of such neural units, such as a column within the sensory cortex, can take hours of computer time to model seconds of real time. The goal of this project is to develop new techniques to facilitate large-scale modeling of neural networks in the mammalian brain. The theory of probability (population) density function, borrowed from the field of statistical mechanics, uses large numbers of elements to advantage. In the population density method, similar neurons are lumped together in a population, and one tracks the distribution of neurons over 'state space' in each population. The state of a neuron is determined by the dynamic variables in the underlying single-neuron model, to allow deriving a population firing rate from a flux of probability across a particular surface in state space, taking into account coupling between neurons by excitatory and inhibitory input events at stochastic synapses. The present project will extend the population density theory by incorporating realistic synaptic kinetics. This consequent increased computational complexity will lead to developing and testing new simulation methods to compare with conventional direct simulations for accuracy and speed. Population density methods will be applied to the well known primary visual cortex, including interactions among cortical layers and columns, to account for physiologically known features such as sensitivity to orientation of visual bar stimuli, and the relation of responses to stimulus contrast levels. Results will have an impact on visual neuroscience as well as computational neuroscience, and on understanding information processing in the brain in general. These new methods could accelerate network simulations by orders of magnitude, with the promise of applications in computer and information sciences. Cross-disciplinary graduate training is an important added feature of this project.
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1 |