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Daniel Tranchina

Researcher at New York University

Publications -  60
Citations -  5028

Daniel Tranchina is an academic researcher from New York University. The author has contributed to research in topics: Population & Receptive field. The author has an hindex of 31, co-authored 59 publications receiving 4675 citations. Previous affiliations of Daniel Tranchina include Albert Einstein College of Medicine & Center for Neural Science.

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Channel modulation and the mechanism of light adaptation in mouse rods.

TL;DR: A kinetic model in which light produces a Ca2+-mediated decrease in PDE 6 decay rate, with the novel feature that both spontaneously activated and light-activated PDE6 are modulated, can successfully account for changes in sensitivity and response waveform in background light.
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A comparison of release kinetics and glutamate receptor properties in shaping rod-cone differences in EPSC kinetics in the salamander retina.

TL;DR: Rod–cone differences in the rates of exocytosis are a major factor in producing faster cone‐driven responses in second‐order retinal neurones, and release kinetics shape the EPSC.
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A Systems Approach Uncovers Restrictions for Signal Interactions Regulating Genome-wide Responses to Nutritional Cues in Arabidopsis

TL;DR: Surprisingly, global analysis of gene expression in response to C, N, L, and O revealed that the number of genes controlled by a signal is proportional to the magnitude of the gene expression changes elicited by the signal, which prompted the existence of a "code" of signal integration.
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Origin of the apparent tissue conductivity in the molecular and granular layers of the in vitro turtle cerebellum and the interpretation of current source-density analysis

TL;DR: The result implies that the effective sigma e may be larger than sigma a for neuronally generated currents in the turtle cerebellum because the space constant for Purkinje cells is several times greater than that for the ependymal glia and consequently Purkinja cell-generated currents travel over a long distance relative to the space constants of glial cells.
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Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods.

TL;DR: A pair of coupled partial differential–integral equations describing the evolution of PDFs for neurons in non-refractory and refractory pools are formulated and an operator-splitting method is used to reduce computation time.