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David Tsai

Researcher at University of New South Wales

Publications -  49
Citations -  884

David Tsai is an academic researcher from University of New South Wales. The author has contributed to research in topics: Retinal ganglion & Retinal ganglion cell. The author has an hindex of 14, co-authored 45 publications receiving 684 citations. Previous affiliations of David Tsai include Howard Hughes Medical Institute & Harvard University.

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A very large-scale microelectrode array for cellular-resolution electrophysiology

TL;DR: An array of 65k simultaneously recording and stimulating electrodes is built and used to sort and classify single neurons across the entire mouse retina and is applicable to other electrophysiological systems and electrode configurations.
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Direct Activation and Temporal Response Properties of Rabbit Retinal Ganglion Cells Following Subretinal Stimulation

TL;DR: The results suggest that for subretinal vision prostheses short pulses are preferable for efficacy and safety considerations, and that direct activation of RGCs will be necessary for reliable activation during high-frequency stimulation.
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Targeted intracellular voltage recordings from dendritic spines using quantum-dot-coated nanopipettes

TL;DR: Using simultaneous somato-spine electrical recordings, it is found that back propagating action potentials fully invade spines, that excitatory postsynaptic potentials are large in the spine head but are strongly attenuated at the soma, and that the estimated neck resistance is large enough to generate significant voltage compartmentalization.
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Current steering in retinal stimulation via a quasimonopolar stimulation paradigm.

TL;DR: Results of in vivo studies showed that quasimonopolar stimulation can be used to maintain the activation containment properties of hexapolar stimulation, while lowering the activation threshold to values almost equivalent to those of monopolar stimulation.
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Responses of Retinal Ganglion Cells to Extracellular Electrical Stimulation, from Single Cell to Population: Model-Based Analysis

TL;DR: Simulation of morphologically and biophysically detailed computational RGC models on high performance computing clusters found that at the single cell level the electric potential gradient in conjunction with neuronal element excitability, rather than the electrode center location per se, determined the response threshold and latency.