activity
20092015
most citedSparsity-based single-shot sub-wavelength coherent diffractive imaging

217 citations · 375 across the 6 of their papers we have counts for

collaborators

6 papers

q-bio.NC2015★ 6 cited

Functional optoacoustic neuro-tomography (FONT) for whole-brain monitoring of calcium indicators

Gali Sela, Antonella Lauri, X. Luís Deán-Ben +5

Non-invasive observation of spatiotemporal neural activity of large neural populations distributed over entire brains is a longstanding goal of neuroscience. We developed a real-ti…

q-bio.NC2013★ 143 cited

Intramembrane Cavitation as a Predictive Bio-Piezoelectric Mechanism for Ultrasonic Brain Stimulation

Michael Plaksin, Shy Shoham, Eitan Kimmel

Low-intensity ultrasonic waves can remotely and nondestructively excite central nervous system (CNS) neurons. While diverse applications for this effect are already emerging, the b…

q-bio.NC2012★ 3 cited

Photo-Thermal Neural Excitation by Extrinsic and Intrinsic Absorbers: A Temperature-Rate Model

Nairouz Farah, Inbar Brosh, Christopher R. Butson +1

Infrared neural stimulation (INS) pulses at water-absorbed mid-IR wavelengths could provide a non-invasive and safe modality for stimulating peripheral and cranial nerves and centr…

physics.optics2011★ 217 cited

Sparsity-based single-shot sub-wavelength coherent diffractive imaging

A. Szameit, Y. Shechtman, E. Osherovich +13

We present the experimental reconstruction of sub-wavelength features from the far-field intensity of sparse optical objects: sparsity-based sub-wavelength imaging combined with ph…

physics.optics2010★ 3 cited

Far-Field Microscopy of Sparse Subwavelength Objects

A. Szameit, Y. Shechtman, H. Dana +9

We present the experimental reconstruction of sub-wavelength features from the far-field of sparse optical objects. We show that it is sufficient to know that the object is sparse,…

q-bio.NC2009★ 3 cited

Correlation-distortion based identification of Linear-Nonlinear-Poisson models

Michael Krumin, Avner Shimron, Shy Shoham

Linear-Nonlinear-Poisson (LNP) models are a popular and powerful tool for describing encoding (stimulus-response) transformations by single sensory as well as motor neurons. Recent…