scholarly journals Single-shot depth profiling by spatio-temporal encoding with a multimode fiber

2020 ◽  
Vol 28 (2) ◽  
pp. 1124 ◽  
Author(s):  
Szu-Yu Lee ◽  
Pui-Chuen Hui ◽  
Brett Bouma ◽  
Martin Villiger
Keyword(s):  
Depth Profiling ◽  
Single Shot ◽  
Multimode Fiber ◽  
Temporal Encoding ◽  
Spatio Temporal

Overcoming limitations in diffusion-weighted MRI of breast by spatio-temporal encoding

2014 ◽  
Vol 73 (6) ◽  
pp. 2163-2173 ◽  
Author(s):  
Eddy Solomon ◽  
Noam Nissan ◽  
Edna Furman-Haran ◽  
Amir Seginer ◽  
Myra Shapiro-Feinberg ◽  
...  
Keyword(s):  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Temporal Encoding ◽  
Spatio Temporal

scholarly journals Spatio-temporal encoding by quadratic gradients in magnetic resonance imaging

2019 ◽  
Author(s):  
Sina Marhabaie ◽  
Geoffrey Bodenhausen ◽  
Philippe Pelupessy
Keyword(s):  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Magnetic Field Gradient ◽  
Quadratic Function ◽  
Transverse Magnetization ◽  
Chirp Pulse ◽  
Simultaneous Application ◽  
Quadratic Phase ◽  
Temporal Encoding ◽  
Spatio Temporal

Abstract. SPatio-temporal ENcoding (SPEN) MRI is a non-Fourier imaging technique that encodes the spatial information in such a way that there is a one-to-one correspondence between the signal intensity as a function of time and the spin density at the corresponding position. In current spatio-temporal encoding methods imparting a quadratic phase – that is the phase of the transverse magnetization depends as a quadratic function of the spatial coordinates – onto the transverse magnetization is the first crucial step. Usually, this is achieved by simultaneous application of a frequency-swept (chirp) pulse and a linear magnetic field gradient. In this work, we show that it can be advantageous to use quadratic encoding gradients for this purpose. By avoiding chirp pulses one can achieve much smaller specific absorption rates (SARs), and shorter echo times (TEs), while the spatial resolution, the field of view (FOV), and the signal-to-noise ratio (SNR) are the same as in SPEN if one uses similar parameters. In addition, the proposed sequence can readily be used for multi-slice applications.

scholarly journals Spatio-temporal encoding improves neuromorphic tactile texture classification

2021 ◽  
pp. 1-1
Author(s):  
Anupam K. Gupta ◽  
Andrei Nakagawa ◽  
Nathan F. Lepora ◽  
Nitish V. Thakor
Keyword(s):  
Texture Classification ◽  
Temporal Encoding ◽  
Spatio Temporal

scholarly journals Unsupervised Learning of Temporal Features for Word Categorization in a Spiking Neural Network Model of the Auditory Brain

2016 ◽  
Author(s):  
Irina Higgins ◽  
Simon Stringer ◽  
Jan Schnupp
Keyword(s):  
Neural Network ◽  
Auditory Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Spiking Neural Network ◽  
Cortical Areas ◽  
Speaker Independent ◽  
Temporal Encoding ◽  
Spatio Temporal ◽  
Spiking Neural Network Model

AbstractThe nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.Author SummaryCurrently we still do not know how the auditory cortex encodes the identity of complex auditory objects, such as words, given the great variability in the raw auditory waves that correspond to the different pronunciations of the same word by different speakers. Here we argue for temporal information encoding within neural cell assemblies for representing auditory objects. Unlike the more traditionally accepted rate encoding, temporal encoding takes into account the precise relative timing of spikes across a population of neurons. We provide support for our hypothesis by building a neurophysiologically grounded spiking neural network model of the auditory brain with a biologically plausible learning mechanism. We show that the model learns to differentiate between naturally spoken digits “one” and “two” pronounced by numerous speakers in a speaker-independent manner through simple unsupervised exposure to the words. Our simulations demonstrate that temporal encoding contains significantly more information about the two words than rate encoding. We also show that such learning depends on the presence of stable patterns of firing in the input to the cortical areas of the model that are performing the learning.

scholarly journals <sup>129</sup>Xe Ultrafast Z-spectroscopy enables micromolar detection of biosensors on a 1T benchtop spectrometer

2021 ◽  
Author(s):  
Kévin Chighine ◽  
Estelle Léonce ◽  
Céline Boutin ◽  
Hervé Desvaux ◽  
Patrick Berthault
Keyword(s):  
Chemical Exchange ◽  
Low Cost ◽  
Indirect Detection ◽  
Low Field ◽  
Cage Molecules ◽  
Temporal Encoding ◽  
Spatio Temporal

Abstract. The availability of a benchtop NMR spectrometer, of low cost and easily transportable, can allow detection of low quantities of biosensors, provided that hyperpolarized species are used. Here we show that the micromolar threshold can easily be reached, by employing laser-polarized xenon and cage-molecules reversibly hosting it. Indirect detection of caged xenon is made via chemical exchange, using ultrafast Z-spectroscopy based on spatio-temporal encoding. On this non-dedicated low-field spectrometer, several ideas are proposed to improve the signal.

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