Nonlinear acoustic forces acting on inhomogeneous fluids at slow time-scales

2016 ◽  
Vol 139 (4) ◽  
pp. 2152-2152 ◽  
Author(s):  
Jonas T. Karlsen ◽  
Per Augustsson ◽  
Henrik Bruus
Keyword(s):  
Time Scales ◽  
Slow Time ◽  
Inhomogeneous Fluids ◽  
Nonlinear Acoustic

scholarly journals Local postsynaptic signalling on slow time scales in reciprocal olfactory bulb granule cell spines matches asynchronous release

2020 ◽  
Author(s):  
Tiffany Ona Jodar ◽  
Vanessa Lage-Rupprecht ◽  
Nixon M. Abraham ◽  
Christine R. Rose ◽  
Veronica Egger
Keyword(s):  
Olfactory Bulb ◽  
Time Scales ◽  
Time Course ◽  
Granule Cells ◽  
Gaba Release ◽  
Fluorescence Signal ◽  
Slow Time ◽  
Triggered Release ◽  
Time To Peak ◽  
Adult Mice

AbstractIn the vertebrate olfactory bulb (OB), axonless granule cells (GC) mediate self- and lateral inhibitory interactions between mitral/tufted cells via reciprocal dendrodendritic synapses. Locally triggered release of GABA from the large reciprocal GC spines occurs on both fast and slow time scales, possibly enabling parallel processing during olfactory perception. Here we investigate local mechanisms for asynchronous spine output.To reveal the temporal and spatial characteristics of postsynaptic ion transients, we imaged spine and adjacent dendrite Ca2+- and Na+-signals with minimal exogenous buffering by the respective fluorescent indicator dyes upon two-photon uncaging of DNI-glutamate in OB slices from juvenile rats. Both postsynaptic fluorescence signals decayed slowly, with average half durations in the spine head of t1/2_Δ[Ca2+]i ~500 ms and t1/2_Δ[Na+]i ~1000 ms. We also analysed the kinetics of already existing data of postsynaptic spine Ca2+-signals in response to glomerular stimulation in OB slices from adult mice, either WT or animals with partial GC glutamate receptor deletions (NMDAR: GluN1 subunit; AMPAR: GluA2 subunit). In a large subset of spines the fluorescence signal had a protracted rise time (average time to peak ~400 ms, range 20 ms - >1000 ms). This slow rise was independent of Ca2+ entry via NMDARs, since similarly slow signals occurred in ΔGluN1 GCs. Additional Ca2+ entry in ΔGluA2 GCs (with AMPARs rendered Ca2+-permeable), however, resulted in larger ΔF/Fs that rose yet more slowly.Thus GC spines appear to dispose of several local mechanisms to promote asynchronous GABA release, which are reflected in the time course of mitral/tufted cell recurrent inhibition.

scholarly journals Local Postsynaptic Signaling on Slow Time Scales in Reciprocal Olfactory Bulb Granule Cell Spines Matches Asynchronous Release

2020 ◽  
Vol 12 ◽  
Author(s):  
Tiffany Ona Jodar ◽  
Vanessa Lage-Rupprecht ◽  
Nixon M. Abraham ◽  
Christine R. Rose ◽  
Veronica Egger
Keyword(s):  
Olfactory Bulb ◽  
Time Scales ◽  
Granule Cell ◽  
Slow Time ◽  
Asynchronous Release

scholarly journals Slow time scales in a dense vibrofluidized granular material

2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Andrea Plati ◽  
Andrea Puglisi
Keyword(s):  
Granular Material ◽  
Time Scales ◽  
Slow Time

scholarly journals A Theoretical Study on the Spontaneous Radiation of Inertia–Gravity Waves Using the Renormalization Group Method. Part I: Derivation of the Renormalization Group Equations

2015 ◽  
Vol 72 (3) ◽  
pp. 957-983 ◽  
Author(s):  
Yuki Yasuda ◽  
Kaoru Sato ◽  
Norihiko Sugimoto
Keyword(s):  
Renormalization Group ◽  
Time Scales ◽  
Gravity Waves ◽  
Radiation Reaction ◽  
Vortical Flow ◽  
Group Method ◽  
Linear Potential ◽  
Slow Time ◽  
Balanced Flow ◽  
Inertia Gravity Waves

Abstract By using the renormalization group (RG) method, the interaction between balanced flows and Doppler-shifted inertia–gravity waves (GWs) is formulated for the hydrostatic Boussinesq equations on the f plane. The derived time-evolution equations [RG equations (RGEs)] describe the spontaneous GW radiation from the components slaved to the vortical flow through the quasi resonance, together with the GW radiation reaction on the large-scale flow. The quasi resonance occurs when the space–time scales of GWs are partially comparable to those of slaved components. This theory treats a coexistence system with slow time scales composed of GWs significantly Doppler-shifted by the vortical flow and the balanced flow that interact with each other. The theory includes five dependent variables having slow time scales: one slow variable (linear potential vorticity), two Doppler-shifted fast ones (GW components), and two diagnostic fast ones. Each fast component consists of horizontal divergence and ageostrophic vorticity. The spontaneously radiated GWs are regarded as superpositions of the GW components obtained as low-frequency eigenmodes of the fast variables in a given vortical flow. Slowly varying nonlinear terms of the fast variables are included as the diagnostic components, which are the sum of the slaved components and the GW radiation reactions. A comparison of the balanced adjustment equation (BAE) by Plougonven and Zhang with the linearized RGE shows that the RGE is formally reduced to the BAE by ignoring the GW radiation reaction, although the interpretation on the GW radiation mechanism is significantly different; GWs are radiated through the quasi resonance with a balanced flow because of the time-scale matching.

scholarly journals Ventral striatum lesions do not affect reinforcement learning with deterministic outcomes on slow time scales.

2017 ◽  
Vol 131 (5) ◽  
pp. 385-391 ◽  
Author(s):  
Raquel Vicario-Feliciano ◽  
Elisabeth A. Murray ◽  
Bruno B. Averbeck
Keyword(s):  
Reinforcement Learning ◽  
Time Scales ◽  
Ventral Striatum ◽  
Slow Time

Dynamic Stochastic Synapses as Computational Units

1999 ◽  
Vol 11 (4) ◽  
pp. 903-917 ◽  
Author(s):  
Wolfgang Maass ◽  
Anthony M. Zador
Keyword(s):  
Time Scales ◽  
Network Models ◽  
Release Site ◽  
Slow Time ◽  
Neural Network Models ◽  
Synaptic Physiology ◽  
Wide Range ◽  
Dynamic Synapses ◽  
Dynamic Stochastic ◽  
Theoretical Results

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.

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