Dynamic Sampling
Dynamic Sampling - Related Videos
08:14
Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience
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Cited by 6 •
2014
This manuscript describes a protocol that applies comprehensive profiling for analysis of transcriptional programs induced in specific brain nuclei of rodents following behavioral paradigms. Herein, this approach is illustrated in the context of profiling genes induced in the nucleus accumbens (NAc) of mice following acute cocaine exposure, utilizing microfluidic qPCR arrays.
12:03
Dynamics of Structures
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
02:20
Dynamic Equilibrium
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2020
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
14:14
Visualization of Cortex Organization and Dynamics in Microorganisms, using Total Internal Reflection Fluorescence Microscopy
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Cited by 12 •
2012
Total Internal Reflection Fluorescence (TIRF) microscopy is a powerful approach to observe structures close to the cell surface at high contrast and temporal resolution. We demonstrate how TIRF can be employed to study protein dynamics at the cortex of cell wall-enclosed bacterial and fungal cells.
07:05
A New Workflow for Sampling and Digitizing Increment Cores
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Cited by 11 •
2024
We present a protocol to use 3D-printed mounts to fix increment cores in the field without the necessity of unpacking and gluing them on wooden mounts. The new GSC holder allows placing the cores in a core microtome to cut their surface and directly transfer them to digital image capturing.
FAQs
Dynamic sampling changes its measurement schedule in response to system state, neural activity, behavioral state, stimulus timing, or emerging signal features. The decision process therefore links acquisition to information content: measurements become denser when a process is changing or especially informative. This can preserve detail about rapid brain events while avoiding equally intensive sampling during less informative periods.
Behavioral state and stimulus timing provide context for interpreting changes in neural signals. A sampling strategy can emphasize periods when an organism is responding, attending, or encountering a relevant stimulus, rather than treating every time point as equally informative. This alignment helps connect brain activity with behavior and can expose temporal relationships that a fixed schedule may miss.
Fixed-rate sampling collects measurements according to a predetermined schedule, whereas Dynamic Sampling can alter collection as neural activity or experimental conditions change. The adaptive approach may concentrate observations around informative transitions and reduce measurements during less informative periods. Its main benefit is a more targeted representation of changing brain processes, with potential reductions in unnecessary data and computational demands.
Several features can guide this decision, including ongoing neural activity, behavioral state, stimulus timing, and newly emerging characteristics of the signal. These factors indicate when the system may be changing or when brain-behavior relationships are especially relevant. Selecting measurements around such features focuses resources on meaningful temporal patterns instead of distributing effort uniformly across the entire experiment.
A basic workflow begins by monitoring the neural signal and relevant experimental context, such as behavior or stimulus timing. Researchers then identify changes or emerging features that warrant closer measurement and adjust subsequent sampling accordingly. The resulting observations can be analyzed for temporal patterns in neural activity, behavior, or their relationship, while reducing collection during less informative periods.
This approach is useful when neural processes change rapidly or when their significance depends on behavioral state and stimulus timing. Applications include neural recordings, sensory processing studies, and brain-behavior experiments. By concentrating measurements around informative events, researchers can examine temporal structure that static sampling may overlook and potentially reduce the amount of data requiring storage or computation.