Journal: Sensors (Basel, Switzerland)

Article Title: Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

doi: 10.3390/s18093117

Figure Lengend Snippet: The data collection and representation tools. ( A ) Two professional dancers in T-pose while being calibrated within the Phase Space. Cameras capturing the motion are marked and suits contain 38 LEDs in each dancer’s body. Data is sampled at 960 Hz. ( B ) ( Left ) panel: Skeleton showing the distribution of LEDs from 1–38 across the body segments. ( Right ) panel: Our avatar designed in Matlab using a forward kinematics model in to track the various parameters of interest (see movie from the Phase Space and Bot and Dolly in Link 1 of the .).

Article Snippet: The number of overlapping frames is randomly chosen using the continuous Gamma family of probability distributions, generated with the MATLAB function gamrand , according to the empirically estimated Gamma-mean and Gamma-variance, using the MLE approach, as explained above (fitting the Gamma shape and scale parameters obtainable from the whole data set to attain the mean and variance).

Techniques:

Journal: Journal of computational neuroscience

Article Title: A general method to generate artificial spike train populations matching recorded neurons

doi: 10.1007/s10827-020-00741-w

Figure Lengend Snippet: Constant target rate templates. Surrogate data were constructed as rate templates with a constant target rate and low or high spiking regularity (LV of 0.1 or 1.5). We find that the average spike rate from 100 ASTs matches the target rate with only very small errors (see Table 2). The individual ASTs (blue traces) show large rate fluctuations due to the stochastic nature of the gamma spike train. These random rate fluctuations are much more pronounced for a highly irregular spike train target (Fig. 7b,​,d;d; LV=1.5) vs. a regular spike train target (Fig. 7a,​,c).c). Individual AST rates were constructed as aGLRs with a scale factor (sf, eq. 4) of 1.0 instead of 0.25 as used for physiological spike trains in order to more clearly show the contribution of single spikes to rate changes (Fig 4a,​,b;b; blue trace).

Article Snippet: The AST generation process starts at t=0 by selecting a random gamma interval using the Matlab gamrnd() function for a mean rate of 1, and a regularity of κ.

Techniques: Construct