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