reverted to save to static

docs/tutorials/plot_06_calcium_imaging.md

@@ -195,7 +195,7 @@ basis = heading_basis + coupling_basis
 ## Gamma GLM
 
 Until now, we have been modeling spike trains, and have used a Poisson distribution for the observation model. With calcium traces, things are quite different: we no longer have counts but continuous signals, so the Poisson assumption is no longer appropriate. A Gaussian model is also not ideal since the calcium traces are non-negative. To satisfy these constraints, we will use a Gamma distribution from NeMoS with a soft-plus non linearity.
-:::{admonirion} Non-linearity
+:::{admonition} Non-linearity
 :class: note
 
 Different option are possible. With a soft-plus we are assuming an "additive" effect of the predictors, while an exponential non-linearity assumes multiplicative effects. Deciding which firing rate model works best is an empirical question. You can fit different configurations to see which one capture best the neural activity.