Table 1.

Comparison of different model-based approaches.

Approach	Binomial	STP	Inference qualityh	Experimental easeh	Algorithm complexity
Mean-variance analysisa	✓	×	**	*(PSR)	$\mathcal{O}(M)$
Bayesian quantal analysisb	✓	×	***	**(PSR)	$\mathcal{O}(MN)$
Least-square STP fittingc	×	✓	**	***(PSR)	$\mathcal{O}(M)$
Bayesian Gaussian-STPd	×	✓	****	***(PSR)	$\mathcal{O}(MS)$
Binomial-STPe	✓	✓	***	***(PSR)	$\mathcal{O}(M{N}^4)$
Bayesian binomial-STPf	✓	✓	*****	***(PSR)	$\mathcal{O}(M{N}^4)$
Spike-based GLMg	×	✓	*	****(spikes)	$\mathcal{O}(M)$

Note that the approaches that consider parameter uncertainty can be readily extended to Bayesian. In the $\mathcal{O}$ algorithm complexity analysis M refers to the number of data points, N to the number of release sites and S to the number of samples needed. Point estimate methods that obtain some measures of uncertainty of the parameters rely on getting multiple point estimates, whereas this comes naturally in full probabilistic methods (this is here reflected in the inference quality). The list of methods presented here is grouped into quantal methods (first two rows) and into STP models (last 5 rows) and then sorted by their publication date (earlier first). PSR: Postsynaptic responses. We use star-based ranking system for both inference quality and experimental ease, where one star means worse/harder.

^asee Korn and Faber (1991), Lanore and Silver (2016), and Figure 3A.

^bsee Bhumbra and Beato (2013) and Figure 3A.

^csee for example Markram et al. (1998), Le Bé and Markram (2006), Markram (2006), Wang et al. (2006), Rinaldi et al. (2008), Ramaswamy et al. (2012), Testa-Silva et al. (2012), Romani et al. (2013) and Figure 3B.

^dsee Costa et al. (2013) and Figure 3C.

^esee Loebel et al. (2009), Barri et al. (2016), and Figure 3C.

^fsee Bird et al. (2016) and Figure 3C.

^gsee Ghanbari et al. (2017) and Figure 3D.

^hNote that this ranking is subjective and based purely on our experience with these methods.