Truncated proposals for scalable and hassle-free simulation-based inference
Anonymous Author(s)
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Abstract
Simulation-based inference (SBI) solves statistical inverse problems by repeatedlyrunning a stochastic simulator and inferring posterior distributions from modelsimulations.
To improve simulation efficiency, several inference methods take asequential approach and iteratively adapt the proposal distributions from whichmodel simulations are generated.
However, many of these sequential methods aredifficult to use in practice, both because the resulting optimisation problems can bechallenging and efficient diagnostic tools are lacking.
To overcome these issues,we present Truncated Sequential Neural Posterior Estimation (TSNPE).
TSNPEperforms sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches.
In addition, TSNPE allows to efficientlyperform coverage tests that can scale to complex models with many parameters.
We demonstrate that TSNPE performs on par with previous methods on establishedbenchmark tasks.
We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions,whereas previous methods fail.
Overall, our results demonstrate that TSNPE isan efficient, robust, and accurate inference method that can scale to problems thatwere previously inaccessible to neural posterior estimation.
Introduction
Computational models are an important tool to understand physical processes underlying empiricallyobserved phenomena.
These models, often implemented as numerical simulators , incorporatemechanistic knowledge about the physical process underling data generation, and thereby provide aninterpretable model of empirical observations.
In many cases, several parameters of the simulator haveto be inferred from data, e g., with Bayesian inference.
However, performing Bayesian inference inthese models can be difficult:
Running the simulator may be computationally expensive, evaluating thelikelihood-function might be computationally infeasible and the model might not be differentiable.
Inorder to overcome these limitations, Approximate Bayesian Computation (ABC) methods [Beaumontet al., 2002, 2009], synthetic likelihood approaches [Wood, 2010] and neural network-based methods[e.g.
, Papamakarios and Murray, 2016, Hermans et al., 2020, Thomas et al., 2022] have beendeveloped.
A subset of neural network-based methods, known as neural posterior estimation (NPE) [Papamakarios and Murray, 2016, Lueckmann et al., 2017, Greenberg et al., 2019], train a neural densityestimator on simulated data such that the density estimator directly approximates the posterior.
Unlike other methods, NPE does not require any further Markov-chain Monte-Carlo (MCMC) orvariational inference (VI) steps.
As it provides an amortized approximation of the posterior, whichcan be used to quickly evaluate and sample the approximate posterior for any observation, thisallows the application in time-critical and high-throughput inference scenarios [Gonçalves et al.
,2020, Dax et al., 2021], and fast application of diagnostic methods which require posterior samples for many different observations [Cook et al., 2006, Talts et al., 2018].
Inaddition, unlike methods targeting the likelihood (e.g., neural likelihoodestimation, NLE [Papamakarios et al., 2019, Lueckmann et al., 2019]), NPEcan learn summary statistics from data and it can use equivariances in thesimulations to improve the quality of inference [Dax et al., 2021, 2022].
If inference is performed for a particular observation x o , sampling efficiency of NPE can be improved with sequential training schemes: Insteadof drawing parameters from the prior distribution, they are drawn adaptivelyfrom a proposal (e.g., a posterior estimate obtained with NPE) in order tooptimize the posterior accuracy for a particular x o .
These procedures arecalled Sequential Neural Posterior Estimation (SNPE)
[Papamakarios and
Murray, 2016, Lueckmann et al., 2017, Greenberg et al., 2019] and havebeen reported to be more simulation-efficient than training the neural network only on parameters sampled from the prior, across a set of benchmarktasks [Lueckmann et al., 2021].
Despite the potential to improve simulation-efficiency, two limitations haveimpeded a more widespread adoption of SNPE by practitioners: First, thesequential scheme of SNPE can be unstable.
SNPE requires a modificationof the loss function compared to NPE, which suffers from issues that canlimit its effectiveness on (or even prevent their application to) complexproblems (see Sec. 2).
Second, several commonly used diagnostic tools for
SBI [Talts et al., 2018, Dalmasso et al., 2020, Miller et al., 2021, Hermanset al., 2021] rely on performing inference across multiple observations.
In
SNPE (in contrast to NPE), this requires generating new simulations andnetwork retraining for each observation, which often prohibits the use ofsuch diagnostic tools [Lueckmann et al., 2021, Hermans et al., 2021].
Here, we introduce Truncated Sequential Neural Posterior Estimation (TSNPE) to overcome theselimitations.
TSNPE follows the SNPE formalism, but uses a proposal which is a truncated versionof the prior: TSNPE draws simulations from the prior, but rejects them before simulation if theylie outside of the support of the approximate posterior.
Thus, the proposal is (within its support)proportional to the prior, which allows us to train the neural network with maximum-likelihood inevery round and, therefore, sidesteps the instabilities (and hence ‘hassle’) of previous SNPE methods.
Our use of truncated proposals is strongly inspired by Blum and François [2010] and Miller et al.[2020, 2021], who proposed truncated proposals respectively for regression-adjustment approachesin ABC and for neural ratio estimation (see Discussion).
Unlike methods based on likelihood(-ratio)-estimation [Miller et al., 2021, Hermans et al., 2021], TSNPE allows direct sampling and densityevaluation of the approximate posterior, and thus permits computing expected coverage of the fullposterior quickly (without MCMC) and at every iteration of the algorithm, thus allowing to diagnosefailures of the method even for high-dimensional parameter spaces (we term this ‘simulation-basedcoverage calibration’ (SBCC), given its close connection with simulation-based calibration, SBC,
Cook et al. [2006], Talts et al.
We show that TSNPE is as efficient as SNPE on several established benchmark problems (Sec. 4.1).
We then demonstrate that for two challenging neuroscience problems, TSNPE—but not previousmethods—can robustly identify the posterior distributions (Sec. 4.2).
Background
In Neural Posterior Estimation (NPE), parameters are sampled from the prior p ( θ ) and simulated(i.e.
, x is sampled from p ( x | θ ) ).
Then, a neural density estimator q ϕ ( θ | x ) (in our case a normalizingflow), with learnable parameters ϕ , is trained to minimize the loss:
[Equation]
which is minimized if and only if, for a sufficiently expressive density estimator, q ϕ ( θ | x )
= p ( θ | x )for all x ∈ supp ( p ( x ))
[Paige and Wood, 2016, Papamakarios and Murray, 2016].
Sequential Neural Posterior Estimation (SNPE) aims to infer the posterior distribution p ( θ | x o ) for aparticular observation x o .
SNPE initially performs NPE and, thereby, obtains an initial estimate of the posterior distribution.
It then samples parameters from a proposal ˜
p
( θ ) , which is often chosen tobe the previously obtained estimate of the posterior ˜ p
( θ ) =
q ϕ ( θ | x o ) , and retrains the neural densityestimator [Papamakarios and Murray, 2016].
This procedure can be repeated for several rounds.
Importantly, if parameters θ are sampled from the proposal ˜ p ( θ ) rather than from the prior p ( θ ) ,the estimator q ϕ ( θ | x ) that minimizes the maximum-likelihood loss function no longer convergesto the true posterior.
If one used the maximum-likelihood loss on data sampled from ˜ p
( θ ) , i.e.,
L = E θ ∼ ˜ p
( θ ) E x ∼ p ( x | θ ) [ − log q ϕ ( θ | x )] , then L would be minimized by q ϕ ( θ | x ) ∝ p ( θ | x )
˜ p ( θ ) p ( θ ) ,which is not the true posterior.
Multiple schemes have been developed to overcome this [Papamakariosand Murray, 2016, Lueckmann et al., 2017].
The most recent of these methods, Automatic Posterior
Transformation (APT, or SNPE-C, in its atomic version)
[Greenberg et al., 2019, Durkan et al., 2020]employs a loss that aims to classify the parameter set that generated a particular data point amongother parameter sets (details in Appendix Sec. 6.5).
While APT has been reported to significantly outperform previous methods, several studies have alsodescribed cases in which the approach exhibits performance issues: Both the original APT paper[Greenberg et al., 2019] and Durkan et al.
[2020] reported that APT can show ‘leakage’ of posteriormass outside of bounded priors.
We demonstrate this issue on a simple 1-dimensional simulator withbounded prior (Fig. 1, Appendix Fig. 7).
The posterior estimated by APT is only required to matchthe true posterior density within the support of the prior (details in Appendix Sec. 6.5).
Thus, afterten rounds of APT, while the approximate posterior matches the true posterior within the bounds ofthe prior, a substantial fraction of posterior mass lies in regions with zero prior probability.
In simplemodels, approximate posterior samples that lie outside of the prior bounds can be efficiently rejected.
However, in models with high numbers of parameters, the rejection rate can become so large thatdrawing posterior samples which lie inside of the prior bounds is prohibitive.
For example, Glöckleret al.
[2022] reported a rejection rate of more than 99.9999% in a model with 31 parameters, thusrequiring approximately one minute to draw a single posterior sample from within the prior bounds.
We overcome these limitations by using ‘truncated’ proposal distributions.
This allows us to trainwith maximum-likelihood at every round, thereby sidestepping issues of previous SNPE methods.
Methodology
3.1 Truncated proposals for SNPE
Given a particular observation x o , we suggest to restrict the proposals ˜
p
( θ ) to be proportional to theprior p ( θ ) at least in the 1 − ϵ highest-probability-region (HPR ϵ , the smallest region that contains1 − ϵ of the mass) of p ( θ | x o ) , i.e.˜ p ( θ )
∝ p ( θ ) · 1 θ ∈M
Inputs
Outputs: Approximate
Initialize: Proposal ˜ p
( θ ) =
p ( θ ) , dataset X = {} for r ∈ [1 , ..., R ] do for i ∈ [1 , ..., N ] do θ i ∼ ˜ p
( θ ) simulate x i ∼ p ( x | θ i ) add ( θ i , x i ) to X ϕ ∗ =
arg min ϕ − 1 N (cid:80) ( θ i , x i ) ∈X log q ϕ
( θ i | x i )
Compute expected coverage( ˜ p ( θ ) , q ϕ ) ; // see Alg.˜ p ( θ ) ∝ p ( θ ) · 1 θ ∈ HPR ϵ ; // see Alg.with HPR ϵ ( p ( θ | x o )) ⊆ M .
Thus, ˜ p ( θ ) is a ‘truncated’ proposal.
The key insight is that, when usingsuch a proposal and ϵ = 0 , one can train q ϕ ( θ | x ) with maximum likelihood:min
[Equation]
, and q ϕ ( θ | x o ) will still converge to p ( θ | x o )
(Proof in Appendix Sec. 6.2).
We estimate M as the HPR ϵ of the approximate posterior M = HPR ϵ ( q ϕ ( θ | x o )) .
Since themaximum likelihood loss employed to train q ϕ ( θ | x ) is support-covering, the HPR ϵ of q ϕ ( θ | x o )tends to cover the HPR ϵ of p ( θ | x o )
[Bishop and Nasrabadi, 2006].
In order to obtain the HPR ϵ of q ϕ ( θ | x o ) , we define a threshold τ on the approximate posterior densityq ϕ ( θ | x o ) .
To do so, we use a normalizing flow as q ϕ ( θ | x ) , which allows for closed-form densityestimation and fast sampling.
We then approximate the HPR ϵ of q ϕ ( θ | x o ) as
[Equation]
We chose τ as the ϵ -quantile of approximate posterior densities of samples from q ϕ ( θ | x o ) , andevaluated TSNPE for ϵ = 10 − 3 , 10 − 4 , and 10 − 5 .
Using values of ϵ > 0 yields a proposal priorwhich has smaller support than the current estimate of the posterior, e.g., using ϵ = 10 − 3 neglects0.1% of mass from the support of the approximate posterior.
Thus, this approach can lead to errorsin posterior estimation, e.g., to ‘under-covered’ posteriors.
However, as we describe later, the errorintroduced by this truncation is negligible, as we will demonstrate empirically on several benchmarktasks.
We note that TSNPE can be trained on data pooled from all rounds (Appendix Sec. 6.2).
Thefull algorithm is summarized in Alg.
1 (Fig. 2).
3.2 Sampling from the truncated proposal
To generate training data for subsequent rounds, we have to draw samples from the truncated proposal˜ p ( θ ) .
Several strategies would allow us to draw samples from the truncated proposal, e.g., nestedsampling or Markov-chain Monte-Carlo algorithms [Skilling, 2004], but we here explored rejectionsampling and sampling importance resampling (SIR)
[Rubin, 1988].
For rejection sampling, wesample the prior θ ∼ p ( θ ) and accept samples only if their probability under the approximateposterior q ϕ ( θ | x ) is above threshold τ .
This strategy samples from the truncated proposal exactly, but can fail if the rejection rate becomes too high.
To deal with these situations, we used SIR.
For each sample from the truncatedproposal, SIR draws K samples from the approximate posterior, computes weights w i =1 ...
K =p ( θ i )
θ i ∈M /q
ϕ ( θ i | x ) , normalizes w i such that they sum to one, draws from a categorical distribution with weights s ∼ Categorical ( w i ) , and selects the posterior sample with index s .
SIR requires afixed sampling budget of K posterior samples per sample from the truncated proposal and returnsexact samples from the truncated proposal for K → ∞ .
In our runs, we use K = 1024 .
For adiscussion on the error induced by finite K , see Appendix Sec. 6.11, for computational costs see
Sec.
3.3 Coverage diagnostic
[Figure] In order for the estimated posterior q ϕ ( θ | x o ) to converge to p ( θ | x o ) , TSNPE requiressupp ( p ( θ | x o )) ⊆
HPR ϵ ( q ϕ ( θ | x o )) , i.e., the estimated posterior must be broader than the trueposterior (proof in Appendix Sec. 6.2).
In order to diagnose whether the posterior is, on average,sufficiently broad, we perform expected coverage tests as proposed in Miller et al.
[2021], Dalmassoet al. [2020], Hermans et al. As described in Dalmasso et al. [2020], Rozet et al.
[2021] and illustrated in Fig. 3, the coverage ofthe approximate posterior can be computed as
[Equation]
where θ ∗ is sampled from the truncated proposal and x ∗ is the corresponding simulator output. In order to approximate this integral, one has to either evaluate the approximate posterior on agrid [Dalmasso et al., 2020, Hermans et al., 2021] or apply a Monte-Carlo average which includesrepeatedly sampling (and evaluating) the (unnormalized) approximate posterior
[Miller et al., 2021, Rozet et al., 2021].
The first option does not scale to high-dimensional spaces whereas the secondis computationally expensive for methods estimating likelihood(-ratios) and, thus, require MCMC.
In contrast, the TSNPE-posterior can be sampled from and evaluated in closed-form, leading to acomputationally efficient and scalable diagnostic which can be run after every training round.
The expected coverage can be computed as an average of the coverage across multiple pairs ( θ ∗ , x ∗ ) [Miller et al., 2021, Hermans et al., 2021] and should match the confidence level for all confidencelevels (1 − α ) ∈ [0 , 1] (Fig. 3c). We term this procedure of computing the empirical coverage‘simulation-based coverage calibration‘ (SBCC), due to its close connection with SBC [Cook et al.
,2006, Talts et al., 2018] (identical under certain conditions, see Appendix Sec. 6.6).
For TSNPE, it isparticularly important that the empirical expected coverage matches the confidence level for highconfidence levels (i.e., for small α ), since overconfidence in these regions would indicate that groundtruth parameters θ ∗ are falsely excluded from the HPR ϵ = α .
The resulting method is summarized in Appendix Alg. 2.
Results
We evaluated TSNPE on several benchmark tasks and on two complex problems from neuroscience.
We found that TSNPE performs as well as APT on the benchmark tasks and that it is robust to choicesof ϵ .
In addition, we found that, in contrast with APT, TSNPE can successfully infer the posteriordistribution for complex models with large numbers of parameters.
4.1 Performance on benchmark tasks
We compared TSNPE with NPE and APT on six benchmark tasks for which samples from theground-truth posterior are available (see Appendix Sec. 6.9 for tasks)
[Lueckmann et al., 2021].
Wequantified the performance with a classifier two-sample test (C2ST), for which 0.5 indicates thatthe approximate posterior is identical to the ground-truth posterior, whereas 1.0 implies that thedistributions can be completely separated by a classifier.
Overall, APT and TSNPE perform similarlywell and both outperform NPE (Fig. 4, left three columns).
On two of the six tasks (Gaussian Linearand SLCP), APT has slightly better performance than TSNPE, whereas on two other tasks (SIRand Lotka-Volterra), TSNPE outperforms APT.
Overall, TSNPE and APT perform similarly well
In order to get insights into the improved performance of TSNPE as compared to NPE, we computedthe fraction of prior samples that lie within the HPR ϵ of the approximate posterior (Fig. 4, fourthcolumn).
In tasks with broad posteriors and few simulations, the HPR ϵ is almost as wide as theprior and thus the performances of NPE and TSNPE are similar (e.g., SLCP with 1k simulations).
In other tasks and with more simulations, the HPR ϵ is much narrower than the prior, leading to animprovement in simulation efficiency (e.g., Lotka-Volterra with 100k simulations).
Finally, we evaluated whether the HPR ϵ of the approximate posterior contains the support of the trueposterior (Fig. 4, fifth column).
We computed the fraction of true-posterior samples within the HPR ϵof the approximate posterior.
For most tasks, fewer than 0.1% of samples were excluded, and the rateof erroneously rejected samples decreased as more simulations were used.
In the Lotka-Volterra taskwith 1k and 10k simulations, many ground-truth samples were rejected and TSNPE performed poorly,but NPE and APT also failed to solve the task.
Thus, while truncated proposals can potentially induceposterior biases, these only have a negligible effect on the performance of TSNPE.
We note that
TSNPE performance is qualitatively unaffected by the choice of ϵ ≤ 10 − 4 and proposal samplingscheme (Appendix Fig.
8, Fig. 9, Fig.10).
Applying truncated proposals to APT leads to equally goodor worse performance than ’standard’ APT, depending on the task (Appendix Fig. 12).
4.2 Efficient and robust inference in two complex neuroscience problems
Next, we evaluate the performance of TSNPE on two challenging neuroscience problems, where thecompetitive advantage of TSNPE is fully realized.
Pyloric network We applied TSNPE to a challenging real-world simulator from neuroscience: Thepyloric network of the stomatogastric ganglion in the crab Cancer Borealis [Prinz et al., 2003, 2004].
The model has 31 parameters and simulates 3 voltage traces that we reduce to 18 summary statistics.
The prior distribution is uniform within previously described parameter ranges [Prinz et al., 2004,
Gonçalves et al., 2020].
We identify the posterior distribution given experimentally observed data[Haddad and Marder, 2021] (Fig. 5a) with APT and TSNPE (13 rounds, 30k simulations per round).
When applying APT ‘out of the box’ (from ‘sbi’ toolbox [Tejero-Cantero et al., 2020]), the rateof approximate-posterior samples within the prior bounds was 0.02% after the second round and0.0000% after the third round (Fig. 5b), which rendered a fourth round too computationally expensive.
We attempted to overcome these issues by appending a transformation T to the density estimatorq ϕ
( θ | x ) such that its support is constrained to match the support of the prior.
In practice, we used asigmoid transformation.
While the resulting approximate posterior exhibited no leakage, this setuprevealed another problem when running APT:
In transformed (i.e., unbounded) space, the densityestimator q ϕ ( θ | x ) can put significant mass in regions outside of the training data.
When forced intoconstrained space, these ‘leaking’ regions lead to spikes at the bounds of the parameter space (Fig. 5c,further details in Appendix Sec.
6.5; illustration, additional tests and full posterior in Appendix
Figs. 15, 14 and 16).
These spikes are at odds with previously published posterior distributions[Gonçalves et al., 2020, Deistler et al., 2021, Glöckler et al., 2022] and samples from these parameterregions do not produce good simulations (Appendix Fig. 17).
This demonstrates that leakage occursin APT even when the density estimator is forced into constrained space and that these issues lead toan incorrect posterior approximation as well as to poor predictive samples.
We applied TSNPE to this task for 13 rounds without any issue.
The resulting posterior producessamples that closely match the observed data (Fig. 5d, more samples in Appendix Fig. 13, posteriordistribution across all 31 parameters in Appendix Fig. 18).
The obtained posterior is similar topreviously published posteriors [Gonçalves et al., 2020, Deistler et al., 2021, Glöckler et al., 2022].
Multicompartment model of a single neuron Finally, we turn to a landmark problem in neuroscience for which the posterior has not yet been identified:
A morphologically detailed model of athick-tufted layer 5 pyramidal cell (L5PC) from the neocortex [Ramaswamy et al., 2015, Markramet al., 2015, Van Geit et al., 2016].
The model describes the response of a neuron to current stimuliof different strengths.
The model has approximately 7000 separate compartments which composethe anatomy of the cell (Fig. 6a).
Each compartment has dynamics based on the Hodgkin-Huxleyequations [Hodgkin and Huxley, 1952] and contains multiple ion channels (details in Van Geit et al.[2016]).
The model has 20 free parameters which are the maximal channel conductances and timeconstants of the ion channels.
The simulation consists of four separate simulations corresponding toexperimental protocols which describe the voltage response to different stimuli.
For the first threeprotocols (Step 1, Step 2, Step 3), each voltage response is characterized by 10 summary statistics.
The fourth protocol models the back-propagation of the voltage response through the dendritic treeand is captured by 5 additional summary statistics (bAP soma, bAP dend.
1, bAP dend. 2).
In total, the model produces 35 summary statistics, to which we add Gaussian noise with diagonal covariancematrix capturing the response variability of previously reported measurements [Hay et al., 2011].
Our goal is to infer the posterior distribution over 20 parameters given 35 summary statistics that weresimulated—and thus have a known ground-truth parameter set— to resemble experimentally observedactivity (Fig. 6b).
The prior is uniform within previously established bounds [Van Geit et al., 2016].
A major difficulty in fitting this model is that a large fraction of prior samples generate summarystatistics that are very different from the observed data: About 99.98% of prior predictives contain atleast one summary statistic that is undefined (Fig. 6c).
When a summary statistic is undefined, weassign it a value that is substantially outside the range of the observed data (Appendix Sec. 6.13).
We ran TSNPE over 6 rounds (hyperparameters in Appendix Sec. 6.14).
In each round, we ran 30ksimulations, leading to a total of 180k simulations.
After every round, we evaluated the expectedcoverage with SBCC.
After the first round, the approximate posterior exhibited poor expectedcoverage (Fig.
6d top).
Therefore, as suggested by Hermans et al.
[2021], we used an ensemble of 10neural density estimators to ensure that the approximate posterior is sufficiently broad (Fig.
6d bottom).
Although the approximate posterior remains underconfident, the empirical expected coverage closelymatches the confidence level for high-confidence levels, which is crucial for TSNPE (Sec. 3.3).
The TSNPE-posterior has several parameters with broad marginals, demonstrating that this modelexhibits ‘degeneracy’, a widespread phenomenon in biological systems [Marder and Taylor, 2011](Fig.
6e, Appendix Sec. 6.13 for parameter names).
Other marginals are narrow, demonstrating thatthe model is sensitive to changes in these parameters.
Posterior predictive samples closely matchthe observed data (Fig.
6f,g; more samples in Appendix Fig. 20).
We emphasize that fitting suchmorphologically-detailed neuron models is a challenging and widespread problem in neuroscience,one for which commonly used methods [e.g., genetic algorithms, Druckmann et al., 2007, Van Geitet al., 2016] are often simulation-inefficient or do not estimate the full posterior distribution.
We showpromising results, suggesting that TSNPE could be applied to other complex single-neuron models.
In contrast, with ‘out of the box’ APT, none of the 10M approximate-posterior samples was withinthe prior bounds after the second round.
This rendered a third round too computationally expensive.
Overall, the results on the pyloric network model and on the multicompartment model demonstratethat TSNPE is an efficient and robust method that scales to complex and high-dimensional modelsthat were inaccessible to previous neural posterior estimation methods.
Discussion
We presented a new method to perform Bayesian inference in implicit models, which we call Truncated Sequential Neural Posterior Estimation (TSNPE).
Like previous methods, TSNPE adaptivelyselects parameters to improve simulation-efficiency and allow posterior inference in complex modelswith many parameters.
The key ingredient is that TSNPE samples parameters from a ‘truncated’region of the prior, and thus overcomes instabilities of previous methods while maintaining simulationefficiency.
In order to diagnose potential errors of TSNPE, we developed a coverage test that can berun quickly and at every round of TSNPE.
TSNPE presents a new variant of SNPE which is at leastas powerful as previous variants on benchmark tasks, but provides a powerful alternative which isable to solve inference problems on which previous methods have failed.
Related work TSNPE differs from automatic posterior transformation (APT, SNPE-C) in itsproposal and its loss function: TSNPE uses a truncated prior as proposal, while APT can flexibly useany proposal, which, e.g., allows for more sophisticated active learning rules [Lueckmann et al., 2019,
Järvenpää et al., 2019].
However, APT’s flexibility requires a modification of NPE loss function,which can be an impediment to its usage in practice: First, the modification can lead to ‘leakage’,which can make it prohibitive to draw samples within prior bounds.
Second, APT loss requires anexplicit prior and thus, cannot be applied to models in which the prior can only be sampled [Rameshet al., 2022].
Third, current formulations of APT cannot discard parameters leading to invalidsimulations as the posterior mass would ‘leak’ into parameter regions which only produce invalidsimulations (Appendix Sec. 6.5).
It might be possible that these issues are resolved using a modifiedformulation of APT, e.g., by combining its atomic loss with additional loss terms, or preventingleakage by penalizing ‘bad’ parameters [Greenberg et al., 2019].
In cases in which leakage preventsapplication of APT (in particular, in high-dimensional problems), TSNPE provides an alternative.
Our method is inspired by previous work that introduced a mechanism to post-hoc correct samplesobtained by an Approximate Bayesian Computation (ABC) algorithm
[Blum and François, 2010],i.e.
, ‘regression adjustment ABC’.
Their method draws samples from a truncated region of the priorto avoid correction terms, but estimates the posterior density with ABC samples—rather than using aflexible neural density estimator—and estimates the support by training a dedicated support-vectormachine.
In addition, the method runs a single round of truncation and retraining, whereas wedemonstrate that TSNPE can be robustly applied across 10 rounds.
Truncated proposals have also been proposed for neural ratio estimation [Truncated Marginal Neural
Ratio Estimation (TMNRE) Miller et al., 2021]: TMNRE uses truncated proposals to efficientlyinfer selected posterior marginals while being amortised around the observation, allowing to test thecoverage properties of the selected marginals, e.g., with SBC [Cook et al., 2006, Talts et al., 2018].
In contrast, TSNPE aims at efficiently inferring the full posterior distribution by proposals that avoidthe correction of SNPE loss function.
Truncating the proposal based on the full posterior rather thanon the marginals can lead to drastically narrower proposals: E.g., on the pyloric network problem,truncation based on posterior marginals rejects 20% of prior samples versus 99.94% rejection basedon the posterior joint.
In addition, while TMNRE uses the expected coverage to test the consistencyof the posterior marginals, TSNPE can test the expected coverage of the full posterior distribution.
Simulation-based coverage calibration The main failure mode of TSNPE occurs if the HPR ϵof the approximate posterior is too narrow relative to the support of the true posterior (e.g., if theestimate misses posterior modes).
In this case, TSNPE neglects relevant regions in parameter space.
To diagnose such failure, we applied SBCC, a coverage test for TSNPE.
SBCC evaluates the expectedcoverage of the approximate posterior without evaluating it on a grid [Dalmasso et al., 2020, Hermanset al., 2021] and, unlike diagnostic tools for methods based on learning the likelihood(-ratio), doesnot require MCMC runs for multiple observations x .
This allows SBCC to be run quickly and formodels with many parameters.
In addition, in contrast to diagnostic tools for likelihood-free inferencewith ABC, SBCC does not require an additional step to estimate the density of approximate posteriorsamples [Prangle et al., 2014].
We note that, since SBCC is a variation of SBC [Cook et al., 2006], itonly ensures that the HPR ϵ is correct on average across observations, not for a particular observation.
Conclusion TSNPE combines the simulation-efficiency of sequential neural posterior estimationwith the robustness and coverage-tests of non-sequential methods, allowing to scale neural posteriorestimation to complex and high-dimensional models that were inaccessible to previous methods.
Appendix6.1 Reproducibility statement
We used the configuration manager hydra to track the configuration and seeds of each run [Yadan,2019].
All code to reproduce the results can be found at https://github.com/tsnpeNeurips .
We implemented TSNPE on top of the publicly accessible sbi toolbox [Tejero-Cantero et al., 2020].
All simulations and runs were performed on a high-performance computer.
For each run, we usedbetween 8 and 48 CPU cores.
6.2 Proof of convergence
Below, we prove that, for a given observation x o , the posterior distribution obtained with TSNPEq ϕ ( θ | x o ) converges to the true posterior distribution p ( θ | x o ) under the assumption that the HPR ϵof the approximate posterior at every TSNPE round covers the support (i.e., the HPR ϵ for ϵ = 0 )
ofthe true posterior given the observation x o .
We call the support of the true posterior M , i.e. M =supp ( p ( θ | x o )) . The proof proceeds in two steps: First, we derive the effective proposal distributionwhen pooling data from all rounds. Second, we show that, for such proposal distributions, the neuraldensity estimator converges to the true posterior. Deriving the proposal distribution We denote by ˜
p r ( θ ) the proposal distribution from which θare drawn in round r .
In the first round, we use the prior, i.e., ˜ p r =1 ( θ )
= p ( θ ) .
In later rounds, wesample from the prior but reject samples that lie outside of the HPR ϵ of the approximate posteriorgiven x o .
The samples drawn in round r are thus drawn from
[Equation]
where Z r is the normalization constant and U r ( θ ) is 1 on the HPR ϵ of the approximate posterior atround r and zero otherwise. Given our assumption that the HPR ϵ of the approximate posterior coversthe support of the true posterior M , U r ( θ ) is 1 on the support of the true posterior M . When pooling simulations from all rounds, after R rounds, the parameters are sampled from a mixtureof all proposal distributions:
[Equation] In this equation, we assumed that all rounds contain equally many simulations, but the proof caneasily be extended to rounds with different numbers of simulations by adding weights to the abovesum. As can be seen above, the distribution ˜ p ( θ ) is the prior times a function f ( θ ) which is made up ofseveral steps and whose steps are defined by the HPR ϵ of the approximate posterior of every round(illustration in Appendix Fig. 21).
Finally, under the assumption that all U r ( θ ) are 1 on the supportof the true posterior M , we have, for any θ ∈ M [Equation] i.e., f ( θ ) is not a function of θ . Thus, for any θ ∈ M , we have
[Equation] We emphasise that this proportionality holds only for θ ∈ M , but not necessarily for θ outside of thesupport of the true posterior. Training the neural density estimator Next, we show that, for a proposal distribution of the formderived in the paragraph above, the approximate posterior q ϕ ( θ | x o ) for an observation x o convergesto the true posterior distribution p ( θ | x o ) . TSNPE minimizes the following loss function:
[Equation]
Plugging in the proposal distribution defined above:
[Equation] The term within the integral over θ is proportional to the Kullback-Leibler-divergence betweenf ( θ ) p ( θ | x ) /Z
(with Z = (cid:82) f ( θ ) p ( θ | x ) d θ ) and the approximate posterior q ϕ ( θ | x ) .
Thus, L isminimized if and only if [Equation]
for all x within the the support of p ( x ) [Papamakarios and Murray, 2016].
This means that, for arbitrary x ∈ supp ( p ( x )) , q ϕ ( θ | x ) will not converge to the true posterior p ( θ | x ) ,but to f ( θ ) p ( θ | x ) /Z . However, for the observed data x = x o , we have:
[Equation]
The first case follows from the fact that f ( θ ) is constant on the support of the true posterior. Thesecond case follows because the true posterior has zero probability density for θ outside of its ownsupport M .
Thus:
[Equation] θ | x o ) . Since q ϕ ( θ | x o ) is a (conditional) normalizing flow, it is normalized and, thus:
[Equation]
6.3 Simulation-based coverage calibration (SBCC)
The algorithm for computing the coverage (SBCC) is shown in Alg.
Inputs: proposal ˜ p
( θ ) , approximate posterior q ϕ trained on θ ∼ ˜
p
( θ ) , number of simulations M , number of posterior samples per simulation P .
Database of empirical coverages E , initialized as the empty set.
Outputs: Coverage for different confidence levels 1 − α .
for i ∈ [1 , ..., M ] do θ ∗ i ∼ ˜ p
( θ ) simulate x ∗ i ∼ p ( x | θ ∗ i ) l ∗ i = log( q ϕ ( θ ∗ i | x ∗ i )) ; // compute log-prob of ground-truth c =for j ∈ [1 , ..., P ] do θ j ∼ q ϕ ( θ | x ∗ i ) l j = log( q ϕ ( θ j | x ∗ i )) ; // compute log-probs of posterior samples if l j > l ∗ i then c
= c +e = c/P ; // fraction of posterior samples whose log-prob is larger than ground-truth log-prob add e to E Plot CDF of E
Inputs: Prior p ( θ ) , Approximate posterior q ϕ , observation x o , number of posterior samples M , ϵ that defines the HPR, number of desired samples from ˜ p
( θ ) N .
Database of log-probabilities
P , initialized as an empty set.
Outputs: Samples S from truncated proposal ˜ p
( θ ) .
for i ∈ [1 , ..., M ] do θ
i ∼ q ϕ ( θ | x o ) l i = log( q ϕ ( θ i | x o ) add l i to P κ = quantile ϵ ( P ) ; // threshold for HPR ϵ // sample truncated proposal with rejection sampling while s < N do θ ∼ p ( θ ) l = log( q ϕ ( θ | x o ))
if l > κ then add θ to S s +=
6.4 Algorithm description for sampling from the HPR of the approximate posterior
The algorithm for sampling from the HPR ϵ of the approximate posterior is shown in Alg.
6.5 Alleviating issues of APT
We compared TSNPE to Automatic Posterior Transformation (APT) [Greenberg et al., 2019].
Inthis section, we briefly review how APT works, and why ‘leakage’ occurs, and how we attempted toimprove it.
APT review Broadly, APT exists in two versions.
Its first version can be applied only if the densityestimator q ϕ ( θ | x ) is a mixture of Gaussians, the proposal ˜
p
( θ ) is a mixture of Gaussians, and theprior p ( θ ) is either uniform or Gaussian.
We did not compare TSNPE to APT in this form because wewanted to use more expressive density estimators for q ϕ ( θ | x ) .
The second version of APT, knownas atomic APT, allows to use any density estimator q ϕ ( θ | x ) , any proposal ˜ p
( θ ) and any explicitprior p ( θ ) .
One must be able to evaluate the density estimator and the prior, but the proposal can beimplicit (i.e., without closed form density).
Atomic APT minimizes the loss:
[Equation]
with number of atoms A .
In this loss, θ j and θ i can be sampled from any proposal distribution.
x i issampled from p ( x i | θ i ) .
Leakage issue Notice that the above loss is the same for q ϕ ( θ | x ) and for c · q ϕ ( θ | x ) .
In other words,the approximate posterior has to be correct only up to a proportionality constant [Greenberg et al.
,2019, Durkan et al., 2020].
Thus, an approximate posterior q ϕ ( θ | x ) which is c · p ( θ | x ) will havea minimal loss.
Since the dataset on which q ϕ ( θ | x ) is trained contains no θ that lies outside of theprior bounds, the approximate posterior can be anything outside of the prior bounds without affectingthe value of the loss function.
This is what is called ‘leakage’, and which has been pointed out asa potential problem both in the original APT paper Greenberg et al.
[2019] and work studying therelationship of APT with contrastive learning approaches Durkan et al.
[2020]: While the approximateposterior might be proportional to the true posterior within the bounds of the prior p ( θ ) , it can putsignificant mass outside of the prior bounds (because the loss does not penalize this behaviour).
Transforming the parameter space We tried to fix the leakage issue by appending a transformationsuch that the density estimator has constrained support.
This requires that the bounds of the parameter space are known and that such a transformation can be implemented.
If this fix can be applied, theleakage will be zero by definition.
In all our experiments, such a transformation substantially helpedwith the leakage problem.
Leakage into regions of no training data However, even when possible, such transformation doesnot completely prevent ‘leakage’:
When inspecting the loss of APT, we see that the approximateposterior can put mass into any region of the parameter space in which no parameter sets θ in thetraining data lie.
For example, if the prior distribution is standard Gaussian and one trains APT on100 parameter sets sampled from the prior, the training dataset will unlikely contain values that aresmaller than -3 or larger than 3.
Therefore, APT can ‘leak’ into the regions below -3 or above 3 andstill have an optimal loss.
While, in the limit of infinite data, APT would correct its ‘leakage’ as soonas parameters from these regions are used as training data, given a finite number of simulations, theapproximate posterior can ‘leak’ into new regions which have not been explored (yet).
The (potential)problem of leakage does not affect all applications equally: High-dimensional parameter spacessuffer more from this behavior than low-dimensional ones, as there are many regions into which themass of the approximate posterior can ‘leak’.
This behaviour is illustrated in Appendix Fig.
Leakage because of invalid data Another way in which leakage can occur is if the simulatorproduces invalid data (e.g. NaN or infinity).
Often, such invalid simulations are discarded from thetraining dataset [Lueckmann et al., 2017] and the approximate posterior is trained only on samplesthat produce valid outputs.
For example, assume that a small region of the prior always producesinvalid simulations.
In this case, the approximate posterior will never be trained on simulations fromthis parameter region and APT can ‘leak’ into this region.
Thus, the approximate posterior mightcontain significant mass in parameter regions that produce invalid simulations.
Explicit recommendations for running APT We will now give explicit recommendations forrunning APT.
These modification greatly improved the performance of APT in our experiments, butwere not able to avoid the failure of the algorithm on challenging real-world problems such as thepyloric network (Fig. 5).
For priors with bounded supports: If possible, transform the parameter space into unboundedspace.2.
Do not discard invalid simulations.
Instead, replace invalid entries (such as NaN) with asubstantially different value than the observed data and train on all available simulations.3.
Check if the posterior contains a lot of mass in regions with very low prior probability.
Ifthis is the case, it can hint at a failure of APT through leakage into regions of no trainingdata.4.
If you transformed the parameter space, check if many posterior samples lie very close tothe bounds.
Again, this can hint at a failure of APT through leakage into regions of notraining data.
6.6 Relation between simulation-based calibration and our diagnostic
As discussed above, our method is closely related to simulation-based calibration (SBC) [Cooket al., 2006, Talts et al., 2018].
Briefly, SBC samples θ ∗ from the prior, samples the likelihoodx ∼ p ( x | θ ∗ ) and then draws samples from the posterior θ i ∼ p ( θ | x ) (e.g., with MCMC).
It thenprojects the (potentially high-dimensional) parameters θ i into a one-dimensional space T ( θ i ) .
Often,this projection is the 1D-marginal distribution of parameters [Carpenter et al., 2017].
It then ranks T ( θ ∗ ) under all posterior samples T ( θ 1 ...N ) .
Repeated across several prior samples, the distributionof ranks should be uniform.
For high-dimensional parameter spaces, the marginal distribution of eachparameter is checked independently.
Notably, other projections into a one-dimensional space are possible.
Below, we explain that ourmethod is identical to running SBC with projection T : θ → q ϕ ( θ | x ) and with posterior samplesexactly following q ϕ ( θ | x ) .
In SBC and in our diagnostic method, samples are drawn from the prior θ ∗ ∼ p ( θ ) and simulatedx ∗ ∼ p ( x | θ ) .
In our diagnostic method, as well as in SBC with projection being the log-probability of the approximate posterior, one then samples the posterior to obtain θ i and evaluates the logprobability of all samples,
i.e., l i = T ( θ i ) = log( q ( θ i | x ∗ ) as well as of the initial parameter setl ∗ = log( q ( θ ∗ | x ∗ ) .
SBC then ranks l ∗ under all l i , which is equivalent to computing its quantile(as in our method).
In our diagnostic tool, one then evaluates whether this quantile is above orbelow several confidence levels (evaluation on a 1D evenly spaced grid, same as rank binning in
SBC).
This generates a step-function with the step occuring at the quantile of l ∗ .
This step functionis the cumulative distribution function of a dirac at the quantile of l ∗ .
Therefore, repeated acrossseveral θ ∗ , our coverage plots (e.g., Fig 3c) correspond to the cumulative distribution function ofthe histograms generated by SBC (with projection T : θ → q ϕ ( θ | x ) and posterior samples exactlyfollowing q ϕ θ | x ) ). 6.7 SBCC in a multi-round setting
We run our diagnostic tool after every round of training.
If one trains only on simulations that wererun in the most recent round, SBCC can be run as in the first round.
However, if one wishes to trainon simulations from all rounds, then the deep neural density estimator converges to:
[Equation] Proof in Sec. As is described in Sec. 6.2, this means that q ϕ ( θ | x ) will not converge to the trueposterior for arbitrary x , but only for the observation x o . This poses a problem for SBCC:
As described in Alg. 2, SBCC measures whether the coverage iscorrect (on average) for many x generated by the proposal distribution.
Since the loss employed by
TSNPE only ensures convergence for x o , it will by construction not provide correct results for otherx .
This issue can be solved in two ways:
When
running SBCC, instead of drawing
samples
from the proposal
prior
of the most
recent round
˜
p r (
θ ) , one can draw samples θ ∗ from ˜
p
( θ ) , i.e., the distribution that emerges
from pooling data from all rounds (notation as in Sec.
6.2 and Alg.
This is the
method
described
in Appendix
Alg.
One can truncate the approximate posteriors q ϕ
( θ | x ∗ ) while running SBCC (see Alg.
2 fornotation).
With this strategy, when running SBCC in round r , we draw parameters from˜ p r ( θ ) (the truncated proposal from round r ), simulate them x ∼ p ( x | θ ) , sample from theposterior q ϕ ( θ | x ) ,
reject
samples that lie outside of HPR ϵ ( q ϕ ( θ | x o )) , and then continueas described in Alg. 2.
Strategy 2 only ensures that the posterior regions which lie within
HPR ϵ ( q ϕ ( θ | x o )) are well-calibrated.
It does not
ensure
that the full posterior ( q ϕ ( θ | x ∗ )with x
∗
∼ p (
x ∗
|
θ ∗
and θ ∗ ∼ ˜
p r (
θ
) ) is well-calibrated.
6.8 Toy model
The toy model shown in Fig.
1 is given by a uniform prior within [ − 2 , − 1] and [1 , 2] .
The simulatoris x ∼ θ 2 + ϵ , where ϵ is a Gaussian distribution with mean zero and standard deviation 0 .
We ran
APT [Greenberg et al., 2019] and TSNPE for 10 rounds with 500 simulations per round.
For APT, allhyperparameters are the default values from the sbi package [Tejero-Cantero et al., 2020], but weused a neural spline flow (NSF) for both APT and TSNPE
[Durkan et al., 2019].
6.9 Benchmark tasks
Below, we briefly describe the benchmark tasks.
For details, please see Lueckmann et al.
Gaussian linear: 10 parameters which are the mean of a Gaussian model.
The prior is Gaussian,resulting in a Gaussian posterior.
Bernoulli GLM: Generalized linear model with Bernoulli observations.
Inference is performedon 10-dimensional sufficient summary statistics of the originally 100 dimensional raw data.
Theresulting posterior is 10-dimensional, unimodal, and concave.
Lotka Volterra: A traditional model in ecology [Wangersky, 1978], which describes a predator-preyinteraction between species, illustrating a task with complex likelihood and unimodal posterior.
SLCP:
A task introduced by Papamakarios et al.
[2019] with a simple likelihood and complexposterior.
The prior is uniform, the likelihood has Gaussian noise but is non-linearly related to theparameters, resulting in a posterior with four symmetrical modes.
Two moons: This model has two parameters with a uniform prior.
The simulator is non-linear,generating a posterior with both local and global (bimodal) structure [Greenberg et al., 2019].
SIR: Epidemiological model with two parameters and ten summary statistics [Kermack and McKendrick, 1927].
6.10 Computational cost of rejection sampling and SIR
In this section, we quantify the computational costs of rejection sampling and sampling-importanceresampling (SIR).
The computational cost of both sampling methods comprises the computationalcost of sampling and evaluating the approximate posterior.
On an AMD Ryzen Threadripper 1920X12-Core Processor, drawing (or evaluating) 100k samples takes approximately 10 seconds.
On a
GeForce RTX 2080 GPU, drawing (or evaluating) 100k samples takes approximately 0.17 seconds. Thus, in SIR (with an oversampling factor K = 1024 ), one can draw (or evaluate) 100 samples fromthe truncated proposal in 0.17 seconds on a GPU (versus 10 seconds on a CPU). For most real-worldsimulators, this constitutes a small fraction of the compute time required to simulate the model: e.g.,for the multicompartment model, a single simulation takes approximately 30 seconds and, thus, SIRsampling from the truncated support takes up only 0.012% of the total compute time with a GPU.
For rejection sampling, the time required to draw samples from the truncated support depends onthe rejection rate.
However, as long as the acceptance rate is above 0.001%, the cost of rejectionsampling is still small compared to the cost of running the simulator.
6.11 Accuracy of SIR
Here, we investigate the (potential) error induced by using sampling-importance resampling (SIR).
SIR is an approximate sampling technique and does not produce exact samples for finite K .
Thisraises the question of how strongly the errors induced by SIR influence the results of TSNPE.
Inorder to investigate this, we performed three analyses: 1) We ran all benchmarking tasks with SIRand K = 1024 and compared the results to rejection sampling (Appendix Fig. 10).
Across the sir,bernoulli-glm, and two-moons benchmark tasks, the performance of TSNPE with SIR matches theperformance of TSNPE with rejection sampling.
The other three benchmark tasks are still running,we will update the manuscript accordingly.
In a simple 1D toy model, we investigated how closelythe samples produced by SIR match the samples produced by rejection sampling.
As can be seenin Appendix Fig.
11, the distribution of SIR samples is quite different from rejection sampling for K = 16 and K = 64 . However, for K = 1024 , the distribution of samples from SIR very closelymatches the distribution of rejection samples. 3) Finally, we investigated the performance of SIR byinspecting the effective sample size (ESS), which we computed as
[Equation] with w i being the normalized importance weights [Kong, 1992].
For K = 1024 , across the twomoons and the bernoulli glm tasks, the ESS was never below 8.311, i.e. it was always significantlyhigher than 1 (the number of resampled samples).
All of these results indicate that SIR is expected tobe a useful and robust sampling method for TSNPE. 6.12 Pyloric network model
For the pyloric network model, we used the same prior, simulator, and summary statistics as previouswork [Gonçalves et al., 2020, Deistler et al., 2021, Glöckler et al., 2022]. The model has a total of 31parameters and 18 summary statistics.
We replaced invalid summary statistics with a value that is 2standard deviations (of prior predictives) below the observation. The experimental data [Haddad and
Marder, 2021] is also the same as used in these previous works. [Table]
We performed Bayesian inference in a complex model of single-neuron dynamics.
The model isthe same as used in Van Geit et al.
We added observation noise with standard deviationstaken from previously published measurements [Hay et al., 2011].
The prior is a uniform distributionwithin the same bounds as previously used [Van Geit et al., 2016].
The parameters are shown in Table1.
The summary statistics are also the same as in Van Geit et al.
We replaced NaN values by the minimal value among prior samples minus two standard deviationsof prior samples.
Several summary statistics had heavy tailed distributions, which led to very highstandard deviations.
For these summary statistics, we picked the replacement value by hand.
Thefinal values are shown in Table 2.
6.14 Choices of hyperparameters
For the results on the benchmark tasks, we picked the same hyperparameters for TSNPE as those thatwere used in Lueckmann et al.
[2021] for APT (called SNPE in Lueckmann et al.
For both neuroscience tasks, we used a neural spline slow (NSF) as density estimator [Durkan et al.
The hyperparameters of the NSF are the defaults from the ‘sbi’ package [Tejero-Cantero et al.
On both of these tasks, we used ϵ = 10 − 3 .
All other hyperparameters are the defaults fromthe ‘sbi’ package with one exception: We used a batchsize of 500 (instead of the default value 50 ).
For the pyloric network task, we ran APT with 2 atoms.
We reduced the number of atoms from thedefault value in the ‘sbi’ package ( 10 atoms) because a larger number of atoms increased trainingtime.
When using 10 atoms, the training time exceeded the simulation time of the model on thepyloric network task.
With 2 atoms, the training time of APT was comparable to the training time of
TSNPE (albeit still a bit higher).
We implemented the transformation of the parameter space withthe pytorch method ‘biject_to()‘ [Paszke et al., 2019].
For TSNPE, we sampled from the truncatedproposal with rejection sampling.
For the multicompartment model of single-neuron dynamics, we initially sampled parameters fromthe truncated proposal with rejection sampling.
After the third round of training, the rejection rateexceeded 99 . 9% , which rendered rejection sampling more expensive than sampling importanceresampling (SIR).
We thus switched to SIR with K = 1024 for the final rounds (see Sec. 3.2). [Table]
depth
[Table] slow -55.
step [Table]
soma
[Table]
ahp [Table]
step
[Table] height 16.
step
[Table] width 0.
step [Table] _isi [Table]
cv 0.
step3_soma_adaptation_index 0. step3_soma_doublet_isi 21. step3_soma
[Table]
mean_frequency 16. step
[Table] soma
[Table] _to [Table] first_spike 10.
bap_dend1_ap_amplitude_from_voltagebase 53. bap_dend2_ap_amplitude_from_voltagebase 30.
bap_soma_ap_height 37.
bap [Table]
soma_ap
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width 0. bap_soma_spikecount 1. be