1. Neuroscience

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience

  1. Alexander Fengler  Is a corresponding author
  2. Lakshmi N Govindarajan
  3. Tony Chen
  4. Michael J Frank  Is a corresponding author
  1. Brown University, United States
  2. Boston College, United States
https://doi.org/10.7554/eLife.65074
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  1. Alexander Fengler
  2. Lakshmi N Govindarajan
  3. Tony Chen
  4. Michael J Frank
(2021)
Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience
eLife 10:e65074.
https://doi.org/10.7554/eLife.65074
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Abstract

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

Data availability

All code is provided freely and is available at the following links: https://github.com/lnccbrown/lans/tree/master/hddmnn-tutorial, https://github.com/lnccbrown/lans/tree/master/al-mlp and https://github.com/lnccbrown/lans/tree/master/al-cnn.

Article and author information

Author details

  1. Alexander Fengler

    Robert J and Nancy D Carney Institute for Brain Science; Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, United States
    For correspondence
    alexander_fengler@brown.edu
    Competing interests
    No competing interests declared.

  2. Lakshmi N Govindarajan

    Robert J and Nancy D Carney Institute for Brain Science; Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0936-2919

  3. Tony Chen

    Psychology and Neuroscience, Boston College, Boston, United States
    Competing interests
    No competing interests declared.

  4. Michael J Frank

    Robert J and Nancy D Carney Institute for Brain Science; Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, United States
    For correspondence
    Michael_Frank@brown.edu
    Competing interests
    Michael J Frank, Senior editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8451-0523

Funding

National Institute of Mental Health (P50 MH119467-01)

  • Michael J Frank

National Institute of Mental Health (R01 MH084840-08A1)

  • Michael J Frank

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Fengler et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alexander Fengler
  2. Lakshmi N Govindarajan
  3. Tony Chen
  4. Michael J Frank
(2021)
Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience
eLife 10:e65074.
https://doi.org/10.7554/eLife.65074

Share this article

https://doi.org/10.7554/eLife.65074

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Further reading

    1. Neuroscience

    Flexible and efficient simulation-based inference for models of decision-making

    Jan Boelts, Jan-Matthis Lueckmann ... Jakob H Macke
    Research Advance Updated

    Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.