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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Vetter, Julius Schröder, Cornelius Moss, Guy Gao, Richard Macke, Jakob H. Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen Germany Tübingen AI Center Germany Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations-an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based-leveraging the Sliced-Wasserstein distance to measure the discrepancy between the dataset and simulations-and thus suitable for simulators with intractable likelihoods. We benchmark our method on several tasks, and show that it can recover source distributions with substantially higher entropy than recent source estimation methods, without sacrificing the fidelity of the simulations. Finally, to demonstrate the utility of our approach, we infer source distributions for parameters of the Hodgkin-Huxley model from experimental datasets with hundreds of single-neuron measurements. In summary, we propose a principled method for inferring source distributions of scientific simulator parameters while retaining as much uncertainty as possible. © 2024 Neural information processing systems foundation. All rights reserved.

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Latent Diffusion for Neural Spiking Data 38

Latent Diffusion for Neural Spiking Data

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kapoor, Jaivardhan Schulz, Auguste Vetter, Julius Pei, Felix Gao, Richard Macke, Jakob H. Machine Learning in Science University of Tübingen & Tübingen AI Center Tübingen Germany Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to project discrete high-dimensional spiking data into continuous time-aligned latents. On these inferred latents, we train expressive (conditional) diffusion models, enabling us to sample neural activity with realistic single-neuron and population spiking statistics. We validate LDNS on synthetic data, accurately recovering latent structure, firing rates, and spiking statistics. Next, we demonstrate its flexibility by generating variable-length data that mimics human cortical activity during attempted speech. We show how to equip LDNS with an expressive observation model that accounts for single-neuron dynamics not mediated by the latent state, further increasing the realism of generated samples. Finally, conditional LDNS trained on motor cortical activity during diverse reaching behaviors can generate realistic spiking data given reach direction or unseen reach trajectories. In summary, LDNS simultaneously enables inference of low-dimensional latents and realistic conditional generation of neural spiking datasets, opening up further possibilities for simulating experimentally testable hypotheses. © 2024 Neural information processing systems foundation. All rights reserved.

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Sourcerer: sample-based maximum entropy source distribution estimation 24

Sourcerer: sample-based maximum entropy source distribution ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Julius Vetter Guy Moss Cornelius Schröder Richard Gao Jakob H. Macke Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center and Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (纸本)9798331314385

Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations—an inference task also known as source distribution estimation. This problem can be ill-posed, however, since many different source distributions might produce the same distribution of data-consistent simulations. To make a principled choice among many equally valid sources, we propose an approach which targets the maximum entropy distribution, i.e., prioritizes retaining as much uncertainty as possible. Our method is purely sample-based—leveraging the Sliced-Wasserstein distance to measure the discrepancy between the dataset and simulations—and thus suitable for simulators with intractable likelihoods. We benchmark our method on several tasks, and show that it can recover source distributions with substantially higher entropy than recent source estimation methods, without sacrificing the fidelity of the simulations. Finally, to demonstrate the utility of our approach, we infer source distributions for parameters of the Hodgkin-Huxley model from experimental datasets with hundreds of single-neuron measurements. In summary, we propose a principled method for inferring source distributions of scientific simulator parameters while retaining as much uncertainty as possible.

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Adaptive deep probabilistic regression for real-time motor excitability state prediction from human EEG

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Brain Stimulation 2025年第1期18卷 400-401页

作者： Haxel, Lisa Kapoor, Jaivardhan Ahola, Oskari Kahilakoski, Olli-Pekka Kirchhoff, Miriam Roine, Timo Ziemann, Ulf Macke, Jakob H. Machine Learning in Science Excellence Cluster Machine Learning & Tübingen AI Center Germany Hertie Institute for Clinical Brain Research Department Neurology and Stroke Germany Department of Neuroscience and Biomedical Engineering Aalto University School of Science Finland Department Empirical Inference Max Planck Institute for Intelligent Systems Germany

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Metrizing weak convergence with maximum mean discrepancies

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 8764-8783页

作者： Carl-Johann Simon-Gabriel Alessandro Barp Bernhard Schölkopf Lester Mackey Institute for Machine Learning ETH Zürich Switzerland Empirical Inference Department MPI for Intelligent Systems Tübingen GermanyDepartment of Engineering University of Cambridge Alan Turing Institute UK Empirical Inference Department MPI for Intelligent Systems Tübingen Germany Microsoft Research Cambridge MA

This paper characterizes the maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures for a wide class of kernels. More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose RKHS-functions vanish at infinity (i.e.,ℌk ⊂ C0), metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (∫s.p.d.) over all signed, finite, regular Borel measures. We also correct a prior result of Simon-Gabriel and Schölkopf (JMLR 2018, Thm. 12) by showing that there exist both bounded continuous ∫s.p.d. kernels that do not metrize weak convergence and bounded continuous non-∫s.p.d. kernels that do metrize it.

关键词： maximum mean discrepancy metrization of weak convergence kernel mean embeddings characteristic kernels integrally strictly positive definite kernels

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Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation

arXiv

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arXiv 2024年

作者： Vetter, Julius Moss, Guy Schröder, Cornelius Gao, Richard Macke, Jakob H. Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen Tübingen Germany Tübingen AI Center Tübingen Germany Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

关键词： Digital elevation model

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Latent Diffusion for Neural Spiking Data

arXiv

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arXiv 2024年

关键词： Neurons

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Simultaneous identification of models and parameters of scientific simulators 24

Simultaneous identification of models and parameters of scie...

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Proceedings of the 41st International Conference on machine learning

作者： Cornelius Schröder Jakob H. Macke Machine Learning in Science University of Tübingen and Tübingen AI Center Germany Machine Learning in Science University of Tübingen and Tübingen AI Center Germany and Max Planck Institute for Intelligent Systems Department Empirical Inference Tübingen Germany

Many scientific models are composed of multiple discrete components, and scientists often make heuristic decisions about which components to include. Bayesian inference provides a mathematical framework for systematically selecting model components, but defining prior distributions over model components and developing associated inference schemes has been challenging. We approach this problem in a simulation-based inference framework: We define model priors over candidate components and, from model simulations, train neural networks to infer joint probability distributions over both model components and associated parameters. Our method, simulation-based model inference (SBMI), represents distributions over model components as a conditional mixture of multivariate binary distributions in the Grassmann formalism. SBMI can be applied to any compositional stochastic simulator without requiring likelihood evaluations. We evaluate SBMI on a simple time series model and on two scientific models from neuroscience, and show that it can discover multiple data-consistent model configurations, and that it reveals nonidentifiable model components and parameters. SBMI provides a powerful tool for data-driven scientific inquiry which will allow scientists to identify essential model components and make uncertainty-informed modelling decisions.

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Generalized Bayesian inference for scientific simulators via amortized cost estimation 23

Generalized Bayesian inference for scientific simulators via...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Richard Gao Michael Deistler Jakob H. Macke Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center Machine Learning in Science Excellence Cluster Machine Learning University of Tübingen and Tübingen AI Center and Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be overly restrictive. Generalized Bayesian inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data. However, GBI methods generally require running multiple simulations to estimate the cost function at each parameter value during inference, making the approach computationally infeasible for even moderately complex simulators. Here, we propose amortized cost estimation (ACE) for GBI to address this challenge: We train a neural network to approximate the cost function, which we define as the expected distance between simulations produced by a parameter and observed data. The trained network can then be used with MCMC to infer GBI posteriors for any observation without running additional simulations. We show that, on several benchmark tasks, ACE accurately predicts cost and provides predictive simulations that are closer to synthetic observations than other SBI methods, especially for misspecified simulators. Finally, we apply ACE to infer parameters of the Hodgkin-Huxley model given real intracellular recordings from the Allen Cell Types Database. ACE identifies better data-matching parameters while being an order of magnitude more simulation-efficient than a standard SBI method. In summary, ACE combines the strengths of SBI methods and GBI to perform robust and simulation-amortized inference for scientific simulators.

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Latent diffusion for neural spiking data 24

Latent diffusion for neural spiking data

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jaivardhan Kapoor Auguste Schulz Julius Vetter Felix Pei Richard Gao Jakob H. Macke Machine Learning in Science University of Tübingen & Tübingen AI Center Tübingen Germany Machine Learning in Science University of Tübingen & Tübingen AI Center Tübingen Germany and Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

ISBN: (纸本)9798331314385

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