96 citations · 203 across the 8 of their papers we have counts for
10 papers
Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish
Jan-Matthis Lueckmann, Viren Jain, Michał Januszewski
Constructing mechanistic models of neural circuits is a fundamental goal of neuroscience, yet verifying such models is limited by the lack of ground truth. To rigorously test model…
An AI system to help scientists write expert-level empirical software
Eser Aygün, Anastasiya Belyaeva, Gheorghe Comanici +39
The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments\cite{hannay2009how}. To address this, we…
Simulation-Based Inference: A Practical Guide
Michael Deistler, Jan Boelts, Peter Steinbach +11
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers…
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Jan-Matthis Lueckmann, Alexander Immer, Alex Bo-Yuan Chen +15
Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Predi…
Forecasting Whole-Brain Neuronal Activity from Volumetric Video
Alexander Immer, Jan-Matthis Lueckmann, Alex Bo-Yuan Chen +14
Large-scale neuronal activity recordings with fluorescent calcium indicators are increasingly common, yielding high-resolution 2D or 3D videos. Traditional analysis pipelines reduc…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts +4
Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generativ…
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg +2
Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a…
Likelihood-free inference with emulator networks
Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos +1
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC…
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto +3
Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically…