G
Giacomo Bassetto
Researcher at Center of Advanced European Studies and Research
Publications - 12
Citations - 440
Giacomo Bassetto is an academic researcher from Center of Advanced European Studies and Research. The author has contributed to research in topics: Bayesian inference & Inference. The author has an hindex of 7, co-authored 12 publications receiving 285 citations. Previous affiliations of Giacomo Bassetto include Technische Universität München & Max Planck Society.
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Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J. Gonçalves,Pedro J. Gonçalves,Jan-Matthis Lueckmann,Jan-Matthis Lueckmann,Michael Deistler,Marcel Nonnenmacher,Marcel Nonnenmacher,Kaan Öcal,Kaan Öcal,Giacomo Bassetto,Giacomo Bassetto,Chaitanya Chintaluri,William F Podlaski,Sara A Haddad,Tim P. Vogels,David S. Greenberg,Jakob H. Macke,Jakob H. Macke +17 more
TL;DR: A machine learning tool that uses density estimators based on deep neural networks— trained using model simulations—to infer data-compatible parameters for a wide range of mechanistic models will help close the gap between data-driven and theory-driven models of neural dynamics.
Journal ArticleDOI
Training deep neural density estimators to identify mechanistic models of neural dynamics.
Pedro J. Gonçalves,Pedro J. Gonçalves,Jan-Matthis Lueckmann,Jan-Matthis Lueckmann,Michael Deistler,Michael Deistler,Marcel Nonnenmacher,Marcel Nonnenmacher,Kaan Öcal,Kaan Öcal,Giacomo Bassetto,Giacomo Bassetto,Chaitanya Chintaluri,Chaitanya Chintaluri,William F Podlaski,Sara A Haddad,Tim P. Vogels,Tim P. Vogels,David S. Greenberg,Jakob H. Macke +19 more
TL;DR: A machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features, which will help close the gap between data-driven and theory-driven models of neural dynamics.
Posted Content
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann,Pedro J. Gonçalves,Giacomo Bassetto,Kaan Öcal,Marcel Nonnenmacher,Jakob H. Macke +5 more
TL;DR: This work builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters, and learns a Bayesian mixture-density network approximating the posterior over multiple rounds of adaptively chosen simulations.
Posted Content
Likelihood-free inference with emulator networks
TL;DR: The authors use probabilistic neural emulator networks to learn synthetic likelihoods on simulated data, both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data.
Proceedings Article
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann,Pedro J. Gonçalves,Giacomo Bassetto,Kaan Öcal,Marcel Nonnenmacher,Jakob H. Macke +5 more
TL;DR: In this article, a Bayesian mixture-density network is used to approximate the posterior distribution over multiple rounds of adaptively chosen simulations for single-neuron models, which can be used to perform Bayesian inference on complex neuron models without having to design model specific algorithms.