Concept
Parameter space
Publications associées (25)
Fitting summary statistics of neural data with a differentiable spiking network simulator
Wulfram Gerstner, Johanni Michael Brea, Alireza Modirshanechi, Shuqi Wang
Fitting network models to neural activity is an important tool in neuroscience. A popular approach is to model a brain area with a probabilistic recurrent spiking network whose parameters maximize the likelihood of the recorded activity. Although this is w ...
2021Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler
Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, Marco Pizzolato, Jonathan Rafael Patino Lopez, Thomas Yu
Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally pr ...
SPRINGER INTERNATIONAL PUBLISHING AG2019Estimation of groundwater storage from seismic data using deep learning
We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks a ...
Wiley2019Approximate maximum likelihood estimation for population genetic inference
In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. Howev ...
De Gruyter2017