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Found 3 papers, 2 papers with code
Likelihood-free inference with emulator networks
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods.
Flexible statistical inference for mechanistic models of neural dynamics
Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters.
A Bayesian model for identifying hierarchically organised states in neural population activity
Here, we present a statistical model for extracting hierarchically organised neural population states from multi-channel recordings of neural spiking activity.
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