Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution ...
By combining spike-triggered covariance analysis with the Bayesian GLM framework, we will present a new method for selecting the filters of this subspace.
Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution ...
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and ...
Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution ...
Using the expectation propagation algorithm, the Bayesian treatment of generalized linear models is presented, able to approximate the full posterior ...
Poster. Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. Sebastian Gerwinn · Jakob H Macke · Matthias Seeger · Matthias Bethge. Abstract:.
Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full posterior distribution ...
We propose a new interpretation of spiking neurons as Bayesian integra- tors accumulating evidence over time about events in the external world or the body.
By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show ...