Hebbian Learning of Bayes Optimal Decisions (2009)
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models for Bayesian decision...
Hebbian learning of Bayes optimal decisions (2009)
Nessler, Bernhard, Pfeiffer, Michael, Maass, Wolfgang
When we perceive our environment, make a decision, or take an action, our brain has to deal with multiple sources of uncertainty. The Bayesian framework of statistical estimation provides...
Reward-modulated Hebbian Learning of Optimal Decision Making (2009)
Pfeiffer, Michael, Nessler, Bernhard, Douglas, Rodney J., Maass, Wolfgang
We introduce a framework for Bayesian decision making in which the learning of optimal decisions is reduced to its simplest and biologically most plausible form: Hebbian learning on a linear neuron....
A Hebbian Learning Rule for Optimal Decision Making (2008)
Pfeiffer, Michael, Nessler, Bernhard, Maass, Wolfgang
Humans and animals can learn to make close to optimal decisions, even if there are multiple sources of uncertainty in their environment. The Bayesian framework has proved to be a valuable...