Long- and short-term history effects in a spiking network model of statistical learning (Maes et al accepted)

We map inverse transform learning onto spiking networks. We show that the model manages to learn from repeated observations of a variable and samples from the target distribution during spontaneous dynamics.

Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron; Abstract integrate-and-fire neuron

Simulation Environment: MATLAB

Implementer(s): Maes, Amadeus [amadeus.maes at gmail.com]

References:

Maes A, Barahona M, Clopath C. (accepted). Long- and short-term history effects in a spiking network model of statistical learning Scientific Reports.