Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

ABSTRACT: We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task.

SUBMITTER: Shim Y 

PROVIDER: S-EPMC5070787 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature