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Adams et al. present a Spiking Neural Network implementation of a SOM which uses

- spike-time dependent plasticity
- a method to adapt the learning rate
- constant neighborhood interaction width⇒

The model due to Cuppini et al. develops low-level multisensory integration (spatial principle) such that integration happens only with higher-level input.

In their model, Hebbian learning leads to sharpening of receptive fields, overlap of receptive fields, and Integration through higher-cognitive input.⇒

Deneve describes how neurons performing Bayesian inference on variables behind Poisson inputs can learn the parameters of the Poisson processes in an online variant of the expectation maximization (EM) algorithm.⇒

Deneve associates her EM-based learning rule in Bayesian spiking neurons with spike-time dependent plasticity (stdp)⇒