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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)