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Lee and Mumford link their theory to resonance and predictive coding.

Neurons in Deneve's model actually generate Poisson-like output themselves (though deterministically).

The process it generates is described as predictive. A neuron $n_1$ fires if the probability $P_1(t)$ estimated by $n_1$ based on its input is greater than the probability $P_2(t)$ estimated by another neuron $n_2$ based on $n_1$'s input.

Predictive coding can implement the EM algorithm.

In predictive coding, a model iterates the following steps:

  • assume values for latent variables,
  • predict sensory input (through a generative model),
  • observe prediction error,
  • adapt assumptions to minimize the error.

Friston's predictive coding model predicts a hierarchical cortical system.

Given a generative model, it can be possible to find the most likely cause (or causes) of a sensation even if the causes interact in complex ways.

Some authors see the lower stages of visual processing as implementing an inverse model of optics—a model deriving causes from sensations and higher stages as implementing a forward model—a model generating expected sensations from assumed causes.

In Friston's architecture, competitive learning serves to de-correlate error units.

My SOMs learn competitively. But they actually don't encode error but latent variables.

Friston states that `models that do not show conditional independence (e.g. those used by connectionist and infomax schemes) depend on prior constraints for unique inference and do not invoke a hierarchical cortical organization;'

What does `models that do not show conditional independence' mean? Does it include SOMs?

If what Friston means by `models that do not show conditional independence' includes SOM, then that would explain why I can't find an error signal. Maybe the prior constraint invoked by SOMs is similarity between stimuli?

Possibly, this is a point for future work: model cortico-collicular connections as prediction. But, in Friston's framework, there would have to be ascending connections, too.

Redundancy reduction, predictive coding, efficient coding, sparse coding, and energy minimization are related hypotheses with similar predictions. All these theories are reasonably successful in explaining biological phenomena.

According to Spratling's model, saliency arises from unexpected features in a scene.

Predictive coding and biased competition are closely related concepts. Spratling combines them in his model and uses it to explain visual saliency.

Much of the activity of cognitive systems is not only due to current stimuli, but also to a large degree to previous experience, specifically due to the expectations following from it.