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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.⇒