Sentence examples for minimising prediction error from inspiring English sources

In the first section, we review the concept of perceptual inference from a Helmholtzian perspective, and describe how it can be instantiated by minimising prediction error using a hierarchical generative model.

If both systems are minimising prediction error, descending hierarchical projections in the motor cortex should share the laminar, topographic and physiological characteristics of backward connections in exteroceptive (sensory) systems.

This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular.

To minimise prediction error, the brain should use the occurrence of the intensity and frequency deviant to prompt a change in the dominant inference ("expect-the-standard") to anticipate the characteristics of the linked deviant.

The brain can minimise prediction error in one of two ways.

The free energy principle itself explains why it is necessary to minimise prediction errors.

However, there is another way to minimise prediction errors; namely, by re-sampling sensory inputs so that they conform to predictions; in other words, changing sensory inputs by changing the world through action.

Self-supervised models of how the brain represents and categorises the causes of its sensory input can be divided into two classes: those that minimise the mutual information (i.e. redundancy) among evoked responses and those that minimise the prediction error.

Top-down predictions are updated in subsequent iterations of this recurrent process to further minimise the prediction error.

Mechanistically, descending proprioceptive predictions provide a target or set point for peripheral reflex arcs – that respond by minimising (proprioceptive) prediction errors.

The optimal configuration of the parameters was chosen to be the set minimising the mean squared prediction error (MSPE).