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Here we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non-recurrent problems.
The brain attempts to minimize prediction error (the component of the input that is not predicted by the brain's current model).
Free energy, as Friston defined it, was roughly equivalent to what Clark called prediction error; and the brain's need to minimize free energy, or minimize prediction error, Friston believed, drove everything the brain did.
Furthermore, the account successfully overcomes the "dark-room" problem, explaining why we do not avoid stimulation to minimize prediction error.
When the brain strove to minimize prediction error, it was not just trying to reduce its uncertainty about what was going on in the world; it was struggling to resolve the contradictions between fantasy and reality ideally by making reality more like fantasy.
This is then aggregated with the first to minimize prediction error for the labeled instances in the target.
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Specifically, one needs a smearing factor to minimize prediction errors.
By contrast, the Kriging interpolation method attempts to minimize prediction errors, which are themselves estimated (Oliver and Webster 1990).
A particularly novel aspect of the present theory is the proposal that many of a neuron's non-synaptic, intrinsically gated ion channels may be selected in order to minimize prediction errors, or deviations in membrane voltage.
Hence, they are removed to minimize prediction errors.
In this framework, perception corresponds to optimizing the model by changing synaptic activity and connection strengths to minimize prediction errors.
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minimize prediction bias
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minimize classification error
minimize swirl error
minimize sampling error
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com