Biomedical Engineering Reference
In-Depth Information
complex and powerful models by combining simpler models that often map different regions of the
input space [
47
]. With boosting, models are weighted to create a strong ensemble decisions so that
a weighted majority “votes” for the appropriate class labeling [
47
]. This is analogous to what the
ICHMM currently does with a global
ζ
weighting or biasing.
In the next chapter, we will shift the focus from signal processing methods that use estimates
of the instantaneous spike rate through binning and concentrate on the spike trains directly. The
trade-offs of computing in BMIs directly with spike trains or with rate codes can have an impact on
the ability to resolve modulation, correlation, integration, and coincidence in neuronal representa-
tions necessary for accurate reconstruction of the sensory or motor function. The implications and
significance of the choice of spike traines in BMI approaches will be reviewed next.
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