Class FeatureMaps

Overrides: object.__init__

(inherited documentation)

Present the given input patterns and collate the responses.

If weighted_average is True, the feature responses are calculated from a weighted average of the values of each bin in the distribution, rather than simply using the actual value of the parameter for which response was maximal (the discrete method). Such a computation will generally produce much more precise maps using fewer test stimuli than the discrete method. However, weighted_average methods generally require uniform and full-range sampling, as described below, which is not always feasible.

For measurements at evenly-spaced intervals over the full range of possible parameter values, weighted_averages are a good measure of the underlying continuous-valued parameter preference, assuming that neurons are tuned broadly enough (and/or sampled finely enough) that they respond to at least two of the tested parameter values. This method will not usually give good results when those criteria are not met, i.e. if the sampling is too sparse, not at evenly-spaced intervals, or does not cover the full range of possible values. In such cases weighted_average should be set to False, and the number of test patterns will usually need to be increased instead.

selectivity_multiplier

Value:

param.Number(default= 17, bounds= (0.0, None), doc= """ Factor by which to multiply the calculated selectivity values before plotting them. Usually set much greater than 1.0 to highlight particularly unselective areas, especially when combining selectivity with other plots as when using Confidence subplots.""")

sheet_views_prefix

Value:

param.String(default= "", doc= """ Prefix to add to the name under which results are stored in sheet_views.""")