Table 2 shows the effect of changing the gain penalty from 0.1 (10 gains score the same as one loss) to 10 (10 losses score the same as one gain) for the genome-tree topology and the rRNA tree topology on the reconstructed gene sets of LUCA and the last common ancestors of archaea and bacteria, and on various characteristics of the parsimonious scenarios.
Changing the gain penalty affects the distribution of gains and losses among the vertices and leaves in a predictable way: scenarios for g = 0.9 are dominated by gains and have very few losses at internal nodes (Figs. 13, 14), whereas the scenarios with increasing g values show progressive increase in the early losses, e.g. at the common ancestor of the archaea (Figs. 15, 16, 17, 18, 19, 20).
The ID3 algorithm can be used to build a decision tree for regression by changing Information Gain with Standard Deviation Reduction.
The modulation is performed by changing the gain of the core, that results in different transmittance through the waveguides.
Changing the gain in units of fitness by increasing the scaling constant γ leads to an increase in the effort that should be shown by individuals: if there is more to be gained from being correct, it pays to invest more effort (figure 1A).
Figure 10 summarizes the effects of changing the gain of either the cerebellum (learning) or the inferior olive (coupling).
Changing the gain of the inferior olive coupling to 20%, 40% or 80% of full strength caused the movements to grow from nothing to a moderately sized oscillation.
The measured signals are further amplified by changing the gain voltage across the micro-channel plate (MCP) in the intensifier before impinging on a P43 phosphor screen.
In particular, the altered membrane conductances produced by concurrent excitation and inhibition are believed to be a fundamental mechanism for changing the gain and dynamic properties of neurons (Chance et al., 2002; Mitchell and Silver, 2003; Abbott and Chance, 2005).
Thus, it may be argued that neuronal synchrony could regulate the flow of information in the cortical circuit not only by changing the gain (Salinas and Sejnowski 2000) but also by switching the network states.
