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In fact, all organisms use a mechanism that can reduce the maximal entropy to 0, which means equal probability for all amino acids when encoding protein sequences.
For similar reason for encoding protein sequences, in order to cope with changing environments, organisms have to use a mechanism that can generate any distribution, including equal distribution, to reduce the maximal entropy to 0. This kind of mechanism provides flexibility for organisms to change the quantities of certain proteins any time to adapt the environments.
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Thus, using the maximal entropy principle to characterize spike statistics in the gIF model by expressing constraints in terms of spike events (monomials), one can at best find an approximation which can be rather bad, especially if those constraints focus on instantaneous spike patterns ( D = 0 ) or short memory patterns.
The infinite range of the potential corresponds, in the maximal entropy principle interpretation, to having infinitely many constraints.
The maximal entropy method has been used to identify association of genes [ 11, 12].
The information-theoretic analysis that we use is called surprisal analysis [ 19] to emphasize that at maximal entropy genes are not necessarily equally expressed.
We give an answer to this question using a maximal entropy approach.
A maximal entropy production principle (MEPP) for chemical reactions was proposed to develop a sharp interface model for rapid solidification.
In this way, the truncated potential corresponds to a finite number of constraints in the maximal entropy principle interpretation.
4 From the low-frequency power (LF; 0.02 0.15 Hz) and the high-frequency power (HF; 0.15 0.50 Hz), the ratio of LF to HF power (LF/HF) was calculated using the maximal entropy method (MEM).
From the low-frequency power (LF; 0.02 0.15 Hz) and high-frequency power (HF; 0.15 0.50 Hz), the ratio of LF to HF power (LF/HF) was calculated using the maximal entropy method.
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