  Optimization results are one method for understanding neural computation from
Nature's perspective and for defining the physical limits on neuron-like
engineering. Earlier work looks at individual properties or performance
criteria and occasionally a combination of two, such as energy and information.
Here we make use of Jaynes' maximum entropy method and combine a larger set of
constraints, possibly dimensionally distinct, each expressible as an
expectation. The method identifies a likelihood-function and a sufficient
statistic arising from each such optimization. This likelihood is a
first-hitting time distribution in the exponential class. Particular constraint
sets are identified that, from an optimal inference perspective, justify
earlier neurocomputational models. Interactions between constraints, mediated
through the inferred likelihood, restrict constraint-set parameterizations,
e.g., the energy-budget limits estimation performance which, in turn, matches
an axonal communication constraint. Such linkages are, for biologists,
experimental predictions of the method. In addition to the related likelihood,
at least one type of constraint set implies marginal distributions, and in this
case, a Shannon bits/joule statement arises.
