  Darwinian evolution tends to produce energy-efficient outcomes. On the other
hand, energy limits computation, be it neural and probabilistic or digital and
logical. Taking a particular energy-efficient viewpoint, we define neural
computation and make use of an energy-constrained, computational function. This
function can be optimized over a variable that is proportional to the number of
synapses per neuron. This function also implies a specific distinction between
ATP-consuming processes, especially computation \textit{per se} vs the
communication processes including action potentials and transmitter release.
Thus to apply this mathematical function requires an energy audit with a
partitioning of energy consumption that differs from earlier work. The audit
points out that, rather than the oft-quoted 20 watts of glucose available to
the brain \cite{sokoloff1960metabolism,sawada2013synapse}, the fraction
partitioned to cortical computation is only 0.1 watts of ATP. On the other hand
at 3.5 watts, long-distance communication costs are 35-fold greater. Other
novel quantifications include (i) a finding that the biological vs ideal values
of neural computational efficiency differ by a factor of $10^8$ and (ii) two
predictions of $N$, the number of synaptic transmissions needed to fire a
neuron (2500 vs 2000).
