  Recent experimental and theoretical studies show that energy efficiency,
which measures the amount of information processed by a neuron with per unit of
energy consumption, plays an important role in the evolution of neural systems.
Here, we calculated the information rates and energy efficiencies of the
Hodgkin-Huxley (HH) neuron model at different temperatures in a noisy
environment. We found that both the information rate and energy efficiency are
maximized by certain temperatures. Though the information rate and energy
efficiency cannot be maximized simultaneously, the neuron holds a high
information processing capacity at the temperature corresponding to maximal
energy efficiency. Our results support the idea that the energy efficiency is a
selective pressure that influences the evolution of nervous systems.
