Energy-efficient interspike interval codes

We investigate the energy efficiency of interspike interval (ISI) neural codes. Using the hypothesis that nature maximizes the energy efficiency of information processing, it is possible to derive neuronal firing frequencies which maximize the information/energy ratio. With simple assumptions about the encoded ISI and noise distributions, we show that ISI codes can be at least as efficient as discrete binary and frequency codes and that their predicted optimal frequencies are in the same range.