Information Capacity and Robustness of Stochastic Neuron Models

The reliability and accuracy of spike trains have been shown to depend on the nature of the stimulus that the neuron encodes. Adding ion channel stochasticity to neuronal models results with a macroscopic behavior that replicates the input-dependent reliability and precision of real neurons. We calculate the amount of information that an ion channel based stochastic Hodgkin-Huxley (HH) neuron model can encode about a wide set of stimuli. We show that both the information rate and the information per spike of the stochastic model is similar to the values reported experimentally. Moreover, the amount of information that the neuron encodes is correlated with the amplitude of fluctuations in the input, and less so with the average firing rate of the neuron. We also show that for the HH ion channel density, the information capacity is robust to changes in the density of ion channels in the membrane, whereas changing the ratio between the Na+ and K+ ion channels has a considerable effect on the information that the neuron can encode. This suggests that neurons may maximize their information capacity by appropriately balancing the density of the different ion channels that underlies neuronal excitability.