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Abstract:
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 in 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.
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