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Information Capacity and Transmission in a Courbage–Nekorkin–Vdovin Map-Based Neuron Model

The map-based neuron models have received attention as valid phenomenological neuron models due to their computational efficiency and flexibility to generate rich patterns. Here we evaluate the information capacity and transmission of the Courbage–Nekorkin–Vdovin (CNV) map-based neuron model with a bursting and tonic firing mode in response to external pulse inputs, in both temporal and rate coding schemes. We find that for both firing modes, the CNV model could capture the essential behavior of the stochastic Hodgkin–Huxley model in information transmission for the temporal coding scheme, with regard to the dependence of total entropy, noise entropy, information rate, and coding efficiency on the strength of the input signal. However, in tonic firing mode, it fails to replicate the input strength-dependent information rate in the rate coding scheme. Our results suggest that the CNV map-based neuron model could capture the essential behavior of information processing of typical conductance-based neuron models.