Abstract
A wide variety of neuron models are studied at different positions in the trade-off between reproducibility of neural activity and computational efficiency. The DSSN model is one of them, which is designed so that it can be effectively implemented in digital arithmetic circuits with small hardware resources. We found appropriate parameter sets for the DSSN model that correspond to the Hodgkin-Huxley-type neuron models of 4 classes of cortical and thalamic neurons. Firstly, we developed 3-variable neuron models by applying a dimension reduction technique to those Hodgkin-Huxley-type neuron models, whose mathematical structures are elucidated by bifurcation analysis. Then, we determined the parameter sets that reproduce these mathematical structures and are able to reproduce characteristic behavior in each class.
