A conductance-based silicon neuron with dynamically tunable model parameters

This paper presents an analog neuromimetic ASIC. It integrates Hodgkin-Huxley (HH) model types, computed in real-time and in analog continuous mode. We developed a library of sub-circuits calculating the elementary mathematical functions encountered in the HH models. Those sub-circuits are organized to form the model set of equations, in which all numerical parameters are dynamically tunable via a mixed analog-digital interface. Neural activity examples are presented to validate the library elements and illustrate the diversity of models simulated by a single ASIC.