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Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, the authors present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. They first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, the authors demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.