Summary: A variety of neuron models have been presented so far, where there exist two major modeling approaches: a nonlinear ordinary differential equation approach and a nonlinear difference equation approach. On the other hand, recently, our group has been developing a new hardware-oriented neuron modeling approach: a reconfigurable asynchronous cellar automaton approach. In this chapter, neuron-like bifurcations of the asynchronous cellar automaton neuron model are analyzed and an on-chip learning algorithm (on-chip dynamic circuit reconfiguration algorithm) for reproducing biological neuron’s behaviors is demonstrated.
For the entire collection see [Zbl 1358.92006].