Towards reliable hybrid bio-silicon integration using novel adaptive control system

Hybrid bio-silicon networks are difficult to implement in practice due to variations of biological neuron bursting frequency. This causes the hybrid network to have inaccuracies and unreliability. The network may produce irregular bursts or incorrect spiking phase relationships if the electrical neuron bursting frequency is not suitable for biological neurons. To solve this potentially vital problem, a novel adaptive control system based on dynamic clamp is proposed. Biological measurement is combined with an adaptive controller to control to silicon neuron bursting periods in real time. We use a hybrid pyloric network which contains three real neurons and one electronic neuron as a case study. Simulation results indicate that the silicon neuron can follow the biological neuron bursting frequency in real time to achieve hybrid network functionalities. System settling time can be achieved in 303 milliseconds and percentage overshoot kept to 1%. We believe that our methodology is scalable to various larger bio-silicon hybrid neural networks.