Multi-Domain SystemC Model of a Neural Interface

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Neural networks have been investigated by researchers for several decades. Microelectrodes and neural interfaces are used to obtain the information contained in the neuronal networks activity, which can be used to control neural prosthetic devices. This field has advanced rapidly and the current research is focusing on multi-channel implementation of neural interface systems to monitor the activity of a large number of neurons simultaneously. Area and safety are two main constraints in the design of neural interface electronics. The chip area constraint is important to minimize the severity of the surgery and limit the displacement of the brain caused by the implanted device. The safety constraint is critical in avoiding the damage to the brain tissue. Both constraints create a limitation on the power consumption of the neural interface system. This limited power budget needs to be utilized carefully to implement a design with low noise and high data rate with as few computational resources as possible. An efficient design allows a large number of channels to be implemented within the allowed power budget. This thesis proposes behavioral models of the electrode and the neural interface front-end, a part which precondition the neural signals before they are further processed and stored. The functionality of the proposed models are verified and, together with a power estimation model, they are used to perform a system study to investigate the trade-offs between neural interface design parameters.