Implementing a neuromorphic cross-correlation engine with silicon neurons

The cross-correlation function is an important yet computationally intensive processing step in many engineering applications such as wireless communication and object recognition. A neuromorphic approach to this function has been shown to facilitate implementation using a neural-based architecture. Using a custom designed array of silicon neurons on a compact, low-power chip, we demonstrate a cross-correlation system based on two half center oscillators. These preliminary results show the validity of this approach and could provide an elegant solution to wireless communication systems in the next generation of neuroprosthetic devices.