Synchronization of nonlinear electronic oscillators for neural computation.

This paper deals with coupled oscillators as the building blocks of a bioinspired computing paradigm and their implementation. In order to accomplish the low-power and fast-processing requirements of autonomous applications, we study the microelectronic analog implementation of physical oscillators, instead of the software computer-simulated implementation. With this aim, the original oscillator has been adapted to a suitable microelectronic form. So as to study the hardware nonlinear oscillators, we propose two macro models, demonstrating that they preserve the synchronization properties. Secondary effects such as mismatch and output delay and their relation to network synchronization are analyzed and discussed. We show the correct operation of the proposed electronic oscillators with simulations and experimental results from a manufactured integrated test circuit. The proposed architecture is intended to perform the scene segmentation stage of an autonomous focal-plane self-contained visual processing system for artificial vision applications.

Scene segmentation using neuromorphic oscillatory networks.

Using the neuromorphic approach, we propose an analog very large-scale integration (VLSI) implementation of an oscillatory segmentation algorithm based on local excitatory couplings and global inhibition. The original model has been simplified and adapted for its efficient VLSI implementation while preserving its segmentation properties. To demonstrate the feasibility of the approach, a 16/spl times/16-pixel testchip has been manufactured. Extensive experimental results demonstrate that it can properly segment binary images. Power consumption, segmentation time per cell, and system complexity are very low compared to other hardware and software implementation schemes. We also show two main differences between the original algorithm and the analog approach. First, the network is noise tolerant without the need of additional elements and second, delays between oscillators due to the combination of mismatch and output capacitances have to be accounted for network performance.