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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(More)
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.(More)
The architecture of a complete image segmentation system and the development of an embedded VLSI low-power integrated circuit are reported. A neuromorphic engineering approach is adopted, with the purpose of reproducing behaviour of biological neural networks by taking advantage of the microelectronic implementation properties, especially low power(More)
In this paper we show a low power and very compact VLSI implementation of a FitzHugh-Nagumo neuron for large network implementations. The circuit consists of only 17 small transistors and two capacitors and consumes less than 23μW. It is composed of a nonlinear resistor and a lossy active inductor. We demonstrate that a simple low Q active inductor can be(More)
In this paper we present a complete neuromorphic image processing system and we report the development of an integrated CMOS low-power circuit to test the feasibility of its different stages. The image system consists of different parallel-processing stages: phototransduction, non-linear filtering, oscillatory segmentation network and post-processing to(More)