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Neuromorphic systems are engineering solutions that take inspiration from biological neural systems. They use spike-or event-based representation and codification of the information. This codification allows performing complex computations, filters, classifications and learning in a pseudo-simultaneous way. Small incremental processing is done per event, which shows useful results with very low latencies...
Neuromorphic engineering is a growing and promising discipline nowadays. Neuro-inspiration and brain understanding applied to solve engineering problems is boosting new architectures, solutions and products today. The biological brain and neural systems process information at relatively low speeds through small components, called neurons, and it is impressive how they connect each other to construct...
In this paper, we explore the capabilities of a sound classification system that combines both a novel FPGA cochlear model implementation and a bio-inspired technique based on a trained convolutional spiking network. The neuromorphic auditory system that is used in this work produces a form of representation that is analogous to the spike outputs of the biological cochlea. The auditory system has...
A key requirement for modern large scale neuromorphic systems is the ability to detect and diagnose faults and to explore self-correction strategies. In particular, to perform this under area-constraints which meet scalability requirements of large neuromorphic systems. A bio-inspired online fault detection and self-correction mechanism for neuro-inspired PID controllers is presented in this paper...
This paper presents a new DVS sensor with one order of magnitude improved contrast sensitivity over previous reported DVSs. This sensor has been applied to a bio-inspired event-based binocular system that performs 3D event-driven reconstruction of a scene. Events from two DVS sensors are matched by using precise timing information of their ocurrence. To improve matching reliability, satisfaction of...
Address-event representation (AER) is a neuromorphic communication protocol for transferring information of spiking neurons implemented into VLSI chips. These neuro-inspired implementations have been used to design sensor chips (retina, cochleas), processing chips (convolutions, filters) and learning chips, what makes possible the development of complex, multilayer, multichip neuromorphic systems...
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