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This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four(More)
In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon(More)
This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asynchronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four(More)
We describe the construction and characterization of an event-based hardware vision system (CAVIAR) that learns to classify spatio-temporal trajectories. Our characterization so far showed that stimuli of two different shapes on a rotating disk could simultaneously be discriminated and their position extracted at level of the object chip. CAVIAR is the(More)
I. INTRODUCTION The Address-Event Representation (AER) was proposed by the Mead lab in 1991 [1] for communicating between neuromorphic chips with spikes (Fig. 1). Each time a cell on a sender device generates a spike, it communicates with the array periphery and a digital word representing a code or address for that pixel is placed on the external(More)