Gabriel Jiménez-Moreno

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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 recent implementations of neuromorphic spike-based sensors, multi-neuron processors, and actuators; the spike traffic between devices is coded in the form of asynchronous spike streams following the Address-Event-Representation protocol. This spike information can be modified during the transmission from one device to another by using a mapper device.(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)
—In this paper, a chip that performs real-time image convolutions with programmable kernels of arbitrary shape is presented. The chip is a first experimental prototype of reduced size to validate the implemented circuits and system level techniques. The convolution processing is based on the address–event-representation (AER) technique, which is a(More)
This paper addresses the problem of converting a conventional video stream based on sequences of frames into the spike event-based representation known as the address-event-representation (AER). In this paper we concentrate on rate-coded AER. The problem is addressed as an algorithmic problem, in which different methods are proposed, implemented and tested(More)