Ramin Hosseiny

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Applications of recurrent neural networks (RNNs) tend to be rare because training is difficult. A recent theoretical breakthrough [Jae01b] called Echo State Networks (ESNs) has made RNN training easy and fast and makes RNNs a versatile tool for many problems. The key idea is training the output weights only of an otherwise topologically unrestricted but(More)
In this paper we show how a combination of multiple neuromorphic vision sensors can achieve the same higher level visual processing tasks as carried out by a conventional vision system. We process the multiple neuromorphic sensory signals with a standard auto-regression method in order to fuse the sensory signals and to achieve higher level vision(More)
In this paper we show how a combination of low dimensional vision sensors can be used to aid the higher level visual processing task of colour blob tracking, carried out by a conventional vision system. The processing elements are neuromorphic analog VLSI (aVLSI) vision sensors capable of computing motion and estimating the spatial position of high-contrast(More)
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