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We studied neuromorphic models of binocular disparity processing and mapped them onto a vision chip containing a massively parallel analog processor array. Our goal was to make efficient use of the available hardware while preserving the fundamental computations performed by the models. We also developed an optical fixture that used mirrors to simultaneously focus two images onto the vision chip....
We describe a robotic system consisting of an arm and an active vision system learns to align its sensory and motor maps so that it can successfully reach the tip of its arm to touch the point where it is looking. This system uses an unsupervised Hebbian learning algorithm, and learns the alignment by watching its arm waving in front of its eyes. After watching for 25 minutes, the maps are sufficiently...
This paper proposes a silicon neuron circuit which uses a slow-variable controlled leakage term to extend the repertoire of spiking patterns achievable in an integrate and fire model. The simulations reveal the potential of the circuit to provide a wide variety of neuron firing patterns observed in neocortex, including adapting and non-adapting, regular spiking, fast spiking, bursting, chattering,...
FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biological neuron/synaptic models. Also their routing structures cannot accommodate the high levels of neuron inter-connectivity inherent in complex SNNs. This paper...
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