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Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance.

, , , and . CoRR, (2017)

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Local structure helps learning optimized automata in recurrent neural networks., , and . IJCNN, page 1-7. IEEE, (2015)eMorph: Towards Neuromorphic Robotic Vision., , , , , , , , and . FET, volume 7 of Procedia Computer Science, page 163-165. Elsevier, (2011)Neuromorphic Bistable VLSI Synapses with Spike-Timing-Dependent Plasticity.. NIPS, page 1091-1098. MIT Press, (2002)A hybrid analog/digital Spike-Timing Dependent Plasticity learning circuit for neuromorphic VLSI multi-neuron architectures., , , and . ISCAS, page 854-857. IEEE, (2014)Live demonstration: State-dependent sensory processing in networks of VLSI spiking neurons., , , , and . ISCAS, page 2788. IEEE, (2010)A Current-mode Conductance-based Silicon Neuron for Address-event Neuromorphic Systems., and . ISCAS, page 2898-2901. IEEE, (2009)Silicon neurons., , and . Scholarpedia, 3 (3): 1887 (2008)Processing EMG signals using reservoir computing on an event-based neuromorphic system., , , , , , , and . BioCAS, page 1-4. IEEE, (2018)Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies., and . BioCAS, page 552-555. IEEE, (2016)Robust state-dependent computation in neuromorphic electronic systems., and . BioCAS, page 1-4. IEEE, (2017)