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Neuromorphic computing takes inspiration from how the brain works to explore novel computing paradigms. Recently, neuromorphic architectures using spiking neurons were proposed for unsupervised learning of pattern- and feature-based representations. These approaches typically use a common WTA architectural motif of lateral inhibition that introduces competition between the neurons. In this paper,...
Neuromorphic computing takes inspiration from the brain to build highly parallel, energy- and area-efficient architectures. Recently, hardware realizations of neurons and synapses using memristive devices were proposed and applied for the task of correlation detection. However, for weakly correlated signals, this task becomes challenging because of the variability and the asymmetric conductance response...
Neuromorphic systems provide biologically inspired methods of computing, alternative to the classical von Neumann approach. In these systems, computation is performed by a network of spiking neurons controlled by the values of their synaptic weights, which are updated in the process of learning. Providing efficient synaptic learning rules, such as spike-timing-dependent plasticity (STDP), is a challenging...
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