This review article summarises recently proposed theories on how neural circuits in the
brain could approximate the error back-propagation algorithm used by artificial neural …

As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

An electronic skin (e-skin) for the next generation of robots is expected to have biological
skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is …

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

In the brain, most synapses are formed on minute protrusions known as dendritic spines.
Unlike their artificial intelligence counterparts, spines are not merely tuneable memory …

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons
coding for a percept are bound together via associative long-term synaptic plasticity. In this …

Neural dendrites play a critical role in various cognitive functions, including spatial
navigation, sensory processing, adaptive learning, and perception. The spatial layout, signal …

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory
networks in the brain must form object representations invariant to stimulus changes, such …

Backpropagation (BP) has been the most successful algorithm used to train artificial neural
networks. However, there are several gaps between BP and learning in biologically …

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …