Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

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 …

Spiking neural networks (SNNs) are well known as the brain-inspired models with high
computing efficiency, due to a key component that they utilize spikes as information units …

While deep learning has led to remarkable advances across diverse applications, it
struggles in domains where the data distribution changes over the course of learning. In …

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 …

The close replication of synaptic functions is an important objective for achieving a highly
realistic memristor-based cognitive computation. The emulation of neurobiological learning …

Emulation of brain‐like signal processing with thin‐film devices can lay the foundation for
building artificially intelligent learning circuitry in future. Encompassing higher functionalities …

Decoding the human brain is perhaps the most fascinating scientific challenge in the 21st
century. The Human Brain Project (HBP), a 10-year European Flagship, targets the …

Human visual perception carves a scene at its physical joints, decomposing the world into
objects, which are selectively attended, tracked and predicted as we engage our …

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 …