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 stark …

Abstract Systems neuroscience seeks explanations for how the brain implements a wide
variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to …

Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-efficient
signal processing. To translate these benefits into hardware, a growing number of …

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 …

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre- and postsynaptic activity. However, models that rely solely …

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance
could be established and maintained in an experience-dependent manner by synaptic …

Synaptic plasticity, the putative basis of learning and memory formation, manifests in
various forms and across different timescales. Here we show that the interaction of Hebbian …

Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between
neurons, is considered a key ingredient underlying learning and memory in the brain. However…

Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …

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 solve …