  Memory is a key component of biological neural systems that enables the
retention of information over a huge range of temporal scales, ranging from
hundreds of milliseconds up to years. While Hebbian plasticity is believed to
play a pivotal role in biological memory, it has so far been analyzed mostly in
the context of pattern completion and unsupervised learning. Here, we propose
that Hebbian plasticity is fundamental for computations in biological neural
systems. We introduce a novel spiking neural network architecture that is
enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment
renders spiking neural networks surprisingly versatile in terms of their
computational as well as learning capabilities. It improves their abilities for
out-of-distribution generalization, one-shot learning, cross-modal generative
association, language processing, and reward-based learning. As spiking neural
networks are the basis for energy-efficient neuromorphic hardware, this also
suggests that powerful cognitive neuromorphic systems can be build based on
this principle.
