  Spiking neural networks, also often referred to as the third generation of
neural networks, carry the potential for a massive reduction in memory and
energy consumption over traditional, second-generation neural networks.
Inspired by the undisputed efficiency of the human brain, they introduce
temporal and neuronal sparsity, which can be exploited by next-generation
neuromorphic hardware. To open the pathway toward engineering applications, we
introduce this exciting technology in the context of continuum mechanics.
However, the nature of spiking neural networks poses a challenge for regression
problems, which frequently arise in the modeling of engineering sciences. To
overcome this problem, a framework for regression using spiking neural networks
is proposed. In particular, a network topology for decoding binary spike trains
to real numbers is introduced, utilizing the membrane potential of spiking
neurons. As the aim of this contribution is a concise introduction to this new
methodology, several different spiking neural architectures, ranging from
simple spiking feed-forward to complex spiking long short-term memory neural
networks, are derived. Several numerical experiments directed towards
regression of linear and nonlinear, history-dependent material models are
carried out. A direct comparison with counterparts of traditional neural
networks shows that the proposed framework is much more efficient while
retaining precision and generalizability. All code has been made publicly
available in the interest of reproducibility and to promote continued
enhancement in this new domain.
