  The resonate-and-fire (RF) neuron, introduced over two decades ago, is a
simple, efficient, yet biologically plausible spiking neuron model, which can
extract frequency patterns within the time domain due to its resonating
membrane dynamics. However, previous RF formulations suffer from intrinsic
shortcomings that limit effective learning and prevent exploiting the
principled advantage of RF neurons. Here, we introduce the balanced RF (BRF)
neuron, which alleviates some of the intrinsic limitations of vanilla RF
neurons and demonstrates its effectiveness within recurrent spiking neural
networks (RSNNs) on various sequence learning tasks. We show that networks of
BRF neurons achieve overall higher task performance, produce only a fraction of
the spikes, and require significantly fewer parameters as compared to modern
RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable
training convergence, even when bridging many hundreds of time steps during
backpropagation through time (BPTT). These results underscore that our BRF-RSNN
is a strong candidate for future large-scale RSNN architectures, further lines
of research in SNN methodology, and more efficient hardware implementations.
