  Dale's principle implies that biological neural networks are composed of
neurons that are either excitatory or inhibitory. While the number of possible
architectures of such Daleian networks is exponentially smaller than
non-Daleian ones, the computational and functional implications of using
Daleian networks by the brain are mostly unknown. Here, we use models of
recurrent spiking neural networks and rate-based networks to show,
surprisingly, that despite the structural limitations on Daleian networks, they
can approximate the computation performed by non-Daleian networks to a very
high degree of accuracy. Moreover, we find that Daleian networks are more
functionally robust to synaptic noise. We then show that unlike non-Daleian
networks, Daleian ones can learn efficiently by tuning single neuron features,
nearly as well as learning by tuning individual synaptic weights - suggesting a
simpler and more biologically plausible learning mechanism. We thus suggest
that in addition to architectural simplicity, Dale's principle confers
computational and learning benefits for biological networks, and offers new
directions for constructing and training biologically-inspired artificial
neural networks
