  Biological neurons and their in-silico emulations for neuromorphic artificial
intelligence (AI) use extraordinarily energy-efficient mechanisms, such as
spike-based communication and local synaptic plasticity. It remains unclear
whether these neuronal mechanisms only offer efficiency or also underlie the
superiority of biological intelligence. Here, we prove rigorously that, indeed,
the Bayes-optimal prediction and inference of randomly but continuously
transforming environments, a common natural setting, relies on short-term
spike-timing-dependent plasticity, a hallmark of biological synapses. Further,
this dynamic Bayesian inference through plasticity enables circuits of the
cerebral cortex in simulations to recognize previously unseen, highly distorted
dynamic stimuli. Strikingly, this also introduces a biologically-modelled AI,
the first to overcome multiple limitations of deep learning and outperform
artificial neural networks in a visual task. The cortical-like network is
spiking and event-based, trained only with unsupervised and local plasticity,
on a small, narrow, and static training dataset, but achieves recognition of
unseen, transformed, and dynamic data better than deep neural networks with
continuous activations, trained with supervised backpropagation on the
transforming data. These results link short-term plasticity to high-level
cortical function, suggest optimality of natural intelligence for natural
environments, and repurpose neuromorphic AI from mere efficiency to
computational supremacy altogether.
