Transient boosting of action potential backpropagation for few-shot temporal pattern learning

One of the remarkable properties of cortical neurons is their innate ability to detect spike patterns in continuous but noisy information streams. As animals learn behaviorally relevant information quickly, pattern detection by neurons should also be rapid. However, how pattern-selective neuronal responses rapidly develop, tune, and remain robust has not been fully understood. Here, we propose a biologically plausible synaptic plasticity rule to learn patterned synaptic inputs rapidly. Our rule facilitates intracellular "self-supervised" learning of intermittently and repeatedly co-activated presynaptic-neuron communities, as in a machine-learning-based rule derived previously for temporal pattern segmentation. Importantly, this model proposes that a spike-triggered transient increase in somatodendritic coupling dramatically boosts the crediting of synapses responsible for the learned response. This boosting effect is essential for rapid pattern learning of single neurons at a high signal-to-noise ratio. Moreover, we demonstrate in recurrent networks how the rule recruits pre-configured cell assemblies for even faster, few-shot learning of multiple patterns. Our results illuminate the self-supervisory role of backpropagating action potentials for rapid pattern learning.