Breaking Balance: Encoding local error signals in perturbations of excitation-inhibition balance

Efficient learning algorithms such as backpropagation, predictive coding, and self-supervised learning require instructive learning signals to influence synaptic plasticity locally at specific neurons. This requirement is in-consistent with classic Hebbian theories and neuromodulation involving a non-specific third factor, thus raising the question of how neurobiology can achieve efficient learning. Here, we propose a simple and biologically plausible solution: local deviations from precise excitation/inhibition (E/I) balance encode error signals that instruct synaptic plasticity. Using a computational model derived from an adaptive control theory framework, we demonstrate that breaking E/I balance through targeted feedback to inhibitory interneurons, can produce neuron- or assembly-specific error signals that enable learning in multi-layer networks. Simulations reveal that such a balance-controlled plasticity mechanism is consistent with phenomenological local plasticity models while enabling online learning in hierarchically organized networks. Furthermore, we demonstrate that this framework is consistent with key features of disinhibitory microcircuit dynamics during in-vivo learning experiments. These results suggest that the brain may exploit E/I balance not only for stability, but also as a substrate for error-driven learning.