Predictive learning with local plasticity in excitatory-inhibitory networks

Predictive coding is a powerful normative framework for understanding cortical computation, but it is still an open question how biologically plausible networks with local plasticity support predictive inference and representation learning. In this work we show that a recurrent excitatory-inhibitory circuit with purely local plasticity can perform predictive inference without explicit error representations. We establish a direct analytic link that shows that learning in these circuits requires the weights to remain on a consistency manifold where recurrent inhibition matches the inhibition required by the predictive coding objective. Using a closed-form derivation of the consistency condition, we derive a plasticity rule that maintains it exactly under a Gaussian prior. Under a non-Gaussian prior, we find the rule supports learning sparse, factorized features, such as edge detectors from natural images. We show empirically that a BCM-like rule with an activity-dependent threshold approximates this well, while other Hebbian-like rules tend to learn less accurate solutions because they keep the weights too far from this manifold. With recurrent excitation the networks acquire spatiotemporal features like direction selectivity, and the ability to complete partially observed sequences. Time-continuous learning then leads to the development of low-dimensional attractor-like structures and noise-driven replay. Overall, these results link predictive coding to local circuit plasticity, show it does not require an explicit prediction error representation, and suggest a normative role for BCM-like plasticity in excitatory synapses.