  How neuronal circuits achieve credit assignment remains a central unsolved
question in systems neuroscience. Various studies have suggested plausible
solutions for back-propagating error signals through multi-layer networks.
These purely functionally motivated models assume distinct neuronal
compartments to represent local error signals that determine the sign of
synaptic plasticity. However, this explicit error modulation is inconsistent
with phenomenological plasticity models in which the sign depends primarily on
postsynaptic activity. Here we show how a plausible microcircuit model and
Hebbian learning rule derived within an adaptive control theory framework can
resolve this discrepancy. Assuming errors are encoded in top-down
dis-inhibitory synaptic afferents, we show that error-modulated learning
emerges naturally at the circuit level when recurrent inhibition explicitly
influences Hebbian plasticity. The same learning rule accounts for
experimentally observed plasticity in the absence of inhibition and performs
comparably to back-propagation of error (BP) on several non-linearly separable
benchmarks. Our findings bridge the gap between functional and experimentally
observed plasticity rules and make concrete predictions on inhibitory
modulation of excitatory plasticity.
