Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
This work addresses a central problem in systems neuroscience by bridging functional and experimental plasticity rules, making it significant for researchers in neural computation and learning, though it is incremental as it builds on existing adaptive control and Hebbian frameworks.
The study tackled the discrepancy between functional models requiring explicit error signals for synaptic plasticity and phenomenological models where plasticity depends on postsynaptic activity, showing that a microcircuit model with dis-inhibitory afferents enables error-modulated learning and performs comparably to back-propagation on non-linearly separable benchmarks.
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.