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.

翻译：神经元回路如何实现信用分配仍是系统神经科学中一个未解决的核心问题。多项研究提出了通过多层网络反向传播误差信号的可行方案。这些纯粹功能驱动的模型假设不同的神经元区室代表决定突触可塑性符号的局部误差信号。然而，这种显式误差调制与现象学可塑性模型不一致——后者中可塑性符号主要取决于突触后活动。本文展示了一种基于自适应控制理论框架推导的合理微回路模型与赫布学习规则如何解决这一矛盾。假设误差由自上而下的去抑制性突触传入编码，我们证明当循环抑制显式影响赫布可塑性时，误差调制学习会自然地在回路层面涌现。该学习规则同样解释了无抑制条件下实验观察到的可塑性现象，并在多个非线性可分基准测试中达到与误差反向传播（BP）相当的性能。我们的研究弥合了功能驱动与实验观察的可塑性规则之间的鸿沟，并对兴奋性可塑性的抑制性调制提出了具体预测。