AI 中文总结
研究基于电导的树突网络，探讨受限体细胞反馈何时能近似特定隔室反向传播误差，通过精确梯度分解将局部学习转化为信用信号压缩问题，验证分流抑制对学习的作用机制。
AI 中文摘要
生物神经元在分支树突上分配信用，突触驱动等相互作用塑造突触可塑性。研究具有E/I突触库等的基于电导的树突网络，考察受限体细胞反馈情况。精确梯度分解为局部资格x隔室误差项，测试分流抑制在特定约束下对学习的益处，多种诊断支持该机制，结果表明反馈场保真度仍是主要瓶颈。
英文摘要
Biological neurons assign credit across branching dendrites, where synaptic drive, conductance, local voltage, and somatic teaching signals interact to shape plasticity. We study conductance-based dendritic networks with excitatory and inhibitory synapses, shunting inhibition, and tree-structured branch-to-soma coupling, asking when restricted somatic feedback can approximate compartment-specific backpropagated errors. Exact gradients factor into a synapse-local eligibility term, set by presynaptic activity, driving force, and input resistance, and a path-specific compartment error obtained by transporting a somatic error through dendritic gains. This turns local learning into a credit-signal approximation problem. We test whether shunting improves learning when its effect on dendritic gain makes compartment errors more compatible with restricted feedback. Exact-gradient reconstruction verifies the factorization, while path-gain, feedback-fidelity, inhibition-intervention, and transported-error controls probe the mechanism and its limits. With nonnegative conductances and a five-factor rule using matched-width feedback with scalar fallback, shunting LocalCA remains 5 to 6 percentage points below matched backpropagation on MNIST, Fashion-MNIST, and figure-ground MNIST, showing that feedback fidelity remains a major bottleneck. A three-factor rule approaches matched backpropagation with exact transported feedback in the shunting model and with neuron-wise feedback in both architectures, but shunting has no general advantage under matched initialization. These results show how conductance and dendritic branching enter the exact credit equation and identify restricted feedback as a principal limit.
