树突结构可实现强大的可塑性
Dendritic structure enables powerful plasticity
AI总结：
该研究聚焦树突结构对突触可塑性的影响，提出神经元区室化可让突触计算误差信号，通过梯度下降实现局部深度学习，能学习比全局赫布可塑性更复杂的任务。
AI中文摘要：
在过去数十年间，人们日益明确：皮层神经元的复杂形态绝非进化的偶然，树突区室本身就是计算单元，而非仅作为神经细胞体之间的连接结构。尽管多数计算研究探讨了多区室模型相比点神经元的增强表征能力，但本研究聚焦于神经元形态对突触可塑性的影响。我们提出，单个神经元同时编码多种信息的能力，使突触能局部获取远超经典赫布法则的内容——不仅包含经典赫布的突触前、突触后项，还具有更高的特异性和反应速度，这是其他全局调制因子无法实现的。通过对近期树突学习模型的比较综述，我们展示了这种神经元区室化如何为突触提供计算各类误差信号的手段，而这些误差信号进而通过梯度下降实现强大的实时、完全局部的深度学习实例。在能够传播和操纵这些误差的皮层微回路中，区室化神经元最终可实现比仅靠全局调制赫布可塑性复杂得多的任务学习。
英文摘要：
Over the past decades, it has become increasingly clear that the complex morphology of cortical neurons is more than just a quirk of evolution, and that dendritic compartments serve as computational elements in their own right, rather than just providing connections between nerve cell bodies. While most computational studies discuss the enhanced representational capabilities of multi-compartment models as compared to point neurons, we focus here on the implications of neuronal morphology for synaptic plasticity. We argue that the ability of single neurons to simultaneously encode multiple pieces of information gives synapses local access to more than just the classical Hebbian pre- and postsynaptic terms, and with much greater specificity and reaction speed than permitted by other globally modulated factors. Based on a comparative review of recent dendritic learning models, we show how such neuronal compartmentalization can provide synapses with the means for calculating various forms of error signals, which in turn give rise to powerful real-time and fully local instantiations of deep learning through gradient descent. Implemented within cortical microcircuits capable of propagating and manipulating these errors, compartmentalized neurons thus ultimately enable the learning of far more complex tasks than are achievable by globally modulated Hebbian plasticity alone.
