基于电导的树突执行可靠性加权意见池
Conductance-based dendrites perform reliability-weighted opinion pooling
摘要：
线索整合，即结合不同来源的信息以减少不确定性，是大脑功能的基本计算原理。从规范模型开始，我们表明具有基于电导的树突的多隔室神经元的动力学自然地实现了所需的概率计算。相关的错误驱动的可塑性规则允许神经元从数据样本中学习不同通路的相对可靠性，在多感觉整合任务中近似贝叶斯最优观察者。此外，该模型提供了一个功能的解释，从多感觉整合实验的神经记录，并作出具体的预测个别神经元的膜电位和电导动力学。
英文摘要：
Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative model we show that the dynamics of multi-compartment neurons with conductance-based dendrites naturally implement the required probabilistic computations. The associated error-driven plasticity rule allows neurons to learn the relative reliability of different pathways from data samples, approximating Bayes-optimal observers in multisensory integration tasks. Additionally, the model provides a functional interpretation of neural recordings from multisensory integration experiments and makes specific predictions for membrane potential and conductance dynamics of individual neurons.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
