论文标题
基于电导的树突执行可靠性加权意见集合
Conductance-based dendrites perform reliability-weighted opinion pooling
论文作者
论文摘要
提示整合，即不同信息来源以减少不确定性的组合，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室神经元的动力学自然实现了所需的概率计算。相关的错误驱动可塑性规则使神经元可以从数据样本中学习不同途径的相对可靠性，从而在多感官集成任务中近似于贝叶斯最佳观察者。此外，该模型从多感官整合实验中提供了神经记录的功能解释，并为膜的潜在和电导动力学做出了特定的预测。
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.
