论文标题
自适应突触衰竭可以从大脑的后验预测分布中取样
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain
论文作者
论文摘要
贝叶斯对神经加工的解释要求生物学机制根据贝叶斯定理代表并根据概率分布进行操作。许多人推测，突触失败构成了变异的机理，即大脑中的贝叶斯推断。尽管模型先前已经使用突触失败来对模型参数中的不确定性进行取样，但我们证明，通过将传输概率调整到学习的网络权重，突触失败不仅可以过度过度模型不确定性，而且可以完成后验预测分布。我们的结果可能解释了大脑执行概率搜索和近似复杂积分的能力。这些操作参与了许多计算，包括对复杂计划的似然评估和状态价值估计。
Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem. Many have speculated that synaptic failure constitutes a mechanism of variational, i.e., approximate, Bayesian inference in the brain. Whereas models have previously used synaptic failure to sample over uncertainty in model parameters, we demonstrate that by adapting transmission probabilities to learned network weights, synaptic failure can sample not only over model uncertainty, but complete posterior predictive distributions as well. Our results potentially explain the brain's ability to perform probabilistic searches and to approximate complex integrals. These operations are involved in numerous calculations, including likelihood evaluation and state value estimation for complex planning.
