论文标题
随机复发性神经元网络的大偏差方法：参数推断和波动引起的过渡
Large Deviations Approach to Random Recurrent Neuronal Networks: Parameter Inference and Fluctuation-Induced Transitions
论文作者
论文摘要
我们在这里将具有较大偏差理论的神经元网络的现场理论方法统一。对于具有连续值单元的典型随机复发网络模型，我们表明该有效作用与速率函数相同，并使用场理论得出后者。此速率函数采用了kullback-leibler差异的形式，该差异可以实现模型参数的数据驱动推断，并计算均值场理论之外的波动。最后，我们暴露了一个平均场解决方案之间波动诱导的过渡的制度。
We here unify the field theoretical approach to neuronal networks with large deviations theory. For a prototypical random recurrent network model with continuous-valued units, we show that the effective action is identical to the rate function and derive the latter using field theory. This rate function takes the form of a Kullback-Leibler divergence which enables data-driven inference of model parameters and calculation of fluctuations beyond mean-field theory. Lastly, we expose a regime with fluctuation-induced transitions between mean-field solutions.
