一种新的训练有噪交互神经元浅层和深层广义线性模型的推理方法
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
摘要：
广义线性模型是预测神经元网络响应外部刺激的相关随机活动的最有效的范例之一，在许多大脑领域都有应用。然而，当处理复杂的刺激，推断的耦合参数往往不概括在不同的刺激统计，导致性能下降和爆破不稳定性。在这里，我们开发了一个两步推理策略，使我们能够训练强大的广义线性模型的相互作用的神经元，明确分离的影响，在刺激的相关性，从网络的相互作用，在每个训练步骤。将这种方法应用于视网膜神经节细胞对复杂视觉刺激的反应，我们表明，与经典方法相比，以这种方式训练的模型表现出更好的性能，更稳定，产生强大的交互网络，并在复杂的视觉统计中推广良好。该方法可以扩展到深度卷积神经网络，从而产生对神经元放电率及其相关性具有高预测准确性的模型。
英文摘要：
Generalized linear models are one of the most efficient paradigms for predicting the correlated stochastic activity of neuronal networks in response to external stimuli, with applications in many brain areas. However, when dealing with complex stimuli, the inferred coupling parameters often do not generalize across different stimulus statistics, leading to degraded performance and blowup instabilities. Here, we develop a two-step inference strategy that allows us to train robust generalized linear models of interacting neurons, by explicitly separating the effects of correlations in the stimulus from network interactions in each training step. Applying this approach to the responses of retinal ganglion cells to complex visual stimuli, we show that, compared to classical methods, the models trained in this way exhibit improved performance, are more stable, yield robust interaction networks, and generalize well across complex visual statistics. The method can be extended to deep convolutional neural networks, leading to models with high predictive accuracy for both the neuron firing rates and their correlations.
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