Department of Computer Science, Saarland University, Saarbrücken, Germany.
Institute for Ophthalmic Research and Centre for Integrative Neuroscience (CIN), Tübingen University, Tübingen, Germany.
PLoS Comput Biol. 2024 Jul 31;20(7):e1012354. doi: 10.1371/journal.pcbi.1012354. eCollection 2024 Jul.
Neural population responses in sensory systems are driven by external physical stimuli. This stimulus-response relationship is typically characterized by receptive fields, which have been estimated by neural system identification approaches. Such models usually require a large amount of training data, yet, the recording time for animal experiments is limited, giving rise to epistemic uncertainty for the learned neural transfer functions. While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as most exciting inputs (MEIs), from in silico experiments. Here, we present a Bayesian system identification approach to predict neural responses to visual stimuli, and explore whether explicitly modeling network weight variability can be beneficial for identifying neural response properties. To this end, we use variational inference to estimate the posterior distribution of each model weight given the training data. Tests with different neural datasets demonstrate that this method can achieve higher or comparable performance on neural prediction, with a much higher data efficiency compared to Monte Carlo dropout methods and traditional models using point estimates of the model parameters. At the same time, our variational method provides us with an effectively infinite ensemble, avoiding the idiosyncrasy of any single model, to generate MEIs. This allows us to estimate the uncertainty of stimulus-response function, which we have found to be negatively correlated with the predictive performance at model level and may serve to evaluate models. Furthermore, our approach enables us to identify response properties with credible intervals and to determine whether the inferred features are meaningful by performing statistical tests on MEIs. Finally, in silico experiments show that our model generates stimuli driving neuronal activity significantly better than traditional models in the limited-data regime.
感觉系统中的神经群体响应是由外部物理刺激驱动的。这种刺激-反应关系通常以感受野为特征，感受野是通过神经系统识别方法来估计的。这些模型通常需要大量的训练数据，然而，动物实验的记录时间是有限的，这导致了所学习的神经传递函数的认知不确定性。虽然深度神经网络模型在神经预测方面表现出了优异的能力，但它们通常不提供从计算机实验中得出的神经表示和衍生统计量（如最令人兴奋的输入（MEIs））的不确定性。在这里，我们提出了一种贝叶斯系统识别方法来预测视觉刺激的神经反应，并探讨了明确建模网络权重变异性是否有助于识别神经反应特性。为此，我们使用变分推理来估计给定训练数据的每个模型权重的后验分布。使用不同的神经数据集进行的测试表明，与蒙特卡罗随机失活方法和使用模型参数点估计的传统模型相比，该方法在神经预测方面可以实现更高或相当的性能，同时具有更高的数据效率。同时，我们的变分方法为我们提供了一个有效的无限集合，避免了任何单个模型的特殊性，从而生成 MEIs。这使我们能够估计刺激-反应函数的不确定性，我们发现该不确定性与模型层面的预测性能呈负相关，并且可能有助于评估模型。此外，我们的方法使我们能够通过对 MEIs 进行统计检验来确定推断特征是否有意义，并确定响应特征是否具有可信度区间。最后，在计算机实验中，我们的模型在有限数据条件下生成的刺激比传统模型更能有效地驱动神经元活动。
