Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underlying biological computation. Given that neural variance alone does not uniquely determine a latent dynamical system, interpretable architectures should prioritize accurate and low-dimensional latent dynamics. In this work, we evaluated the performance of sequential autoencoders (SAEs) in recovering three latent chaotic attractors from simulated neural datasets. We found that SAEs with widely-used recurrent neural network (RNN)-based dynamics were unable to infer accurate rates at the true latent state dimensionality, and that larger RNNs relied upon dynamical features not present in the data. On the other hand, SAEs with neural ordinary differential equation (NODE)-based dynamics inferred accurate rates at the true latent state dimensionality, while also recovering latent trajectories and fixed point structure. We attribute this finding to the fact that NODEs allow use of multi-layer perceptrons (MLPs) of arbitrary capacity to model the vector field. Decoupling the expressivity of the dynamics model from its latent dimensionality enables NODEs to learn the requisite low-D dynamics where RNN cells fail. The suboptimal interpretability of widely-used RNN-based dynamics may motivate substitution for alternative architectures, such as NODE, that enable learning of accurate dynamics in low-dimensional latent spaces.
翻译:能够从有记录的神经活动中恢复潜伏动态的人工神经网络,从有记录的神经神经活动中恢复潜伏动力的人工神经网络,可能为识别和解释生物计算所根据的动态状态提供了强有力的途径。鉴于神经差异本身并不能独特地决定潜伏动态系统,可解释的建筑应优先考虑准确和低维潜伏动态动态。在这项工作中,我们评估了从模拟神经数据集中从模拟神经元数据集中恢复三个潜伏的隐隐性随机吸引器的性能。我们发现,具有广泛使用的经常性神经网络(RNNNN)基础动态的AE公司无法在真实潜伏状态维度上推断准确率,而较大的RNNNNNN不能依靠数据中不存在的动态特征。另一方面,具有神经普通差异方程式(NODE)基础动态的SAE公司在真实潜伏状态维度上推断出准确率,同时恢复潜伏轨迹和固定点结构。我们把这一调查结果归因于以下事实,即NDEDE允许使用多层次的隐性神经网络(MLP)基于基于的动态,无法在真正的潜伏型内任意能力中推推算,而使低度的动态动态动态的动态能能能能进行广泛的解释。