Kim Timothy Doyeon, Luo Thomas Zhihao, Can Tankut, Krishnamurthy Kamesh, Pillow Jonathan W, Brody Carlos D
Princeton Neuroscience Institute, Princeton University, Princeton, NJ.
School of Natural Sciences, Institute for Advanced Study, Princeton, NJ.
bioRxiv. 2023 Nov 16:2023.11.14.567136. doi: 10.1101/2023.11.14.567136.
Computations involved in processes such as decision-making, working memory, and motor control are thought to emerge from the dynamics governing the collective activity of neurons in large populations. But the estimation of these dynamics remains a significant challenge. Here we introduce Flow-field Inference from Neural Data using deep Recurrent networks (FINDR), an unsupervised deep learning method that can infer low-dimensional nonlinear stochastic dynamics underlying neural population activity. Using population spike train data from frontal brain regions of rats performing an auditory decision-making task, we demonstrate that FINDR outperforms existing methods in capturing the heterogeneous responses of individual neurons. We further show that FINDR can discover interpretable low-dimensional dynamics when it is trained to disentangle task-relevant and irrelevant components of the neural population activity. Importantly, the low-dimensional nature of the learned dynamics allows for explicit visualization of flow fields and attractor structures. We suggest FINDR as a powerful method for revealing the low-dimensional task-relevant dynamics of neural populations and their associated computations.
诸如决策、工作记忆和运动控制等过程中所涉及的计算，被认为源自支配大量神经元群体集体活动的动力学。但对这些动力学的估计仍然是一项重大挑战。在此，我们引入了使用深度循环网络从神经数据中进行流场推断（FINDR），这是一种无监督深度学习方法，能够推断神经群体活动背后的低维非线性随机动力学。利用执行听觉决策任务的大鼠额叶脑区的群体尖峰序列数据，我们证明FINDR在捕捉单个神经元的异质性反应方面优于现有方法。我们进一步表明，当训练FINDR来分离神经群体活动中与任务相关和无关的成分时，它能够发现可解释的低维动力学。重要的是，所学习到的动力学的低维特性允许对流场和吸引子结构进行明确的可视化。我们建议将FINDR作为一种强大的方法，用于揭示神经群体的低维任务相关动力学及其相关计算。
