The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to characterize the neural activity from the cortex of a mouse during natural sleep, captured through local field potential measurements. Our approach relies on using a discretized Wilson--Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the stimulus received by the cortex from other brain regions, while simultaneously inferring a state that aligns with the observed signal.

翻译：在不同状态（如决策、睡眠和运动）下研究皮层动力学是神经科学的重要课题。建模工作旨在将皮层记录中出现的神经节律与促成其产生的潜在动力学联系起来。我们提出了一种方法，用于表征自然睡眠期间小鼠皮层神经活动（通过局域场电位测量获取）。该方法基于离散化的Wilson-Cowan Amari神经场模型描述神经活动，并结合数据同化方法实现状态与参数的贝叶斯联合估计。我们首先在合成测量数据上验证了该方法的可行性，随后将其应用于文献中已有的数据集。研究结果表明，该方法在推断与观测信号一致的状态的同时，具有表征皮层接收自其他脑区刺激的潜力。