通过Fisher信息量化神经潜轨迹中的信噪比
Quantifying Signal-to-Noise Ratio in Neural Latent Trajectories via Fisher Information
AI总结：
该研究针对神经群体锋电位序列的低维潜轨迹信噪比量化问题，利用Fisher信息推导其实用上界，验证了时间规律推断方法的SNR优势，为神经建模、响应模拟与实验设计提供参考。
摘要：
从大规模神经元群体记录的锋电位序列信号通常呈现低维时空结构，且被建模为条件泊松观测。捕捉大脑内部状态的低维信号可用于构建脑机接口及理解有意义行为背后的神经计算机制。我们利用Fisher信息推导出推断出的神经潜轨迹信噪比（SNR）的实用上界，证明该SNR界与过离散因子及每个神经元的Fisher信息成正比。进一步的数值实验表明，利用时间规律性的推断方法能获得与该界成正比的更高SNR。我们的结果为模型拟合数据、模拟神经响应及实验设计提供了见解。
英文摘要：
Spike train signals recorded from a large population of neurons often exhibit low-dimensional spatio-temporal structure and modeled as conditional Poisson observations. The low-dimensional signals that capture internal brain states are useful for building brain machine interfaces and understanding the neural computation underlying meaningful behavior. We derive a practical upper bound to the signal-to-noise ratio (SNR) of inferred neural latent trajectories using Fisher information. We show that the SNR bound is proportional to the overdispersion factor and the Fisher information per neuron. Further numerical experiments show that inference methods that exploit the temporal regularities can achieve higher SNRs that are proportional to the bound. Our results provide insights for fitting models to data, simulating neural responses, and design of experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
