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tags: 論文分析
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# 論文分析：Inferring single-trial neural population dynamics using sequential auto-encoders

# 摘要：
Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

# 想解決的問題
這篇論文的主要目的是希望能夠透過新的深度學習方法，從單次神經射頻數據中推斷出潛在的神經元動態，以便更深入地了解神經元群體動態。此方法可以準確預測觀察到的行為變量、提取精確的神經動態單次射頻估計、推斷與行為選擇相關的動態干擾，並結合跨越數月的非重疊記錄會話的數據，以提高對潛在動態的推斷。

# 使用的方法
這篇論文使用了一種名為"latent factor analysis via dynamical systems"的深度學習方法，以從單次神經射頻數據中推斷出潛在的神經元動態。這種方法通過將單次神經射頻數據轉換為潛在因子空間，並使用動態系統模型來描述因子之間的動態關係，從而推斷出神經元群體的動態。該方法可以準確預測觀察到的行為變量，提取精確的神經動態單次射頻估計，並推斷與行為選擇相關的動態干擾。此外，該方法還可以結合跨越數月的非重疊記錄會話的數據，以提高對潛在動態的推斷。這種方法的優勢在於它可以從單次神經射頻數據中推斷出神經元群體動態，這對於更深入地了解神經元群體動態和行為之間的關係具有重要意義。

# 最後的成果
這篇論文的最終成果是應用深度學習方法"latent factor analysis via dynamical systems"，從單次神經射頻數據中推斷出潛在的神經元動態。這種方法可以準確預測觀察到的行為變量、提取精確的神經動態單次射頻估計、推斷與行為選擇相關的動態干擾，並結合跨越數月的非重疊記錄會話的數據，以提高對潛在動態的推斷。這些成果有助於更深入地了解神經元群體動態。

# 關鍵字
"Neuroscience"、"population dynamics"、"latent factor analysis"、"dynamical systems"、"single-trial neural spiking data"。