Inferring single-trial neural population dynamics using sequential auto-encoders,Nature Methods

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Inferring single-trial neural population dynamics using sequential auto-encoders
Nature Methods ( IF 48.0 ) Pub Date : 2018-09-17 , DOI: 10.1038/s41592-018-0109-9
Chethan Pandarinath _{1,

2,

3,

4,

5} , Daniel J O'Shea _{4,

6} , Jasmine Collins _{7,

8} , Rafal Jozefowicz _{7,

9} , Sergey D Stavisky _{3,

4,

5,

6} , Jonathan C Kao _{4,

10} , Eric M Trautmann ₆ , Matthew T Kaufman _{6,

11} , Stephen I Ryu _{4,

12} , Leigh R Hochberg _{13,

14,

15} , Jaimie M Henderson _{3,

5} , Krishna V Shenoy _{4,

5,

16,

17,

18,

19} , L F Abbott _{20,

21,

22} , David Sussillo _{4,

5,

7}

Affiliation

Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
Department of Neurosurgery, Emory University, Atlanta, GA, USA.
Department of Neurosurgery, Stanford University, Stanford, CA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Stanford Neurosciences Institute, Stanford University, Stanford, CA, USA.
Neurosciences Graduate Program, Stanford University, Stanford, CA, USA.
Google AI, Google Inc., Mountain View, CA, USA.
University of California, Berkeley, Berkeley, CA, USA.
OpenAI, San Francisco, CA, USA.
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, CA, USA.
VA RR&D Center for Neurorestoration and Neurotechnology, Veterans Affairs Medical Center, Providence, RI, USA.
Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA.
Department of Neurobiology, Stanford University, Stanford, CA, USA.
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Bio-X Program, Stanford University, Stanford, CA, USA.
Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
Department of Neuroscience, Columbia University, New York, NY, USA.
Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA.

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.

中文翻译：

使用顺序自动编码器推断单次试验神经种群动态

神经科学正在经历一场革命，同时记录数千个神经元正在揭示单神经元反应不明显的群体动态。这种结构通常是从许多试验的平均数据中提取的，但更深入的理解需要研究在单个试验中检测到的现象，由于神经群体的不完整抽样、试验间的变异性和动作电位时间的波动，这具有挑战性。我们通过动态系统引入潜在因素分析，这是一种从单次试验神经脉冲数据中推断潜在动态的深度学习方法。当应用于各种猕猴和人类运动皮层数据集时，通过动态系统进行的潜在因素分析可以准确地预测观察到的行为变量，

更新日期：2018-12-10

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