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Inferring single-trial neural population dynamics using sequential auto-encoders.

Chethan Pandarinath1,2,3,4,5, Daniel J O'Shea6,7, Jasmine Collins8,9

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This study introduces a deep learning method for analyzing single-trial neural activity, enabling a deeper understanding of brain dynamics and behavior. The new approach improves the inference of neural population dynamics from complex spiking data.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Deep Learning

Background:

  • Simultaneous recording of thousands of neurons reveals population dynamics beyond single-neuron responses.
  • Analyzing single-trial neural data is crucial for deeper understanding but challenging due to sampling limitations and variability.

Purpose of the Study:

  • To introduce a novel deep learning method for inferring latent dynamics from single-trial neural spiking data.
  • To address the challenges of analyzing complex neural population dynamics in single trials.

Main Methods:

  • Latent factor analysis via dynamical systems (LFADS), a deep learning approach.
  • Application to macaque and human motor cortical datasets.

Main Results:

  • Accurate prediction of behavioral variables from neural data.
  • Precise estimation of single-trial neural firing rate dynamics.
  • Inference of dynamics perturbations correlating with behavioral choices.
  • Improved inference by combining data across non-overlapping recording sessions.

Conclusions:

  • LFADS enables robust inference of neural population dynamics from single-trial spiking data.
  • The method enhances understanding of neural computations underlying behavior.
  • LFADS can integrate data across extended periods and sessions for improved analysis.