Precise Estimates of Single-Trial Dynamics in Motor Cortex using Deep Learning Techniques
Abstract
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded
simultaneously. This often reveals structure in the population activity that is not apparent from single neuron
responses. However, understanding this structure on a single-trial basis is often challenging due to limited observations
of the neural population, trial-to-trial variability, and the inherent noise of action potential arrival times.
Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep-learning method to infer latent
dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential
model based on a variational auto-encoder (Kingma & Welling, 2013). By making a dynamical systems hypothesis
regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional
temporal factors, per-trial initial conditions, and inferred inputs. Here we apply LFADS to a variety of datasets
from monkey motor cortex. We show that LFADS’s estimates of neural population state are more informative
about behavioral variables than population activity itself. In addition, LFADS uncovers multiple known dynamic
features of single-trial motor cortical firing rates, including slow oscillations (1-3Hz) that accompany the transition
from pre- to peri-movement activity (Churchland et al., Nature 2012), and high-frequency oscillations (15-45
Hz) that occur during the pre-movement period (Donoghue et al., J Neurophys 1998). In cases where the neural
data’s dynamics cannot be modeled by an initial state alone (e.g., unexpected perturbations), LFADS infers
time-varying external inputs that correlate with behavioral outcomes. Finally, we apply LFADS to an unstructured
dataset (no precise timing, free-paced reaching movements, no repeated conditions) and show that it uncovers
precise state estimates and inputs from unstructured activity. These results showcase the ability of LFADS to infer
precise estimates of single-trial dynamics on multiple timescales and uncover inputs that correlate with behavioral
choices.
simultaneously. This often reveals structure in the population activity that is not apparent from single neuron
responses. However, understanding this structure on a single-trial basis is often challenging due to limited observations
of the neural population, trial-to-trial variability, and the inherent noise of action potential arrival times.
Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep-learning method to infer latent
dynamics from simultaneously recorded, single-trial, high-dimensional neural spiking data. LFADS is a sequential
model based on a variational auto-encoder (Kingma & Welling, 2013). By making a dynamical systems hypothesis
regarding the generation of the observed data, LFADS reduces observed spiking to a set of low-dimensional
temporal factors, per-trial initial conditions, and inferred inputs. Here we apply LFADS to a variety of datasets
from monkey motor cortex. We show that LFADS’s estimates of neural population state are more informative
about behavioral variables than population activity itself. In addition, LFADS uncovers multiple known dynamic
features of single-trial motor cortical firing rates, including slow oscillations (1-3Hz) that accompany the transition
from pre- to peri-movement activity (Churchland et al., Nature 2012), and high-frequency oscillations (15-45
Hz) that occur during the pre-movement period (Donoghue et al., J Neurophys 1998). In cases where the neural
data’s dynamics cannot be modeled by an initial state alone (e.g., unexpected perturbations), LFADS infers
time-varying external inputs that correlate with behavioral outcomes. Finally, we apply LFADS to an unstructured
dataset (no precise timing, free-paced reaching movements, no repeated conditions) and show that it uncovers
precise state estimates and inputs from unstructured activity. These results showcase the ability of LFADS to infer
precise estimates of single-trial dynamics on multiple timescales and uncover inputs that correlate with behavioral
choices.
Research Areas
