Abstract
The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia's pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons' participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.
Highlights
The increasing availability of large scale recordings of brain networks at single neuron resolution provides an unprecedented opportunity to discover underlying principles of motor control
What strategies can we use to expose the hoped-for simplifying principles operating beneath the turbulent surface of real-world brain activity? One route is dimension reduction (Briggman et al, 2006; Cunningham and Yu, 2014; Kobak et al, 2016), which focuses on identifying the components of activity that co-vary across the members of a neural population, shifting the focus from the high dimensional recorded data to a low-dimensional representation of the population
We sought to directly test this hypothesis by identifying the simplest dynamical system that can account for high dimensional population activity
Summary
The increasing availability of large scale recordings of brain networks at single neuron resolution provides an unprecedented opportunity to discover underlying principles of motor control Such long-sought data sets are revealing a new challenge - the joint activity of large neural populations is both complex and high dimensional (Ahrens et al, 2012; Cunningham and Yu, 2014; Yuste, 2015). We sought to directly test this hypothesis by identifying the simplest dynamical system that can account for high dimensional population activity
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