New neural activity patterns emerge with long-term learning

Abstract

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning. We demonstrate that these new neural activity patterns cause the new behavior. Thus, the formation of new patterns of neural population activity can underlie the learning of new skills.

Keywords: brain–computer interface; motor cortex; neural population; skill learning.

Fig. 1.

Using a brain–computer interface to study learning. (A) Schematic of the BCI system. Monkeys controlled a BCI cursor (yellow) to acquire one (cyan) of eight possible (gray) targets by modulating their neural activity. In our BCI system, ∼90D neural activity is first mapped into the 10D intrinsic manifold, and then to 2D cursor velocity. (B) A simplified, conceptual schematic of neural activity patterns (dots). The neural activity patterns tend to lie in a low-dimensional subspace, termed the intrinsic manifold (gray plane). Monkeys move the BCI cursor by volitionally modulating their neural activity. At the beginning of each experiment, cursor velocities were determined by an intuitive BCI mapping (black arrow). Here, patterns colored green would move the cursor to the right and purple patterns would move the cursor to the left. To induce learning, we changed the mapping to an OMP mapping (blue arrow). Under the perturbed mapping, neural activity patterns map to different cursor velocities than under the intuitive mapping. This encourages the monkeys to learn. (C) Cursor trajectories for successful trials during a representative multiday OMP experiment (i.e., OMP 1; the multiday OMP experiment beginning on June 17, 2016). “Intuitive” trajectories show 40 consecutive trials with the intuitive mapping as an example of proficient cursor control. The second column shows the first 40 trials after switching to the OMP mapping. Performance is impaired. The third column shows the best 40 consecutive trials on day 1. The fourth column shows the best 40 consecutive trials after 6 d of practice. (D) Quantifying the amount of learning for single-day OMP (blue), multiday OMP, incremental training (green), and single-day WMP (red) experiments. An amount of learning of 1 indicates complete learning, and a value of 0 indicates no learning. Vertical lines indicate the mean of each distribution. (E) Learning curves for the six experiments with the greatest amount of learning. The example in C is highlighted in black.

Fig. 2.

New neural activity patterns emerge with long-term BCI learning. (A–C) A schematic of the technique used to identify outside-repertoire activity patterns. The OMP maps ∼90D neural population activity patterns to 2D cursor velocities. Here we illustrate using 3D neural activity patterns and a 2D OMP mapping. (A) The neural activity patterns (orange dots) generated by the animal while using the intuitive mapping define an intuitive neural repertoire (dark gray ellipsoid). Each neural activity pattern maps to a cursor velocity (orange X, as one example) through the OMP mapping (blue plane). (B, Left) The velocities predicted from the intuitive neural repertoire (orange Xs) through the OMP mapping define a speed limit (dashed gray ellipse). (Right) After learning, cursor velocities are observed that exceed the speed limit (three Xs outside of gray ellipse). (C) If the monkey produces cursor velocities that exceed the speed limit, those velocities were generated by neural activity patterns that lie outside of the intuitive neural repertoire and thus are new. (D–F) Using the speed limit to detect new neural activity patterns for an example experiment (OMP 1 from Fig. 1). (D) Velocities generated from the intuitive neural repertoire mapped through the OMP mapping. Each dot is the velocity resulting from one neural activity pattern (45-ms bin). Dots are colored by instructed target location (Inset). The speed limit is defined as the 95% convex hull (gray dashed line). By definition, 5% of the neural activity patterns are outside of the speed limit. (E) Day 1 velocities, generated while using the OMP mapping, mostly fell within the speed limit. Dots shown are from the 40 consecutive trials when behavior was the best on day 1. On these trials, 6% of the neural activity patterns were outside of the speed limit. (F) On day 8, some velocities exceeded the speed limit (e.g., patterns corresponding to purple, blue, and teal targets). Same conventions as in E. (G) The percentage of neural activity patterns that were new on the last day of OMP learning exceeds the percentage seen on the first day of learning for most experiments. Each symbol is one multiday OMP learning experiment. OMP1 is indicated in black.

Fig. 3.

New neural activity patterns drive behavioral improvements. (A) Illustration of the progress metric. Progress is defined as the component of cursor velocity in the cursor-to-target direction. The + represents the center of the screen. Gray circles are cursor positions at previous time points. (B) Mean progress toward each target on day 1 (thin) and day 8 (thick) for an example experiment (OMP 1). (C) The cursor movements showed more progress when there was a larger percentage of neural activity patterns that were new (Pearson correlation coefficient r = 0.76, P = 6 × 10⁻¹⁶), and generally increased with several days of practice. Each symbol is the mean progress averaged over all eight targets on 1 d of one multiday OMP experiment. The shading of the symbols indicates the day within a given multiday experiment. In general, earlier days showed relatively few new patterns, and later days showed more new patterns and better progress.

Fig. 4.

Monkeys can produce new neural activity patterns outside of the intrinsic manifold. There are two types of new neural activity patterns: (A) those that are outside the repertoire, but remain within the manifold, and (B) those that are outside the manifold. Either type can yield performance improvements. (C) Animals learn using both inside-manifold and outside-manifold strategies for a given OMP mapping. Each bar shows one target from one multiday OMP experiment. The overall learning, defined as change in progress from day 1 to the last day, is represented by the green dot. Targets are ordered based on the amount of learning. The inside-manifold contributions to that learning are shown in red. The outside-manifold contributions are shown in blue. For visual clarity, data presented here show only targets with behavioral improvement and only the helpful contributions are shown. See SI Appendix, Fig. S6 for a full presentation of these data.