Learning leaves a memory trace in motor cortex

Abstract

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex. This memory trace coexisted with proficient performance under the familiar map, primarily by altering neural activity in dimensions that did not impact behavior. Forming memory traces might be how the brain is able to provide for the joint learning of multiple behaviors without interference.

Keywords: brain-computer interfaces; dimensionality reduction; learning; memory; motor control; neural populations.

Figure 1.. How learning could leave a memory trace in neural population activity

(A) Schematic of how neural activity (colored dots) may change when performing different tasks. Performing the familiar task for the first time (light red; familiar task 1), then the new task (blue), then the familiar task again (dark red; familiar task 2) may each yield distinct population activity patterns. (B) There are many different population activity patterns that can be appropriate for the same task (red oval for the familiar task, blue oval for the new task). We consider the possibility that the activity patterns when reperforming the familiar task after learning the new task are appropriate for both the familiar task and the new task (purple intersection). We refer to this as a memory trace. (C) The activity of ~90 neural units recorded in the primary motor cortex (M1) was translated into movements of a computer cursor using a BCI. A BCI directly relates neural activity to behavior (the horizontal and vertical velocities of the cursor) using a map specified by the experimenters. Specifically, only the 10 dimensions of greatest shared variance in the neural population dictate how the cursor moves (see STAR Methods). (D) Target acquisition times for a representative session (N20160714). The monkey first used the familiar map to control the cursor proficiently (light red, familiar task 1). We then switched to the new map, which the monkey had not used before, and target acquisition times abruptly increased (dark blue, new task). The monkey learned to use the new map through trial and error (dark blue, decreasing target acquisition times). Finally, the familiar map was reinstated (dark red, familiar task 2). We focus on this second familiar map period for identifying whether there exists a memory trace. For visualization, acquisition times were smoothed with a causal 25-trial moving window and are not shown for the first 8 trials of each task. Success rates were 100% for all three tasks of this example session. See also Figure S1.

Figure 2.. Leveraging a BCI to probe the existence of a memory trace

(A) The online BCI map dictates cursor movement. The same neural activity can also be interpreted with respect to an offline BCI map that did not determine cursor movement. During familiar task 1 and familiar task 2, the online map is the familiar map. (B) Schematic of BCI maps in population activity space. The projection of neural activity onto a given BCI map (online or offline) determines how appropriate the activity is for that map. For example, many different population activity patterns (vertical dotted line) are projected to the same point and are thus equally good, for the familiar map. However, those same activity patterns are not all equally good for the new map; those near the top of the dashed line are better for the new map than those near the bottom. For illustrative purposes, we show a 2D neural space mapped to a 1D cursor velocity. In the actual experiments, the neural space was ~90D, which was mapped to a 2D cursor velocity. (C–E) We explore three possibilities for where neural activity might reside during familiar task 2. (C) Reversion hypothesis: familiar task 2 neural activity is similar to that used during familiar task 1. (D) Representational drift hypothesis: familiar task 2 neural activity is different from that used during familiar task 1, but not in a manner that influences performance through the new map in a systematic way. We show a stylized three-dimensional (3D) space, with an axis (black line) coming out of the page to illustrate how neural activity could change along a dimension orthogonal to both the familiar map and the new map. (E) Memory trace hypothesis: familiar task 2 neural activity contains a memory trace, whereby neural activity is more appropriate for the new map than it was during familiar task 1. See also Figure S2.

Figure 3.. Learning a new task changes the neural representation of a familiar task

(A) A view of the population neural activity for one example target (J20120601; target 270°) across all three task periods. We applied linear discriminant analysis (LDA) to find the plane that best separates the neural activity from the three task periods. Activity is projected onto that plane, with mean and covariances across timesteps shown. Examining the dimensions of highest shared variance provides similar results (Figure S3A). (B) Population activity was different between familiar task 1 and familiar task 2 (P < 10⁻¹⁰, two-sided paired Wilcoxon signed-rank test, n = 344 targets). Black shows the Mahalanobis distance between the familiar task 1 and familiar task 2 population activity means in the 10D latent space. This distance was computed separately for each of the eight targets in the experiment, aggregated over all 43 experiments. Gray indicates the prediction of the reversion hypothesis, obtained using a shuffle control (see STAR Methods). See also Figure S3.

Figure 4.. Learning leaves a memory trace

(A) During familiar task 1 and familiar task 2, neural activity drives the cursor through the familiar map (red trajectories, with dots denoting cursor positions at each timestep). The same neural activity can also be projected through the new map in an offline analysis (blue arrows indicate cursor velocity at each time step). Both trials come from the 225⁺ target from session N20160329. For visualization purposes, the target directions were rotated to orient at 0°. (B) Average progress through the online familiar map for each trial to the target shown in (A). Task performance, measured by progress (see STAR Methods), was not different between familiar task 1 and familiar task 2 (P = 0:43, two-sided unpaired Wilcoxon rank-sum test). Dots on the horizontal axis denote the average progress for the trials shown in (A). Triangles above the histograms denote the mean of each distribution. (C) Average progress through the offline new map for each trial to the target shown in (A). The difference in average progress defines the memory trace for that target. For this target, there was higher progress through the new map during familiar task 2 than there was during familiar task 1, yielding a memory trace of 14.49 mm/s (P = 0:0077, two-sided unpaired Wilcoxon rank-sum test). (D) The memory trace for each of the eight targets per session, aggregated across all sessions and all three monkeys. On average, the memory trace was positive (P = 2:53 × 10⁻⁸, n = 344 targets, two-sided paired Wilcoxon signed-rank test). Note that the memory trace can be negative, which indicates that progress through the new map is worse during familiar task 2 than familiar task 1. (E) The average memory trace per session was positive (P = 4:25 × 10⁻⁵, n = 43 sessions, two-sided paired Wilcoxon signed-rank test). (F) The size of the memory trace was correlated with the size of learning (monkey J, R² = 0:25;P < 10⁻¹⁰, one-sided F test, n = 176 targets; monkey N, R² = 0:29;P = 1:23 × 10⁻⁸;n = 96; monkey L, R² = 0:13;P = 0:0017;n = 72; see Figure S4E for same plot colored by experimental session). We then considered the marginal distribution of each quantity. The amount of learning was positive for all three monkeys (top marginal histograms; monkey J, P < 10⁻¹⁰, two-sided paired Wilcoxon sign-rank test, n = 176 targets; monkey N, P < 10⁻¹⁰, n = 96; P = 3:21 × 10⁻⁹, n = 72 targets). The memory trace per target was positive for monkeys J and N, but not significantly different from zero for monkey L (right marginal histograms; monkey J, P = 1:57 × 10⁻⁴, two-sided paired Wilcoxon sign-rank test, n = 176 targets; monkey N, P = 1:99 × 10⁻⁶, n = 96; monkey L, P = 0:61, n = 72). See also Figure S4.

Figure 5.. The memory trace persists over time and coexists alongside proficient task performance

(A) To study the persistence of the memory trace, we considered the end of familiar task 2 (gold bar) for the sessions that had the most trials of familiar task 2. Zero relative acquisition time represents the average acquisition time for that target during familiar task 1. (B) The memory trace persisted, remaining positive after extended exposure to familiar task 2 (P = 9:65 × 10⁻⁸, two-sided paired Wilcoxon sign-rank test). (C) Behavioral performance during familiar task 2 from two example sessions, one session with behavior as good or better than during familiar task 1 (faster acquisition time; green) and the other with worse behavior than during familiar task 1 (slower acquisition times; black). (D) To evaluate the influence that behavioral performance during familiar task 2 has on the size of the memory trace, we split targets into two groups. The first group contained targets where the mean target acquisition time during familiar task 2 was less than the mean target acquisition time during familiar task 1 (better behavior, green). The second group contained targets where the mean target acquisition time during familiar task 2 was greater than during familiar task 1 (worse behavior, black). The size of the memory trace was larger for the better behavior group than the worse behavior group (P = 0:0025, two-sided unpaired Wilcoxon sign-rank test). Considered separately, the memory trace was positive for each group of sessions (better behavior, P = 8:08 × 10⁻⁸, two-sided paired Wilcoxon rank-sum test; worse behavior, P = 5:73 × 10⁻⁵). See also Figure S5.

Figure 6.. The memory trace is predominantly in the null space of the familiar map

(A) Memory trace depicted in same space as Figures 2C–2E. (B and C) During familiar task 1 (light red dot) and familiar task 2 (dark red dot), the cursor is controlled using the familiar map (gray arrow). Familiar task 2 activity is further along the new map (blue arrow) than familiar task 1 activity, indicating higher progress along the new map. The memory trace is defined as difference in the projection onto the new map. (B) The change in neural activity from (A) can be decomposed into a component that is output-potent to the familiar map (Δ potent) and a component that is output-null to the familiar map (Δ null). (C) We can correspondingly decompose the memory trace into output-potent and output-null components. (D) Of the targets with a positive memory trace (218 out of 344 targets), the memory trace consistently resided in dimensions null to the familiar map (P < 10⁻¹⁰, two-sided paired Wilcoxon signed-rank test, n = 218 targets across all monkeys). (E) The contributions from the potent space are not significantly different from zero (P = 0:17, two-sided paired Wilcoxon signed-rank test, n = 218 targets across all monkeys). See also Figure S6.

Figure 7.. The path of washout does not retrace the path of learning

(A and B) We consider two possibilities for how the memory trace arises during familiar task 2. (A) The first possibility is that the path of washout (i.e., from the end of the new task to familiar task 2) retraces the path of learning (i.e., from familiar task 1 to the end of the new task). (B) The second possibility is that these two paths are distinct, implying that the washout is not simply “unlearning.” (C) To distinguish between these two possibilities, we measured the angle between these two paths in the 10-dimensional latent space of neural activity. This angle (black histogram) was smaller than the angles that would be obtained under possibility 1 (gray histogram; see STAR Methods; P < 10⁻¹⁰, two-sided paired Wilcoxon signed-rank test, n = 344 targets across monkeys). This implies the paths of learning and washout are distinct (possibility 2). The targets that exhibited near 180° angles between the learning and washout paths did not all come from the same sessions, meaning there was no single session in which learning was undone through the process of unwinding. Among the 14 targets where the difference between the paths exceeded 170°, the maximum number from a single session was 3 (consistent with selecting 14 of the targets at random, P = 0:16).