Constraints on neural redundancy
Abstract
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a braincomputer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and taskrelevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.
https://doi.org/10.7554/eLife.36774.001eLife digest
When you swing a tennis racket, muscles in your arm contract in a specific sequence. For this to happen, millions of neurons in your brain and spinal cord must fire to make those muscles contract. If you swing the racket a second time, the same muscles in your arm will contract again. But the firing pattern of the underlying neurons will probably be different. This phenomenon, in which different patterns of neural activity generate the same outcome, is called neural redundancy.
Neural redundancy allows a set of neurons to perform multiple tasks at once. For example, the same neurons may drive an arm movement while simultaneously planning the next activity. But does performing a given task constrain how often different patterns of neural activity can be produced? If so, this would limit whether other tasks could be carried out at the same time. To address this, Hennig et al. trained macaque monkeys to use a braincomputer interface (BCI). This is a device that reads out electrical brain activity and converts it into signals that can be used to control another device. The key advantage of a BCI is that the redundant activity patterns are precisely known. The monkeys learned to use their brain activity, via the BCI, to move a cursor on a computer screen in different directions.
The results revealed that monkeys could only produce a limited number of different patterns of brain activity for a given BCI cursor movement. This suggests that the ability of a group of neurons to multitask is restricted. For example, if the same set of neurons is involved in both planning and performing movements, then an animal’s ability to plan a future movement will depend on the one it is currently performing.
BCIs can help patients who have suffered stroke or paralysis. They enable patients to use their brain activity to control a computer or even robotic limbs. Understanding how the brain controls BCIs will help us improve their performance and deepen our knowledge of how the brain plans and performs movements. This might include designing BCIs that allow users to multitask more effectively.
https://doi.org/10.7554/eLife.36774.002Introduction
Neural circuits relay information from one population of neurons to another. This relay involves successive stages of downstream neurons reading out the activity of upstream neurons. In many cases, the same activity in the downstream population can be produced by different population activity patterns in the upstream population, a phenomenon termed neural redundancy. Redundancy is ubiquitous in neural computation, from sensory input to motor output. For example, during a task where subjects need to discriminate the color of a stimulus while ignoring its orientation (Mante et al., 2013), population activity patterns corresponding to the same color but different orientations are read out equivalently, and are therefore redundant. There is mounting evidence that redundancy in readouts may provide various computational benefits. For example, neural redundancy may allow us to prepare movements without executing them (Kaufman et al., 2014; Elsayed et al., 2016), enable stable computation despite unstable neural dynamics (Driscoll et al., 2017; Druckmann and Chklovskii, 2012; Murray et al., 2017) and allow the central nervous system to filter out unwanted noise (MorenoBote et al., 2014).
To fully utilize the proposed benefits of neural redundancy, the population activity should be allowed to freely vary, as long as the readout of this activity remains consistent with task demands. This would allow the population activity to perform computations that are not reflected in the readout. However, a commonly held assumption is that neural activity might also be constrained by energetics: All things being equal, if two population activity patterns are read out equivalently, the brain should prefer the pattern that requires less energy to produce (Laughlin et al., 1998; Barlow, 1969; Levy and Baxter, 1996). These two lines of reasoning raise the following questions: What principles guide the production of redundant neural activity patterns? Are there constraints on which redundant activity patterns can be produced? If so, this may limit the extent to which neural circuits can exploit the proposed computational benefits of redundancy.
Redundancy has been studied extensively in motor control (Lashley, 1933; Bernstein, 1967), albeit in terms of muscular redundancy rather than neural redundancy. During arm movements, different combinations of muscle activity can lead to the same arm kinematics, meaning these different muscle activity patterns are redundant. Previous work on this muscle redundancy problem has identified two principles guiding the selection of redundant muscle activity. First, because muscle contraction requires energy in the form of ATP, the selected muscle activity should require minimum energy relative to the other redundant options (Thoroughman and Shadmehr, 1999; Huang et al., 2012; Fagg et al., 2002). Second, a minimal intervention strategy has been proposed in which subjects control only the aspects of muscle activity that influence the task outcome, and allow for variability in the aspects of muscle activity that do not influence the task outcome (Scholz and Schöner, 1999; Todorov and Jordan, 2002; ValeroCuevas et al., 2009). To generate movements, the brain not only needs to deal with muscle redundancy, but also neural redundancy, which has been less studied.
One way in which neural redundancy can arise is when there are more elements (neurons or muscles) upstream than downstream. During arm movements, the activity of around thirty muscles in the arm and hand is controlled by tens of thousands of neurons in the spinal cord (Gray, 1918; Feinstein et al., 1955). Those neurons are in turn influenced by millions of neurons in the primary motor cortex and other motor areas (Ettema et al., 1998; Lemon, 2008). Thus, the neural control of arm movement is redundant (Figure 1A), in that different population activity patterns can generate the same movement (Rokni et al., 2007; Ajemian et al., 2013). Can the principles of muscular redundancy inform our understanding of neural redundancy?
A common challenge in studying neural redundancy is that it is typically not known which neural activity patterns are redundant, because we do not know how downstream neurons or muscles read out information. In this study we overcome this problem by leveraging a braincomputer interface (BCI), in which the activity of dozens of neurons is read out as movements of a cursor on a computer screen (Figure 1B) (Taylor et al., 2002; Carmena et al., 2003; Hochberg et al., 2006; Ganguly and Carmena, 2009; Gilja et al., 2012; Hauschild et al., 2012; Sadtler et al., 2014). A key advantage of a BCI is that the readout of the population activity (termed the BCI mapping) is fully known and defined by the experimenter (Golub et al., 2016). This allows us to determine precisely the redundant population activity patterns, which are those that move the cursor in exactly the same way. To illustrate this, consider a simplified example where the activity of two neurons controls a 1D cursor velocity (Figure 1C). The two dark green activity patterns produce the same cursor movement ($\mathbf{v}}_{1$), and the two light green patterns produce a different movement (${v}_{2}$). We can decompose any population activity pattern into two orthogonal components: outputpotent activity and outputnull activity (Figure 1C, black axes) (Kaufman et al., 2014; Law et al., 2014). The outputpotent component determines the cursor’s movement, whereas the outputnull component has no effect on the cursor. Two population activity patterns are redundant if they have the same outputpotent activity, but different outputnull activity (e.g. the dark green square and circle on the '${v}_{1}$' dotted line in Figure 1C). The question we address here is, which redundant population activity patterns are preferred by the nervous system? To answer this, we assessed the distribution of outputnull activity produced during each cursor movement (Figure 1D), and compared it to what we would expect to observe under each of several candidate hypotheses for explaining neural redundancy.
We trained three Rhesus macaques to perform a braincomputer interface task in which they controlled the velocity of a cursor on a computer screen by volitionally modulating neural activity in primary motor cortex. To understand the principles guiding the selection of redundant neural activity, we compared the observed distributions of outputnull activity to those predicted by three different hypotheses. The first two hypotheses we considered were inspired by studies of muscle redundancy. First, by analogy to minimum energy principles (Thoroughman and Shadmehr, 1999; Huang et al., 2012; Fagg et al., 2002), neural activity may minimize unnecessary spiking (Barlow, 1969; Levy and Baxter, 1996). Second, by analogy to the minimal intervention strategy (Scholz and Schöner, 1999; Todorov and Jordan, 2002; ValeroCuevas et al., 2009), outputnull activity might be uncontrolled (i.e. outputpotent activity is modified independently of outputnull activity) because neural variability in this space has no effect on cursor movement. Third, we considered the possibility that the distribution of redundant activity may be coupled with the taskrelevant activity, so that producing particular activity patterns in outputpotent dimensions requires changing the distribution of activity in outputnull dimensions.
We tested all hypotheses in terms of their ability to predict the distribution of outputnull activity, given the outputpotent activity. Hypotheses were tested within the space in which the population activity naturally resides, termed the intrinsic manifold (Sadtler et al., 2014). The results of Sadtler et al. (2014) indicate that neural activity cannot readily leave this manifold, and more recent results demonstrate that neural activity is further constrained by a neural repertoire within the intrinsic manifold (Golub et al., 2018). However, a repertoire defines only a set of population activity patterns, and not how often different activity patterns within the repertoire are produced. Therefore, to understand the principles governing the selection among redundant population activity patterns, we focused on predicting the distribution of redundant population activity within the intrinsic manifold and neural repertoire.
We found strong evidence for the third hypothesis, that redundant activity is coupled with taskrelevant activity. This indicates that neural redundancy is resolved differently than muscular redundancy. Furthermore, the outputnull space should not be thought of as a space in which neural activity can freely vary to carry out computations without regard to the outputpotent activity. Instead, the distribution of outputnull activity is constrained by the corresponding outputpotent activity. If the required outputpotent activity is defined by the task demands, this can constrain how the outputnull activity can vary, and correspondingly the computations that can be carried out in the outputnull space.
Results
To study the selection of redundant neural activity, we used a BCI based on 85–94 neural units recorded using a Utah array in the primary motor cortex in each of three Rhesus macaques. Animals modulated their neural activity to move a computer cursor in a 2D centerout task (see Materials and methods; Figure 1—figure supplement 1). At the beginning of each experiment, we identified the 10 dimensions of the population activity that described the largest activity modulations shared among the neural units, termed the intrinsic manifold (Sadtler et al., 2014). A twodimensional subspace of the 10dimensional intrinsic manifold was mapped to horizontal and vertical cursor velocity and was therefore outputpotent, while the eight orthogonal dimensions were outputnull. Our goal was to predict the joint distribution of the observed neural activity in this eightdimensional outputnull space.
We tested several hypotheses for the selection of redundant neural activity using the following logic. First, we predicted the distributions of outputnull activity expected under each hypothesis. All hypotheses’ predictions were consistent with the observed behavior (i.e. the outputpotent activity), and we ensured that none of these predictions required unrealistic firing rates when combined with the outputpotent activity. Next, we compared the predicted distributions to the observed distributions of outputnull activity to determine which hypothesis provided the best match to the observed distributions. We built the observed distributions of outputnull activity as follows: At each time step during the BCI task, we assigned the recorded population activity pattern to one of eight bins corresponding to the direction of cursor movement ($0}^{\circ},{45}^{\circ},{90}^{\circ$0°, 45°, 90°, etc.) produced by that neural activity. We binned by the cursor movement because we are studying the population activity that is redundant for a given cursor movement direction. For each bin, we projected the corresponding population activity patterns onto the eight outputnull dimensions of the intrinsic manifold. The black histograms in Figure 2, Figure 3, and Figure 4 show the marginal distributions in the first three outputnull dimensions (ordered by variance accounted for). The colored histograms in Figure 2, Figure 3, and Figure 4 are the predicted outputnull distributions built under each hypothesis, which we compared to the observed distributions. The ensuing three subsections describe each hypothesis, and compare how well the corresponding predicted distributions matched the observed distributions.

Figure 2—source data 1
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Figure 3—source data 1
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Figure 4—source data 1
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During each experiment, animals controlled two different BCI mappings (i.e. the two mappings had different outputpotent subspaces). The first mapping was an ‘intuitive’ one that required no learning for proficient control. The second mapping was a withinmanifold perturbation (see Materials and methods). For the second mapping, we analyzed the trials after the behavioral performance reached asymptote. Each hypothesis predicted the distribution of outputnull activity that the animal would produce under the second mapping. To form its prediction, a hypothesis could utilize the outputpotent activity observed during the second mapping, as well as all neural activity recorded under control of the first mapping. This technique allowed us to avoid circularity in our results because we built the hypothesized distributions using the first behavioral context and evaluated those predictions in the second. Additionally, because animals learned to use the BCI mappings through trial and error, it is possible that the animals’ assumptions about the outputnull dimensions do not align perfectly with the actual outputnull dimensions of the BCI mapping. To control for this, we estimated the animal’s internal model of the BCI mapping (Golub et al., 2015). The results in the main text are based on this internal model, and we show in supplemental figures that all results still hold when using the actual BCI mapping.
Minimal firing hypotheses do not accurately predict outputnull activity
Previous work in motor control has found that subjects select muscle activations that minimize energy use, that is, subjects tend not to make movements with more stiffness or muscular cocontraction than necessary to complete the task (Thoroughman and Shadmehr, 1999; Fagg et al., 2002; Huang et al., 2012). We tested whether an analogous principle might hold true at the level of neurons (Figure 2A, Minimal Firing hypothesis). Because spiking incurs a metabolic cost (Laughlin, 2001; Laughlin et al., 1998), we first considered the hypothesis that among all the population activity patterns that produce the same cursor movement, the subject will select the one requiring the fewest spikes (Barlow, 1969; Softky and Kammen, 1991; Levy and Baxter, 1996).
To predict the distribution of outputnull activity under this hypothesis, at each time step we found the population activity pattern that would produce the observed cursor movement with the fewest spikes across all recorded neurons (see Materials and methods). This means population activity will have minimal variability in outputnull dimensions, because spiking in these dimensions does not affect cursor movement. In Figure 2A, the orange square depicts the activity pattern nearest zero spikes/s (gray square) among all activity patterns that would produce the same cursor movement (black dotted line). This would produce a delta distribution of outputnull activity, where the delta would be located at the predicted value (orange square). To make this prediction more realistic, we incorporated Poisson spiking noise. In addition, for this hypothesis and those following, we ensured that all predictions were physiologically plausible (i.e. firing rates were between zero and the maximum rates observed in the experiment; see Materials and methods).
We constructed histograms of the outputnull activity predicted by the Minimal Firing hypothesis by pooling over all time steps in which the cursor moved in a similar direction (e.g. 0°, 45°, etc.) (Figure 2B, orange). We compared these predicted distributions to the observed distributions of outputnull activity measured for that movement direction during the experiment (Figure 2B, black). Figure 2C depicts these histograms for the same session across eight different cursor directions (rows), in three of the eight outputnull dimensions (columns). For visualization, we applied principal components analysis (PCA) to display the outputnull dimensions ordered by the amount of shared variance in the outputnull activity. To assess how well the Minimal Firing hypothesis predicted the observed outputnull activity, we computed the absolute error between the predicted and observed histograms. These errors were averaged across histograms for all eight cursor directions and eight outputnull dimensions in a given session. We normalized the errors so that a perfect match between the observed and predicted histograms would result in an error of 0%, while complete mismatch between the predicted and observed histograms would yield an error of 100% (see Materials and methods). We found that the predictions of the Minimal Firing hypothesis differed from the observed activity by 73.2% $\pm $1.3% (mean $\pm $ SE) across sessions.
One possible explanation as to why these predictions were so different from the observed activity is that minimal energy principles in the brain may not equate to minimal spiking. Perhaps a more relevant constraint is not how far the activity is away from zero firing, but rather how far the activity is from a different level of activity, such as the mean firing rate for each neuron. This alternative version of a minimal energy hypothesis (Figure 2D, Minimal Deviation hypothesis) predicts that among all the population activity patterns that produce the same cursor movement, subjects select the one with the smallest deviation from some baseline population activity pattern. For each session, we identified the population activity pattern that would minimize the outputnull prediction error across cursor directions in a crossvalidated fashion (see Materials and methods) (Figure 2E). This hypothesis yielded an average histogram error of 30.9% ±1.2% (mean ± SE) across sessions. While this represents a substantial improvement over the Minimal Firing hypothesis (paired ttest of histogram errors in each session, $p<0.001$), the predicted distributions of outputnull activity still show clear discrepancies from the observed distributions (Figure 2F). Thus, we sought a hypothesis that could better predict the observed distributions of outputnull activity.
Uncontrolled hypotheses do not accurately predict outputnull activity
It has been shown that muscle activity exhibits more variability in outputnull dimensions than in outputpotent dimensions (Scholz and Schöner, 1999; Todorov and Jordan, 2002; ValeroCuevas et al., 2009). An explanation of this variability asymmetry is the ‘minimal intervention’ principle (Todorov and Jordan, 2002; ValeroCuevas et al., 2009; Diedrichsen et al., 2010), which states that while variability in outputpotent dimensions should be corrected to ensure task success, variability in outputnull dimensions can be left uncorrected because it does not lead to deficits in task performance. While this principle has been used to explain muscle activity, here we investigate whether it also explains neural activity. This hypothesis, that outputnull activity will be ‘uncontrolled’ and have high variability, is in contrast to the minimal firing hypotheses, which predict that outputnull activity will have low variability.
The idea that neural activity may be selected according to a minimal intervention principle does not, by itself, specify the form of the distribution in outputnull dimensions. We therefore considered two specific forms of uncontrolled hypotheses. First, we supposed that if all values of outputnull activity are equally likely, then outputnull activity would have a uniform distribution with bounds determined by each neuron’s physiological range (Figure 3A, Uncontrolleduniform). We emphasize that the minimal intervention principle does not specify a candidate distribution, and so we consider this particular hypothesis as a limiting case, where outputnull activity has maximum entropy within bounds on minimum and maximum activity. At each time step, we sampled the outputnull activity from a uniform distribution within ranges observed experimentally (see Materials and methods). This procedure predicts that the outputnull activity is selected independently of the current outputpotent activity, reflecting the minimal intervention principle. However, note that the extent of the uniform distribution depends on the physiological range of each neuron, and so the predicted distributions of outputnull activity vary slightly with the cursor direction (Figure 3B–C) (e.g. the length of the green bar in Figure 3A depends on the outputpotent activity). As before, for visualization we ordered the eight outputnull dimensions by the amount of shared variance explained in the recorded activity, and displayed the first three of these outputnull dimensions (Figure 3C). Because these three dimensions were rotated along the dimensions of highest variance, the predicted histograms are moundshaped rather than uniformly distributed (see Materials and methods). The predictions of the Uncontrolleduniform hypothesis differed from the observed outputnull activity by 56.6% ±1.1% (mean ± SE) across sessions.
In the second variant of this hypothesis, we considered a nonuniform distribution of outputnull activity. If the natural variability of outputnull activity is truly unmodified, then the distribution of activity observed in the same dimensions when a subject was controlling a different (previous) BCI mapping should have the same distribution under the current mapping (Figure 3D, Uncontrolledempirical). Thus, under this hypothesis we construct an empirical distribution of outputnull activity, which we form by projecting all of the population activity that the subject produced under the previous mapping onto the outputnull dimensions of the current BCI mapping (see Materials and methods). At each time step, we sampled from this empirical distribution of outputnull activity independently of the outputpotent activity, again reflecting the minimal intervention principle (Figure 3D). We checked that combining the outputnull and outputpotent activity resulted in physiologically plausible population activity (see Materials and methods). If it did not, then we resampled a different outputnull activity pattern until the combination resulted in physiologically plausible population activity. Due to this resampling, the predicted distributions of outputnull activity vary slightly with the cursor direction (Figure 3E–F). The histograms of the predictions differed from the observed data by only 23.8% ±0.8% (mean ± SE) across sessions, which is the lowest error of all hypotheses considered so far. This suggests that previously observed population activity (in this case, recorded during use of a different BCI mapping) offers greater predictive power of the selection of outputnull activity than a priori predictions such as those of the Minimal Firing, Minimal Deviation, and Uncontrolleduniform hypotheses.
Tasktransfer hypotheses accurately predict outputnull activity
Thus far, the hypothesis that best predicts the observed outputnull activity is the one that uses previously observed activity to generate its predictions (Uncontrolledempirical). This motivated us to consider more refined hypotheses that make use of this previously observed activity to generate predictions.
We first considered the hypothesis that in order to produce a desired movement, the subject selects neural activity as if he were still using the previous mapping, and corrects this activity only to ensure task success (Figure 4A, Persistent Strategy). Conceptually, when the subject wants to move the cursor in a particular direction using the current BCI mapping, he starts with the population activity patterns that he used to move the cursor in that direction under an earlier mapping (Figure 4A, light blue shading). Because this activity will not move the cursor in the same way that it did under the previous mapping, this activity is modified along the outputpotent dimensions of the current mapping (Figure 4A, red arrows), reflecting the minimal intervention principle (Todorov and Jordan, 2002; ValeroCuevas et al., 2009; Diedrichsen et al., 2010). This is similar to the Uncontrolledempirical hypothesis in that we assume activity in outputnull dimensions can be corrected independently of the activity in outputpotent dimensions. However, instead of sampling from the entire distribution of previously observed outputnull activity at each time step, here we only sample from the subset of this activity observed when subjects needed to move the cursor in the same direction as the current time step. The predictions of this hypothesis (Figure 4B–C) differed from the observed outputnull activity by 17.4% ±0.7% (mean ± SE) across sessions.
The principle of minimal intervention posits that outputnull activity can change independently from outputpotent activity. Here we examine this assumption in detail. Previous work has found that the characteristic ways in which neurons covary (i.e. the dimensions of the intrinsic manifold) persist even under different BCI mappings, perhaps owing to underlying network constraints (Sadtler et al., 2014). All hypotheses we consider here are evaluated within the intrinsic manifold, and thus respect these constraints on population variability. Because the dimensions of the intrinsic manifold capture the variability among the neurons, it is plausible that the activity along different dimensions of the intrinsic manifold can vary independently, consistent with the minimal intervention principle. By contrast, in the next hypothesis we consider the possibility that activity along different dimensions exhibit dependencies.
We considered the hypothesis that the distribution of activity in outputnull dimensions would be predictably coupled with the activity in outputpotent dimensions, even under a different BCI mapping when those dimensions were not necessarily potent and null. Under this hypothesis (Figure 4D, Fixed Distribution), given the outputpotent activity, the distribution of the corresponding outputnull activity remains the same as it was under a different BCI mapping (Figure 4D, blue frequency distribution), even if this activity was not outputnull under the other mapping. This hypothesis predicts that neural activity patterns are ‘yoked’ across dimensions, such that producing particular activity in outputpotent dimensions requires changing the distribution of activity in outputnull dimensions. The histograms of outputnull activity predicted by the Fixed Distribution hypothesis were a striking visual match to the recorded activity, and accurately predicted the dependence of these distributions on the cursor direction (Figure 4E–F). Overall, these predictions differed from the observed outputnull activity by only 13.4% ±0.5% (mean ± SE) across sessions.

Figure 5—source data 1
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The Fixed Distribution hypothesis yielded a lower histogram error than all other hypotheses across sessions from three different animals (Figure 5A). In total, the Fixed Distribution hypothesis had the lowest histogram error in 41 of 42 sessions. The histogram error metric does not explicitly capture the degree to which hypotheses predicted the mean outputnull activity, or any correlations that exist across outputnull dimensions. We therefore assessed how well the predictions captured the mean and covariance of observed data in all outputnull dimensions jointly (see Materials and methods). In agreement with our findings for histogram error, the mean (Figure 5B) and covariance (Figure 5C) of outputnull activity was best predicted by the Fixed Distribution hypothesis, with an average mean error of 23.5 ± 1.4 spikes/s (mean ± SE) and an average covariance error of 1.4 ± 0.1 (mean ± SE in arbitrary units; see Materials and methods). These error metrics offer further evidence that the Fixed Distribution hypothesis provides a good match to the outputnull distribution, as measured by the agreement between the first and second moments of the two distributions. Because these error metrics rely on a limited number of trials, they should not be compared relative to zero error. We estimated the smallest histogram, mean, and covariance errors achievable by any hypothesis, given the limited number of samples available to estimate the true outputnull distributions (see Materials and methods, and gray regions in Figure 5). The errors of Fixed Distribution were exceedingly close to the lowest achievable error given the number of samples available (see Materials and methods). Next, we found that the Fixed Distribution hypothesis achieved the lowest prediction errors among all hypotheses when data for each monkey was considered individually (Figure 5—figure supplement 1). We repeated our analyses to predict outputnull activity produced during the first mapping using activity observed during the second mapping (Figure 5—figure supplement 2). We also predicted outputnull activity using the actual BCI mapping rather than the animal’s internal model to define the outputnull dimensions (Figure 5—figure supplement 3). Both analyses yielded results similar to those in Figure 5.
Predicting changes in neural variability when activity becomes outputnull
So far we have shown that the Fixed Distribution hypothesis provides a better explanation for the structure of outputnull activity than hypotheses incorporating constraints on firing rates or the minimal intervention principle. We next sought stronger evidence for the Fixed Distribution hypothesis by assessing our predictions in the particular dimensions of population activity where it is least likely to hold. Because cursor velocity is a twodimensional quantity, all but two dimensions of population activity for each BCI mapping are outputnull. Thus, given two different BCI mappings, most dimensions will be outputnull under both mappings, and so most components of the population activity have no reason to change from one mapping to the other. Therefore, we assessed whether our results held in dimensions of population activity that were outputpotent during the first mapping, but outputnull during the second mapping (see Materials and methods). These are the dimensions in which one would expect to see the most changes in the population activity between the first and second mappings.
Our hypotheses make distinct predictions about how the variance of activity should change if a dimension is outputpotent under the first mapping and becomes outputnull under the second mapping. For example, according to the Minimal Firing and Minimal Deviation hypotheses, the variance of activity will collapse in dimensions that are outputnull because unnecessary spiking is undesirable. Thus, if a dimension becomes outputnull, variance in this space should exhibit a marked decrease. On the other hand, the Uncontrolled hypotheses predict that, when conditioned on the cursor movement, variance will expand when the activity is outputnull. This occurs because variability in this dimension will no longer affect cursor movement, and would therefore no longer need to be suppressed. Finally, the Fixed Distribution hypothesis posits that the same distributions of outputnull activity will be observed regardless of whether a dimension was previously outputpotent or outputnull, and so this hypothesis predicts that there will be little to no change in the variance of activity in a particular dimension under the two mappings.

Figure 6—source data 1
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We asked whether the variance of population activity decreased, increased, or remained the same in dimensions that changed from being outputpotent to outputnull (Figure 6A). Critically, we computed the variance of activity after first binning by the corresponding angle in the outputpotent dimensions of the second mapping. This was done so that the neural activity in each bin would all result in similar cursor movements under the second mapping, and is identical to the procedure used previously to assess the errors of the hypotheses’ predictions. Notably, binning in this way means that each bin may contain activity corresponding to different cursor movements under the first mapping, and so one might expect that in each bin the activity recorded under the first mapping would be more heterogeneous than the activity recorded under the second mapping.
We observed that the variance of population activity recorded under the first and second mappings was remarkably similar in the dimensions that changed from outputpotent to outputnull, even though these activity patterns usually corresponded to different cursor movements under the two mappings (Figure 6B). Thus, the variance of activity did not change much when an outputpotent dimension became outputnull, in agreement with the predictions of the Fixed Distribution hypothesis. To quantify these observations, we computed the average change in variance in each session (see Materials and methods). Across sessions, we found that the variance of observed activity showed a small but significant decrease when it became outputnull (Figure 6C, ‘Data’) (ttest, $p<0.001$). This is in contrast to the predictions of the Minimal Firing and Minimal Deviation hypotheses, which predicted much larger decreases.
The observed change in variance lies closest to the predictions of the Fixed Distribution hypothesis. In fact, we observed that the Fixed Distribution hypothesis also predicted a slight decrease in variance in dimensions that became outputnull (Figure 6C, ‘Fixed Distribution’) (ttest, $p<0.001$). This slight predicted change in variance occurs because the distributions of activity in the outputpotent dimensions of the second mapping are different under the first and second mappings. Because the Fixed Distribution hypothesis predicts a fixed conditional distribution of outputnull activity given the outputpotent activity, slightly different sets of outputpotent activity will result in a slightly different distribution of the corresponding outputnull activity.
These analyses show that, contrary to the predictions of the minimal firing and uncontrolled hypotheses, the variance of population activity did not change dramatically in dimensions that were outputpotent under the first mapping and outputnull under the second mapping. We also assessed whether the reverse was true—if the variance of activity changed in dimensions that began as outputnull and became outputpotent. To measure this, we repeated the above analyses after predicting outputnull activity produced during the first mapping using the activity observed under the second mapping (as in Figure 5—figure supplement 2). We found that the activity showed little to no change in variance in these dimensions (ttest, $p>0.5$), in agreement with the predictions of Fixed Distribution (Figure 6—figure supplement 1).
Importantly, the agreement between the observed outputnull activity and the predictions of the Fixed Distribution hypothesis in these analyses indicates that our ability to accurately predict the distribution of outputnull activity is not merely a result of most activity being outputnull under both mappings. Instead, the distribution of outputnull activity remains consistent with the Fixed Distribution hypothesis even in the outputnull dimensions that were previously outputpotent.
In Figure 6C, the observed outputnull activity showed a larger decrease in variance than the predictions of the Fixed Distribution hypothesis, at least in the 2D subspace of outputnull activity that was outputpotent during the first mapping. This slight decrease in variance is in the direction of the predictions of Minimal Firing and Minimal Deviation. If this decrease in variance is to be explained by Minimal Firing or Minimal Deviation principles, we would expect that the observed mean outputnull activity would also move in the direction of the predictions of Minimal Firing and Minimal Deviation, relative to what is predicted by Fixed Distribution. To see if this was the case, we first computed the distance of the observed mean outputnull activity from the mean predicted by Minimal Deviation for each movement direction, and compared this to the distance of the mean outputnull activity predicted by Fixed Distribution from the mean predictions of Minimal Deviation (Figure 6—figure supplement 2A). We did not find evidence that the observed mean outputnull activity was closer to the mean predicted by Minimal Deviation than was the mean predicted by Fixed Distribution (onesided Wilcoxon signed rank test, $p>0.5$; see Figure 6—figure supplement 2B and Materials and methods). Repeating the analysis with Minimal Firing instead of Minimal Deviation yielded similar results (onesided Wilcoxon signed rank test, $p>0.5$). Thus, while we observed a slight decrease in the variance of outputnull activity in dimensions that changed from outputpotent to outputnull, we did not find any evidence that the mean outputnull activity moved in the direction of the predictions of Minimal Firing or Minimal Deviation.
Discussion
Recent work has suggested that neural redundancy may be exploited for various computations (Druckmann and Chklovskii, 2012; Kaufman et al., 2014; MorenoBote et al., 2014; Elsayed et al., 2016; Driscoll et al., 2017; Murray et al., 2017). However, if the activity in outputnull dimensions is constrained by the outputpotent activity, then this may limit the ability of outputnull activity to perform computations without affecting the readout. Here, we studied neural redundancy in the primary motor cortex using a BCI, where it is known exactly which population activity patterns are redundant, meaning they produce an identical cursor movement. We generated predictions of the distributions of outputnull neural activity for subjects performing a BCI cursor control task, and compared them to the distributions observed in our experiments. We found that hypotheses inspired by minimal firing and minimal intervention principles, drawn from theories of muscle coordination, did not accurately predict the observed outputnull activity. Instead, we found that the distribution of outputnull activity was well predicted by the activity in the two outputpotent dimensions. This coupling between the outputpotent and outputnull activity implies that, when outputpotent activity is used to satisfy task demands, there are constraints on the extent to which neural circuits can use redundant activity to perform additional computations.
Our results indicate that the way in which neural redundancy is resolved is different from how muscle redundancy is resolved. There have been several prevalent proposals for how muscle redundancy is resolved, including minimal energy, optimal feedback control (OFC), and habitual control. Models incorporating minimal energy principles have helped to explain observed gait (McNeill Alexander and McNeill, 2002) and arm reaches (Thoroughman and Shadmehr, 1999; Huang et al., 2012; Fagg et al., 2002; Farshchiansadegh et al., 2016). By analogy, it has been proposed that the brain may prefer an ‘economy of impulses’ (Barlow, 1969; Softky and Kammen, 1991; Levy and Baxter, 1996), resolving neural redundancy by minimizing the production of action potentials. However, we found that minimal energy principles in terms of firing rates do not play a dominant role in the selection of outputnull neural activity. Given that metabolic activity can decrease without corresponding changes in firing rates (Picard et al., 2013), the brain may implement minimal energy principles without influencing the way neural redundancy is resolved.
OFC posits that motor control signals are selected to minimize a cost function that depends on task requirements and other factors, such as effort or delayed reward. OFC models have been widely used to explain muscle activity during motor tasks (Todorov, 2004; Scott, 2004; Diedrichsen et al., 2010). Our results for neural activity differ in two important respects from OFC predictions with standard cost functions involving task requirements and effort. First, those implementations of OFC predict that variability in taskirrelevant dimensions should be higher than variability in taskrelevant dimensions, a concept often referred to as the ‘uncontrolled manifold’ (Scholz and Schöner, 1999). We found that the variability of neural activity did not increase in dimensions that went from being taskrelevant to taskirrelevant (Figure 6C). Second, those implementations of OFC predict a ‘minimal intervention’ strategy, whereby activity in taskrelevant dimensions is corrected independently of activity in taskirrelevant dimensions (Todorov and Jordan, 2002; ValeroCuevas et al., 2009; Diedrichsen et al., 2010). Three of the hypotheses we tested incorporate this minimal intervention principle: Uncontrolleduniform, Uncontrolledempirical, and Persistent Strategy. None of these hypotheses predicted neural activity in taskirrelevant dimensions as accurately as did the Fixed Distribution hypothesis, which predicts that the distributions of taskrelevant and taskirrelevant activity are yoked. Overall, our work does not rule out the possibility that OFC is appropriate for predicting neural activity. First, it may be possible to design a cost function such that OFC predictions are consistent with the findings presented here. Second, one could consider applying OFC with the control signal being the input to M1 (e.g. PMd activity), rather than the control signal being M1 activity (as we have done here) or muscle activity (where OFC has been traditionally applied). This could induce coupling between the outputpotent and outputnull dimensions of the M1 activity, and thereby yield predictions that are consistent with the findings presented here.
It has also been proposed that muscle recruitment is habitual rather than optimal, such that muscle recruitment under altered dynamics is a rescaled version of that under normal control (de Rugy et al., 2012). The results for habitual control are similar to what we found for neural activity, in that (1) we could predict activity from previously observed activity, and (2) we observed a tight coupling of the distributions of taskrelevant and taskirrelevant activity (in contrast to minimal intervention). However, the results for habitual control are different from our findings in that we found that subjects appear to use the same distribution of activity in each of two different BCI mappings, whereas different (overlapping) subsets of muscle activation patterns were used under different conditions in de Rugy et al. (2012).
Given how many dimensions of population activity there are (in this case, 10), it is somewhat surprising that conditioning on only the two outputpotent dimensions could provide so much explanatory power for predicting the distribution in the remaining neural dimensions. This suggests that many of the dimensions of population activity are coupled, that is, changing the activity along some dimensions may also lead to changes along other dimensions, even though those dimensions are mutually orthogonal. During arm movement control, output dimensionality and presumably the neural dimensionality are larger than in our BCI setup. We speculate that during arm movements, many of the null dimensions will remain coupled with the potent dimensions, thereby yielding results similar to what we found here. Future work could examine whether animals can be trained to uncouple dimensions, as well as the effects of larger outputpotent dimensionality on redundancy, by repeating our analyses with a higherdimensional effector, such as a multiple degreeoffreedom robotic limb (e.g. Wodlinger et al., 2015).
The results presented here are related to, and go beyond, those in Golub et al. (2018). Although the two studies analyzed data from the same experiments, they ask distinct questions. Golub et al. (2018) focused on explaining the changes in population activity underlying behavioral learning. By contrast, in the present work we seek to determine the constraints on activity in the taskirrelevant (i.e. outputnull) dimensions. In other words, while Golub et al. (2018) focused on explaining the changes leading to behavioral learning, we focus here on the principles other than behavior that constrain population activity. As a result, all hypotheses we consider in the present work make predictions consistent with the observed behavior in the outputpotent dimensions.
Golub et al. (2018) found that the amount of learning animals showed was consistent with a fixed neural repertoire of population activity patterns being reassociated to control the second BCI mapping. The repertoire of population activity refers to the set of population activity patterns that were observed, whereas here we focused on the distribution, which describes how often the animals produced different activity patterns. In other words, the finding of a fixed repertoire is a statement about the support of the distribution of population activity, whereas here we found that the distribution of population activity can be predicted in outputnull dimensions, given the outputpotent activity. Because many different distributions of neural activity can be constructed from a fixed repertoire, the present results represent a stronger constraint on population activity than that shown in Golub et al. (2018). Indeed, the majority of the hypotheses we tested were consistent with a fixed neural repertoire, and thus cannot be disambiguated based on our prior work. This is evidenced by the predicted distributions largely overlapping with the support of the actual data distributions (Figures 2–4). The two hypotheses that were not fully consistent with a fixed repertoire are the Minimal Firing and Uncontrolleduniform hypotheses. However, in the context of predicting the distribution of activity in redundant dimensions, these hypotheses represent interesting cases worth considering (i.e. where population activity either obeys minimal firing constraints, or that the outputnull activity is fully unstructured, respectively), and so we included these hypotheses to cover these possibilities.
It is interesting to consider the relationship between arm movements and BCI cursor movements (Orsborn et al., 2014; Vyas et al., 2018). If the dimensions responsible for moving the arm overlap with both the outputpotent and outputnull dimensions of the BCI, this might explain the coupling we observe between the outputpotent and outputnull dimensions. However, in these experiments, the animal’s arm was not moving during BCI control (see Extended Data Figure 5 in Sadtler et al., 2014). Thus, the activity we study here resides within the arm’s outputnull dimensions. This implies that in our recordings the arm’s outputpotent dimensions do not overlap with either the outputpotent or the outputnull dimensions of the BCI, and so arm movements (or the lack thereof) are unlikely to explain the coupling we observed between the outputpotent and outputnull dimensions of the BCI. Overall, being unaware of extra outputpotent dimensions would likely make the predictions of the Fixed Distribution hypothesis worse, not better. The reason for this is as follows. The Fixed Distribution hypothesis predicts that the distribution of activity in outputnull dimensions depends upon the corresponding outputpotent activity. Under this hypothesis, the more we know of the outputpotent activity, the better we can predict the outputnull distribution. If there is an outputpotent dimension that we have not accounted for in our analyses, accounting for this dimension would likely improve our predictions. The fact that we were able to accurately predict the outputnull distributions (13% histogram error on average, with the lowest possible error being 7%) without knowing all the potent dimensions is then evidence that these extra potent dimensions, if they exist, would not provide substantial additional predictive power.
In this work, we define a set of population activity patterns as redundant if they all result in the same readout in downstream areas. This definition of redundancy comes from early work on motor control (Bernstein, 1967; Sporns and Edelman, 1993), where it was noted that different motor signals can result in the same movement kinematics. This is related to but distinct from the informationtheoretic definition of redundancy (Schneidman et al., 2003; Latham et al., 2005; Averbeck et al., 2006). In the informationtheoretic case, redundancy describes the extent to which correlations among neurons limit decoding accuracy for different stimuli. This is distinct from the type of redundancy studied here, defined as the existence of multiple population activity patterns corresponding to the same readout. For example, by the informationtheoretic definition, a system may have no redundancy (e.g. the population activity allows one to perfectly decode the encoded variable), but there may still be multiple population activity patterns that refer to this same encoded variable.
We found that the distribution of outputnull activity could be well predicted using activity recorded under a different BCI mapping. Two factors of our experimental design are particularly relevant when interpreting this result. First, we used a balanced centerout task design in which subjects made roughly equal numbers of movements in each direction. If we had, for example, required far more leftward than rightward movements, this would have altered the distribution of joint activity and skewed the estimates of outputnull activity during the second mapping. Second, this study focused on short timescales, where we predicted outputnull activity within one to two hours of subjects learning a new BCI mapping. On this timescale, the motor system must be able to rapidly learn a variety of different mappings between neural activity and behavior, and thus, a variety of different sets of redundant activity. An interesting avenue for further research would be to determine if the constraints we observe on neural redundancy remain over longer timescales. Given repeated practice with the same BCI mapping across days and weeks (Ganguly and Carmena, 2009), it is possible that there are different and perhaps fewer constraints on neural redundancy than what we found here.
We have tested six specific hypotheses about how neural redundancy is resolved. These hypotheses cover a spectrum of how strongly the activity in outputnull dimensions is constrained, with the minimal firing hypotheses being the most constrained, the minimal intervention hypotheses being the least constrained, and the Fixed Distribution hypothesis lying in between. Although the hypotheses we tested are not exhaustive, the best hypothesis (Fixed Distribution) yielded predictions of the distributions of outputnull activity whose marginal histograms differed from the data by only 13% on average (Figure 4F), where we estimated the lowest error possible to be 7% on average. Further improvements to the prediction accuracy may be possible by incorporating additional constraints, such as dynamics (Shenoy et al., 2013). It should be stressed that our focus here was on predicting the distribution of outputnull activity. Future work can assess whether outputnull activity can be predicted on a timestepbytimestep basis.
The central premise of the null space concept is that some aspects of neural activity are read out by downstream areas (outputpotent) while other aspects are not (outputnull) (Kaufman et al., 2014). This idea is related to the study of noise correlations, where it was recognized that activity fluctuations that lie outside of a stimulus encoding space (i.e. ‘stimulusnull’) are not detrimental to the stimulus information encoded by the neurons (Averbeck et al., 2006; MorenoBote et al., 2014). Studies have also shown that structuring neural activity in an appropriate null space can allow for multiplexing of different types of information (Mante et al., 2013; Raposo et al., 2014), as well as stable behavior (Leonardo, 2005; Rokni et al., 2007; Ajemian et al., 2013) and stable working memory (Druckmann and Chklovskii, 2012; Murray et al., 2017) in the presence of timevarying neural activity. Additionally, the existence of outputnull dimensions in the motor system may facilitate motor learning (Moorman et al., 2017; Ranganathan et al., 2013; Singh et al., 2016) or allow for motor preparation (Kaufman et al., 2014; Elsayed et al., 2016) or novel feedback processing (Stavisky et al., 2017) without causing overt movement. Our work suggests that there may be limits on the extent to which outputnull activity might be leveraged for neural computation. The coupling we observe between the distributions of outputnull and outputpotent activity suggests that outputnull activity is not modified independently of outputpotent activity. This coupling may cause activity fluctuations in a stimulusnull space to influence the downstream readout, or limit one’s ability to plan the next movement without influencing the current movement. Moving forward, an important direction for understanding the computations performed by different brain areas is to find out which aspects of the neural activity are read out (Pagan et al., 2013; Kaufman et al., 2014) and to understand how the dependencies like those identified in this study impact the computations being performed.
Materials and methods
Defining the mapping between neural activity and cursor movement
Request a detailed protocolExperimental methods are described in detail in both Sadtler et al. (2014) and Golub et al. (2018). Briefly, we recorded from the proximal arm region of primary motor cortex (M1) in three male Rhesus macaques using implanted 96channel microelectrode arrays (Blackrock Microsystems). All animal care and handling procedures conformed to the NIH Guidelines for the Care And Use of Laboratory Animals and were approved by the University of Pittsburgh’s Institutional Animal Care and Use Committee. The population spiking activity in each nonoverlapping 45 ms bin was computed as the number of threshold crossings on each channel. In each session, 85–94 neural units were recorded (25 sessions from monkey J, six sessions from monkey L, 11 sessions from monkey N). These sessions were analyzed previously in Golub et al. (2018). Data from monkeys J and L were first presented in Sadtler et al. (2014). The average firing rate of the neural units per session was 50 ± 8, 42 ± 4, and 55 ± 14 spikes/s (mean ± s.d.) for monkeys J, L, and N, respectively.
Each session began with a block of calibration trials. The calibration procedure for monkey J involved either passive observation of cursor movement, or closedloop BCI cursor control using the previous day’s BCI mapping. For monkeys L and N, we used a closedloop calibration procedure that gradually stepped from passive observation to closedloop control, as described in Sadtler et al. (2014). We then applied factor analysis (FA) to the spike counts recorded during these calibration trials to identify the 10D linear subspace (i.e. the ‘intrinsic manifold’) that captured dominant patterns of comodulation across neural units (Churchland et al., 2010; Harvey et al., 2012; Sadtler et al., 2014; Athalye et al., 2017). We then estimated the factor activity, $\mathbf{z}}_{t}\in {\mathbb{R}}^{10\times 1$, as the posterior expectation given the observed spike counts, $\mathbf{u}}_{t}\in {\mathbb{R}}^{q\times 1$, where $q$ is the number of neural units:
Here, $L,\Psi ,$ and $d$ are FA parameters estimated using the expectationmaximization algorithm, where $\Psi $ is constrained to be a diagonal matrix. The factor activity, ${z}_{t}$, can be interpreted as a weighted combination of the activity of different neural units. We refer to ${z}_{t}$ as a ‘population activity pattern.’
We next orthonormalized ${z}_{t}$ so that it had units of spike counts per time bin (Yu et al., 2009), using the following approach. In our FA model, $L$ defines a mapping from lowdimensional factor space to the higherdimensional neural space. Because the columns of $L$ are not orthonormal, the factor activity does not have the same units (spikes counts per time bin) as the neural activity. However, we can fix this by finding an orthonormal basis for the columns of $L$ (Yu et al., 2009). To do this, we apply the singular value decomposition, yielding $L=US{V}^{\top}$, where $U\in {\mathbb{R}}^{q\times 10}$ and $V\in {\mathbb{R}}^{10\times 10}$ have orthonormal columns and $S\in {\mathbb{R}}^{10\times 10}$ is diagonal. Then, we can write $L{\mathbf{z}}_{t}=U\left(S{V}^{\mathrm{\top}}{\mathbf{z}}_{t}\right)=U{\stackrel{~}{\mathbf{z}}}_{t}$. Because $U$ has orthonormal columns, $\stackrel{~}{\mathbf{z}}}_{t}=S{V}^{\mathrm{\top}}{\mathbf{z}}_{t$ has the same units (spike counts per time bin) as ${u}_{t}$. For notational simplicity, we refer to $\stackrel{~}{\mathbf{z}}}_{t$ as ${z}_{t}$ throughout. The values in ${z}_{t}$ appear larger than those expected for a single neuron because this value tends to grow with the total number of neural units.
Over the course of each experiment, animals used two different BCI mappings (see ‘Behavioral task’ below). Each BCI mapping translated the resulting momentbymoment factor activity (${z}_{t}$) into a 2D cursor velocity (${v}_{t}$) using a Kalman filter:
For the first BCI mapping, $A\in {\mathbb{R}}^{2\times 2}$, $B\in {\mathbb{R}}^{2\times 10}$, and $\mathbf{c}\in {\mathbb{R}}^{2\times 1}$ were computed from the Kalman filter parameters, estimated using the calibration trials. For the second BCI mapping, we changed the relationship between population activity and cursor movement by randomly permuting the elements of ${z}_{t}$ before applying Equation 2. This permutation procedure can be formulated so that Equation 2 still applies to the second BCI mapping, but with an updated definition of $B$ (Sadtler et al., 2014).
Behavioral task
Request a detailed protocolEach animal performed an 8target centerout task by modulating its M1 activity to control the velocity of a computer cursor. Each session involved two different BCI mappings. The first mapping was chosen to be intuitive for the animal to use. The animal used this first mapping for 200–400 trials, after which the mapping was changed abruptly to a second BCI mapping. The second mapping was initially difficult for the animal to use, and the animal was given 400–600 trials to learn to use the second mapping. Both mappings were chosen to be within the animal’s instrinic manifold, mappings that we found in previous work could be readily learned within one session (Sadtler et al., 2014).
At the beginning of each trial, a cursor appeared in the center of the workspace, followed by the appearance of one of eight possible peripheral targets (chosen pseudorandomly). For the first 300 ms of the trial, the velocity of the cursor was fixed at zero. After this, the velocity of the cursor was controlled by the animal through the BCI mapping. If the animal acquired the peripheral target with the cursor within 7.5 s, he received a water reward, and the next trial began 200 ms after target acquisition. Otherwise, the trial ended, and the animal was given a 1.5 s timeout before the start of the next trial.
Session and trial selection
Request a detailed protocolThe data analyzed in this study were part of a larger study involving learning two different types of BCI mapping changes: withinmanifold perturbations (WMP) and outsidemanifold perturbations (OMP) (Sadtler et al., 2014). We found that animals learned WMPs better than OMPs. Because we need animals to show stable cursor control under both mappings, we only analyzed WMP sessions in this study. Among the WMP sessions, we further selected those in which the animal learned stable control of the second mapping (42 selected and 12 discarded). This was important because performance with the second mapping was generally not as good as with the first mapping (Figure 1—figure supplement 1), and we wanted to ensure that any potential results were not due to incomplete learning of the second mapping (see also ‘Internal model estimation’ below). We further subselected from each session only those trials which exhibited stable behavioral performance, using a metric defined below. This was done to ensure that we were analyzing trials for which animals used a consistent strategy for selecting activity patterns.
We included sessions in which there existed a block of at least 100 consecutive trials that showed both substantial learning of the second mapping and consistent behavior. To identify trials showing substantial learning, we computed the running mean of the target acquisition time (on correct trials only), smoothed with a 100trial boxcar shifted one trial at a time. The smoothed acquisition time for a trial corresponded to the average acquisition time within a 100trial window centered on that trial. We then normalized these values so that 1 corresponded to the largest acquisition time in the first 50 trials using the second mapping, and 0 corresponded to the smallest acquisition time in the subsequent trials using the second mapping. We defined trials showing substantial learning as those with normalized acquisition times below 0.5. Next, to identify trials with consistent behavior, we computed the running variance of the target acquisition time. This was computed by taking the variance of the smoothed acquisition time above in a 100trial boxcar, shifted one trial at a time. We then normalized these variances so that 1 corresponded to the largest variance in the first half of trials using the second mapping, and 0 corresponded to the smallest variance in any trial using the second mapping. We defined trials showing stable behavior as those with normalized variance below 0.5. We then identified blocks of consecutive trials that passed both of these criteria, joining blocks if they were separated by no more than 10 trials. We then selected the longest such block of at least 100 trials for our analyses. If no such block of trials was found, we excluded that session from our analyses. This procedure resulted in the 42 sessions across three monkeys that we included in our analyses.
We analyzed only successful trials. To avoid analyzing time steps with potentially idiosyncratic cursor control, we also ignored portions of the trial when the cursor was closer than 50 mm or more than 125 mm away from the origin. We repeated our analyses without the latter exclusion and obtained quantitatively similar results.
Internal model estimation
Request a detailed protocolWhen an animal uses a BCI mapping, its internal conception of the BCI mapping can differ from the actual BCI mapping, even during proficient control (Golub et al., 2015). As a result, the animal’s conception of outputpotent versus outputnull dimensions can be different from those defined by the actual BCI mapping. To control for this possibility, we evaluated our predictions based on the animal’s internal conception of the outputnull dimensions, rather than the actual outputnull dimensions of the BCI mapping. This is particularly important for the second mapping, but we also did this for the first mapping. We used a method (Internal Model Estimation, IME) that we developed previously for estimating the animal’s internal model of the BCI mapping (Golub et al., 2015), with the exception that here we apply the model directly to the factor activity (${z}_{t}$) as opposed to the neural activity (${u}_{t}$), as was done in Golub et al. (2015).
The main idea of the IME framework is that the animal generates neural activity consistent with aiming straight to the target through an internal model of the BCI mapping. Due to natural visual feedback delay, the animal cannot exactly know the current cursor position, and thus aims from an internal estimate of the current cursor position. The internal estimate of the cursor position is a feedforward prediction based on previously issued neural activity and the most recently available visual feedback. Figure 5—figure supplement 4A shows a singletrial BCI cursor trajectory (black), along with the animal’s internal belief (red ‘whisker’) about how cursor position (red dots) evolved from the cursor position known from the most recently available visual feedback. The final segments of the trajectories reflect the same neural activity, which produces the actual cursor velocity (black arrow) through the actual BCI mapping, or the animal’s intended cursor velocity (red arrow) through the animal’s internal model. The animal’s velocity command viewed through the internal model points closer toward the target than the actual movement of the BCI cursor, corresponding to a smaller angular error. Across sessions, the animals’ angular errors when using the second BCI mapping did not usually return to the original level of error that the animal achieved under the first mapping (Sadtler et al., 2014) (Figure 5—figure supplement 4B). However, when viewed through the animals’ internal models of the BCI mappings, angular errors during the second mapping were more similar to those observed during the first mapping (Figure 5—figure supplement 4C). Thus, the internal model helps to control for possible incomplete learning of the second mapping.
We used IME to obtain the animal’s internal model of the BCI mapping (in the form of $A,B,c$ in Equation 2), which yielded a corresponding set of cursor velocities (${v}_{t}$), cursortarget angles (${\theta}_{t}$), and bases for the outputpotent and outputnull dimensions of each mapping (see $N$ and $R$ below) that we used in our offline analyses. The results reported in the main text are based on these quantities obtained from IME. When we analyzed the data without using IME (i.e. using the actual outputnull dimensions of the BCI mapping), all of the results we report still held (Figure 5—figure supplement 3).
Defining outputnull activity
Request a detailed protocolIn Equation 2, the matrix $B\in {\mathbb{R}}^{2\times 10}$ linearly projects a 10dimensional input (factor activity) to a 2dimensional output (cursor velocity). Thus, for any given cursor velocity (${v}_{t}$) there are multiple values of factor activity (${z}_{t}$) that would produce it. These multiple values of factor activity are all behaviorally equivalent, and we refer to their existence as ‘neural redundancy.’
Mathematically, it is useful to consider the null space, $Nul\left(B\right)$, and the row space, $Row\left(B\right)$, of the matrix $B$. The critical property of $Nul\left(B\right)$ is that for any element $y\in Nul\left(B\right)\subseteq {\mathbb{R}}^{10}$, we have $B\mathbf{x}=B\left(\mathbf{x}+\mathbf{y}\right)$ for all $x\in {\mathbb{R}}^{10}$. In other words, any change in activity within the null space of $B$ has no effect on the cursor movement produced. On the other hand, to achieve a particular cursor velocity (${v}_{t}$), there is exactly one $x\in Row\left(B\right)$ such that $Bx={v}_{t}$. Thus, the activity in the row space of $B$ uniquely determines the cursor movement. To find a basis for $Row\left(B\right)$ and $Nul\left(B\right)$, we took a singular value decomposition of $B=US{V}^{T}$, where the diagonal elements of $S$ were ordered so that only the first two values were nonzero. Then, we let $R\in {\mathbb{R}}^{10\times 2}$ be the first two columns of $V$, and $N\in {\mathbb{R}}^{10\times 8}$ be the remaining eight columns. The columns of $N$ and $R$ are mutually orthonormal and together form an orthonormal basis for the 10dimensional space of factor activity. This allowed us to decompose the factor activity ${z}_{t}$ at each time step into two orthogonal components: (1) activity in the row space of $B$ that affects the cursor velocity, which we call the outputpotent activity (${z}_{t}^{r}\in {\mathbb{R}}^{2}$); and (2) activity in the null space of $B$ that does not affect the cursor movement, which we call the outputnull activity (${z}_{t}^{n}\in {\mathbb{R}}^{8}$):
Note that all behaviorally equivalent activity will have the same outputpotent activity (${z}_{t}^{r}$), but can differ in outputnull dimensions. Thus, for time steps with similar cursor movements, the subject’s choice of 8D outputnull activity ($\mathbf{z}}_{t}^{n$) describes how the subject selected activity from a set of behaviorally equivalent options. Because the cursor velocity (${v}_{t}$) at each time step is a combination of outputpotent activity and the cursor velocity at the previous time step (see Equation 2), outputpotent activity can be thought of as driving a change in the cursor velocity. Note that in the depictions of hypotheses in Figure 1, Figure 2, Figure 3, and Figure 4, we used ${v}_{t}=B{z}_{t}$ instead of Equation 2 for clarity.
Predicting outputnull activity
Our goal for each experiment was to predict the distribution of observed outputnull activity during the second mapping across time steps corresponding to a given cursor movement direction (defined as the angle of ${v}_{t}$ in Equation 2). In the context of the centerout task, we assumed that cursor movements in the same direction but with different speeds were still behaviorally equivalent to the animal. This is supported by previous work that found substantially more directionrelated information than speedrelated information in both singleunit and population activity in M1 (Golub et al., 2014). For this reason we assessed the outputnull distribution in bins of cursor movement direction rather than cursor velocity (i.e. direction $\times $ speed).
All hypotheses generated predictions of the distribution of outputnull activity observed while animals used the second BCI mapping, unless otherwise noted. To generate predictions of the distributions of outputnull activity, we made predictions of the outputnull activity at each time step. This allowed us to ensure that our predictions were consistent with the cursor kinematics observed during the experiment. We then aggregated the predictions across all time steps during the experiment with a similar cursor movement direction. In all cases, the predicted outputnull activity respected the intrinsic manifold (Sadtler et al., 2014), because the outputnull activity lies in an $8$dimensional subspace of the $10$dimensional intrinsic manifold.
To generate a prediction of the outputnull activity for a particular time step (${z}_{t}^{n}$), each hypothesis had access to three sources of information recorded during the experiments. First, all hypotheses used the observed outputpotent activity (${z}_{t}^{r}$), in order to ensure that every prediction was physiologically plausible (see below). Second, all hypotheses except for the Minimal Firing hypothesis utilized factor activity recorded during use of the first BCI mapping to form their predictions of outputnull activity. Finally, the Persistent Strategy hypothesis also utilized the current position of the cursor relative to the target, defined as the cursortarget angle (${\theta}_{t}$).
We ensured that all predictions of outputnull activity ($\hat{\mathbf{z}}}_{t}^{n$) corresponded to physiologically plausible neural activity ($\hat{\mathbf{u}}}_{t$). By ‘physiologically plausible’ we mean that the neural activity was nonnegative, and no greater than the maximum number of spikes (per 45 ms time step) observed for that neural unit during trials using the first BCI mapping (${u}_{max}$). To enforce the constraint, we either incorporated the constraint $0\le {\hat{\mathbf{u}}}_{t}\le {\mathbf{u}}_{max}$ directly in the optimization problem (Minimal Firing hypothesis), or rejected predictions of neural activity that fell outside of the constraint (all other hypotheses). In the latter case, we combined the predicted outputnull activity with the observed outputpotent activity at that time step to form the predicted factor activity ($\hat{\mathbf{z}}}_{t$). We then converted this value to neural activity using the FA generative model:
If this neural activity was not physiologically plausible, we attempted to generate a new prediction of $\hat{\mathbf{z}}}_{t}^{n$ according to the hypothesis. This was possible because all hypotheses incorporated some form of sampling to generate their predictions. If this procedure failed even after 100 attempts to generate a physiologically plausible prediction, we skipped making a prediction for that time step. This happened for less than 1% of all time steps.
Minimal firing hypotheses
Request a detailed protocolAccording to the Minimal Firing hypothesis, generating spikes incurs a metabolic cost. Thus, the subject should select the population activity pattern that involves the fewest spikes among all patterns that generate the desired cursor movement. Predictions for this hypothesis were generated as follows. For each time step, we find the spiking activity closest to zero firing that produces the observed cursor velocity:
Above, $f\left(u\right)$ refers to the factor activity corresponding to $u$, as in Equation 1. Because $f\left(u\right)$ is a linear function of $u$, the above minimization is a convex problem. ${u}_{max}$ is the maximum activity level observed for each neuron, as described above. We solved for $\hat{\mathbf{u}}}_{t$ at each time step $t$ using Matlab’s quadprog. All trends in results were the same if the $\ell}_{2$ norm in the optimization problem was changed to an $\ell}_{1$ norm. After solving the above minimization, we incorporated variability in spike generation by sampling from a Poisson: ${\hat{\mathbf{u}}}_{t}^{\prime}\sim \text{Poisson}\left({\hat{\mathbf{u}}}_{t}\right)$. We repeated this last step if necessary until $\hat{\mathbf{u}}}_{t}^{\prime$ was physiologically plausible. Finally, we converted the prediction to factor activity, so that the resulting prediction of $\hat{\mathbf{z}}}_{t}^{n$ was ${\hat{\mathbf{z}}}_{t}^{n}:={N}^{\mathrm{\top}}f\left({\hat{\mathbf{u}}}_{t}^{\prime}\right)$.
We chose to incorporate Poisson variability into the predictions of the Minimal Firing (above) and Minimal Deviation hypotheses (below), rather than the Gaussian noise assumed by our FA model. The observed spike counts are discrete, whereas adding Gaussian noise would make the spike counts predicted by these hypotheses continuous. For this reason, to ensure a fair comparison we used Poisson variability, which will ensure the predictions remain discrete even after adding variability.
For the Minimal Deviation hypothesis, we generalized the Minimal Firing hypothesis so that instead of predicting the spiking activity nearest zero spikes/s, we predicted factor activity closest to some unknown activity level $\eta \in {\mathbb{R}}^{10}$. Solving this problem in the 10dimensional factor space for the optimal value of $\eta $ yields lower prediction error than doing so in the $q$dimensional neural space because we ultimately evaluate the hypotheses’ predictions in factor space. After choosing $\eta $ (see below), the predicted factor activity was obtained by solving the following optimization problem:
The above problem is known as a ‘minimum norm’ problem, and it turns out that the resulting solution’s outputnull activity, $\hat{\mathbf{z}}}_{t}^{n$, is a constant, for all $t$:
Because of the simple form of this solution, it was possible to choose the best value of $\eta $ for each session by minimizing the resulting outputnull prediction error across cursor directions (see ‘Error in mean’ below). This value is:
where ${\mu}_{i}^{n}$ is the average outputnull activity in the ${i}^{th}$ cursor direction bin, which we estimate using activity recorded during the first BCI mapping. This ensures that the data used to evaluate the predictions were not used to obtain $\eta $. Finally, we incorporated spiking variability just as we did for the Minimal Firing hypothesis. To do this, we first converted the above prediction ($\hat{\mathbf{z}}}_{t$) to neural activity using the FA generative model ($\hat{\mathbf{u}}}_{t}:=L{\hat{\mathbf{z}}}_{t}+\mathbf{d$). We then incorporated Poisson variability as described above, repeating the procedure until the resulting prediction was physiologically plausible, where the prediction of $\hat{\mathbf{z}}}_{t}^{n$ was ${\hat{\mathbf{z}}}_{t}^{n}:={N}^{\mathrm{\top}}f\left({\hat{\mathbf{u}}}_{t}^{\prime}\right)$, with ${\hat{\mathbf{u}}}_{t}^{\prime}\sim \text{Poisson}\left({\hat{\mathbf{u}}}_{t}\right)$.
Uncontrolleduniform hypothesis
Request a detailed protocolAccording to the uncontrolled manifold concept (Scholz and Schöner, 1999), variability in outputnull dimensions will be higher than that in outputpotent dimensions. One explanation of this idea is the minimal intervention principle (Todorov and Jordan, 2002; ValeroCuevas et al., 2009; Diedrichsen et al., 2010), which states that the variability in outputpotent dimensions is controlled independently of the outputnull activity, with the outputnull activity being unmodified. While this principle specifies that outputnull activity is independent of outputpotent activity, it does not specify what the distribution of outputnull activity actually is. Thus, we considered two hypotheses about this distribution. First, we supposed that the outputnull activity would be uniformly distributed within bounds determined by the physiological range of population activity. This hypothesis thus predicts that activity in outputnull dimensions has maximal entropy within the physiological range. For each $t$, we sampled:
Above, ${z}_{min}^{n}$ and ${z}_{max}^{n}$ set the range on the minimum and maximum possible outputnull activity. These bounds were set using population activity recorded during use of the first BCI mapping. We then resampled if necessary until our predictions generated physiologically plausible spiking activity when combined with the outputpotent activity.
Note that in Figure 2, Figure 3, and Figure 4 we applied PCA to the observed outputnull activity to depict the three outputnull dimensions with the most shared variance in the observed activity. Because of this, our visualizations of the distributions predicted by the Uncontrolleduniform hypothesis in Figure 3 appear moundshaped rather than uniform. To understand this, suppose we sample from a uniform distribution over a rectangle in 2D. If we rotate this rectangle slightly, and visualize the distribution of points along the xaxis, the distribution will be moundshaped. Similarly, the Uncontrolleduniform hypothesis samples from a uniform distribution in the $8$dimensional outputnull space, where the bounds of the rectangle are determined by bf $\mathbf{z}}_{min}^{n$ and ${z}_{max}^{n}$ above. Applying PCA rotates this activity, such that the density along the PC dimensions appears moundshaped.
Uncontrolledempirical hypothesis
Request a detailed protocolNext, we considered a different hypothesis about the distribution of outputnull activity under the minimal intervention principle. Rather than assuming outputnull activity is uniformly distributed, we obtained an empirical distribution using population activity observed under the first mapping. To produce predictions of outputnull activity during the second mapping, for each time step during the second mapping we sampled randomly from the population activity observed under the first mapping, and assessed the projection of that activity in the null space of the second mapping.
Concretely, let ${T}_{1}$ be the set of all time steps under the first mapping, and ${T}_{2}$ be the set of all time steps under the second mapping. Our prediction for each $t\in {T}_{2}$ is obtained by randomly sampling with replacement:
In other words, at each time step using the second mapping, we randomly select factor activity observed during the first mapping (${z}_{i}$), and project it into the null space of the second mapping (${N}^{\top}{z}_{i}$). We then resampled if necessary until our predictions generated physiologically plausible spiking activity when combined with the outputpotent activity.
Persistent Strategy hypothesis
Request a detailed protocolAn extension of the Uncontrolledempirical hypothesis is motivated by the idea that the subject may select activity under one mapping by modifying the activity he used under the first mapping. For a given cursortarget angle, if the subject selects the same population activity under the second mapping as under the first mapping, that activity may not move the cursor towards the target under the second mapping. To correct the cursor movement, he modifies this activity according to the minimal intervention principle (Todorov and Jordan, 2002; ValeroCuevas et al., 2009; Diedrichsen et al., 2010), correcting activity only along outputpotent dimensions of the current mapping. Concretely, for each $t\in {T}_{2}$, we sampled with replacement:
where ${\theta}_{t}$ is the cursortotarget angle at time $t$. As before, we resampled if necessary until our predictions generated physiologically plausible spiking activity when combined with the outputpotent activity. This hypothesis is identical to the Uncontrolledempirical hypothesis, except that at each time step we sampled only from time steps during the first mapping that had a similar cursortarget angle (i.e. within a 45° wedge around ${\theta}_{t}$). We found no consistent improvements in varying the constraints on the cursortarget angle (i.e. using values other than 22.5° in Equation 11), or when using the outputpotent angle rather than the cursortarget angle.
Fixed Distribution hypothesis
Request a detailed protocolAccording to the Fixed Distribution hypothesis, the activity in outputnull dimensions is tightly coupled to the activity in outputpotent dimensions, even under different BCI mappings when these dimensions are not necessarily still null and potent. This is in contrast to the three previous hypotheses (Uncontrolleduniform, Uncontrolledempirical, Persistent Strategy), which all incorporated a minimal intervention principle, whereby outputnull activity can be modified independently of the outputpotent activity, within the physiological limits on the firing rate of each unit.
Under the Fixed Distribution hypothesis, we predict that the distribution of outputnull activity given the outputpotent activity will be the same distribution as it was under the previous mapping. To implement this hypothesis, for each time step during the second mapping, we predict that the subject selects whichever activity pattern he produced under the previous mapping that would best match the current outputpotent activity. Specifically, given the outputpotent activity produced during the second mapping (${z}_{t}^{r}$), we found the time step during the first mapping (${i}^{\ast}\in {T}_{1}$) where the factor activity ${z}_{{i}^{\ast}}$ would have come closest to producing that outputpotent activity using the second mapping. Our prediction for outputnull activity was then the outputnull component of ${z}_{{i}^{\ast}}$ through the second mapping (${N}^{T}{z}_{{i}^{\ast}}$). Mathematically, for each $t\in {T}_{2}$ our prediction was:
We observed that these predictions all satisfied physiological constraints, which suggests that the values of ${z}_{{i}^{\ast}}$ selected at each time step each produced outputpotent activity sufficiently close to ${z}_{t}^{r}$.
This above implementation is also equivalent to the following: At each time step we identified the $K$ previously observed population activity patterns that would produce outputpotent activity closest to the current outputpotent activity under the second mapping. We then selected one of these patterns at random, and used the outputnull activity of that pattern as our prediction at that time step. In our above implementation, $K=1$. We found that using other values of $K$ (e.g. $K=50$, $K=200$) yielded similar results.
Evaluating predictions
For each session, we evaluated the predicted outputnull distributions of the above hypotheses in terms of how well they matched the observed outputnull distributions for all time steps with similar cursor movements. To do this, we first grouped time steps by their corresponding cursor velocity into eight nonoverlapping bins of cursor movement directions (${0}^{\circ}\pm {22.5}^{\circ},{45}^{\circ}\pm {22.5}^{\circ},\mathrm{...},{315}^{\circ}\pm {22.5}^{\circ}$). We then evaluated the accuracy of the predictions for each cursor movement direction.
For consistency, all predictions were evaluated in terms of factor activity. The Minimal Firing hypothesis generated its predictions in terms of neural activity, and we converted these predictions to factor activity using Equation 1.
Histogram overlap
Request a detailed protocolWe compared the predicted and observed distributions of outputnull activity in each dimension in terms of the average overlap of their histograms. For each session, we selected a single bin size for all histograms using crossvalidation (Rudemo, 1982). Then, for each cursor direction and outputnull dimension, we computed the error between the observed ($y$) and predicted ($\hat{\mathbf{y}}$) histograms. Let ${y}_{i}$ be the normalized frequency in the ${i}^{th}$ bin, so that ${\sum}_{i=1}^{m}}\text{\hspace{0.17em}}{y}_{i}=1$, and similarly for $\hat{\mathbf{y}}$. Then the histogram error was computed as follows:
Above, ${\scriptscriptstyle \frac{1}{2}}$ is included so that $L\left(\mathbf{y},\hat{\mathbf{y}}\right)=1$ if the two histograms are completely nonoverlapping. $L\left(\mathbf{y},\hat{\mathbf{y}}\right)=0$ if the two histograms are identical. This error was then averaged across all cursor directions and outputnull dimensions. We multiplied this value by 100 to yield the average histogram error percentages reported in the main text.
For the visualizations in Figure 2, Figure 3, and Figure 4, we displayed the marginal histograms in the three outputnull dimensions with highest variance in the observed outputnull activity, as found by PCA. For all error calculations we considered all eight outputnull dimensions without applying PCA.
Error in mean
Request a detailed protocolWe assessed how well our predictions matched the observed mean outputnull activity for each cursor movement direction. For all time steps in the same cursor movement direction bin, let ${\mu}^{n}\in {\mathbb{R}}^{8\times 1}$ be the vector of the mean observed outputnull activity, and $\hat{\mu}}^{\mathbf{n}}\in {\mathbb{R}}^{8\times 1$ the mean outputnull activity predicted by a particular hypothesis. These are both vectors, and so we computed the distance between them using the $\ell}_{2$ norm:
For each hypothesis, we computed the error in mean in each cursor movement direction bin, and took the average of these values as the error in mean for each session.
Error in covariance
Request a detailed protocolWe next assessed how well our predictions matched the observed covariance of outputnull activity for each cursor movement direction. Let ${C}^{n}\in {\mathbb{R}}^{8\times 8}$ and $\hat{C}}^{n}\in {\mathbb{R}}^{8\times 8$ be the covariance of the observed and predicted outputnull activity, respectively. There are a variety of methods for comparing covariance matrices, such as comparing their trace or determinant. We chose a metric invariant to affine transformations (e.g. scaling, translations, rotations) of the coordinate system (Dryden et al., 2009). Because the amount of variance in the recorded population activity might vary from session to session, this property of affine invariance helps ensure we can reasonably compare our covariance errors across sessions.
Let ${\lambda}_{i}\left({C}^{n},{\hat{C}}^{n}\right)$ be the ${i}^{th}$ generalized eigenvalue of ${C}^{n}$ and $\hat{C}}^{n$ (i.e. a value $\lambda $ such that $det\left({C}^{n}\lambda {\hat{C}}^{n}\right)=0$). Then following Lang (1999) and Förstner and Moonen (2003), we computed the distance between these two matrices as:
If $C}^{n}={\hat{C}}^{n$, then $L\left({C}^{n},{\hat{C}}^{n}\right)=0$. For each hypothesis, we computed the error in covariance in each cursor movement direction bin, and took the average of these values as the error in covariance for each session.
Error floor
Request a detailed protocolTo estimate the smallest errors achievable by any hypothesis (the ‘error floor’), given a limited number of samples to estimate the true outputnull distributions, we performed the following analysis. For each session, we randomly split the data during the second mapping in half, and measured the histogram, mean, and covariance errors when using the outputnull activity from one half to predict the distribution of the outputnull activity during the other half. We repeated this process 100 times per session, and took the averages of the resulting errors as our estimates of the error floors for that session.
Activity that became outputnull in the second mapping
Request a detailed protocolWe sought to assess whether the variance of population activity changed in dimensions that became outputnull under the second mapping. To do this, we identified the subspace of activity that was outputpotent under the first mapping, but outputnull under the second mapping.
As before, let the columns of $N$ be a basis for the null space of the second mapping. Now let the columns of ${R}_{1}$ be a basis for the row space of the first mapping. Then the space spanned by the columns of $\left(N{N}^{\top}\right){R}_{1}\in {\mathbb{R}}^{10\times 2}$ describes the activity that would move the cursor during the first mapping but would not move the cursor during the second mapping. Let $S\in {\mathbb{R}}^{10\times 2}$ be an orthonormal basis for $\left(N{N}^{\top}\right){R}_{1}$, which we obtained by performing a singular value decomposition. Now let $Z\in {\mathbb{R}}^{10\times n}$ be a matrix of $n$ factor activity patterns. To measure the amount of variance of $Z$ in the subspace spanned by the columns of $S$, we computed $\text{Trace}\left(\text{Cov}\right({Z}^{\top}S\left)\right)\in \mathbb{R}$.
To assess how the variance of activity changes when it becomes irrelevant to cursor control, we grouped the time steps based on the cursor movement angle under the second mapping, for activity recorded under both the first and second mappings. First conditioning on the movement angle under the second mapping is consistent with our earlier analyses, when comparing the predicted and observed outputnull distributions. To compute the cursor movement angle through the second mapping for activity recorded under the first mapping, we used the terms of Equation 2 not involving the cursor velocity at the previous time step (i.e. we computed ${v}_{t}=B{z}_{t}+c$). For consistency, we recomputed the cursor movement angle for activity recorded under the second mapping in the same way.
Let ${Z}_{1}$ and ${Z}_{2}$ be the factor activity in the same cursor movement angle bin recorded during the first and second mappings, respectively. We then computed the ratio of variance $R$ as follows:
The sign of $R$ specifies whether the variance of activity increased ($R>0$) or decreased ($R<0$) when that activity became irrelevant to cursor control under the second mapping. We took the average of this ratio across all cursor movement direction bins to compute a ratio for each session.
To compute this ratio for the predictions of our hypotheses, as in Figure 6C, we substituted ${Z}_{2}$ with the predictions of our hypotheses, i.e. by combining their predicted outputnull activity with the observed outputpotent activity under the second mapping.
We also repeated the above analyses on our predictions of outputnull activity produced during the first mapping using the activity observed under the second mapping, as shown in Figure 5—figure supplement 2 and Figure 6—figure supplement 1. This was done by swapping the roles of the first and second mappings in the above analysis description.
Distances of mean outputnull activity from Minimal Firing and Minimal Deviation
Request a detailed protocolFor each cursor direction on each session, we computed the distance from the mean observed outputnull activity to the mean predicted by the Minimal Deviation hypothesis, where the distance was computed as the $\ell}_{2$ norm between the two 8D mean vectors. We then compared this distance to the distance between the mean predicted by Fixed Distribution and the mean predicted by Minimal Deviation (Figure 6—figure supplement 2). If the latter distance was consistently smaller than the former, this would be evidence that the observed mean outputnull activity had moved towards the predictions of Minimal Deviation, relative to what was predicted by Fixed Distribution. We did not find evidence that this was the case (onesided Wilcoxon signed rank test, $p>0.5$), suggesting that the mean observed outputnull activity was not closer to Minimal Deviation than expected under Fixed Distribution. We repeated the same analysis using the mean predicted by Minimal Firing instead of Minimal Deviation, and reached the same results (onesided Wilcoxon signed rank test, $p>0.5$).
Appendix 1
To understand how our results might change if we recorded from more neural units, we assessed the dimensionality and shared variance of population activity with a varying number of units (Williamson et al., 2016) (Appendix 1—figure 1). For each session, we fit factor analysis (FA) models (as defined in Equation 1) to subsets of varying numbers of units and identified the number of factors needed to maximize the crossvalidated data likelihood. This resulted in estimates of the model parameters $L$ and $\Psi $. As in Williamson et al. (2016), dimensionality was defined as the number of eigenvectors of $L{L}^{\top}$ needed to explain 95% of the shared variance. Concretely, if the eigenvalues of $L{L}^{\top}$ are ${\lambda}_{1},{\lambda}_{2},\mathrm{...},{\lambda}_{D}$, then ${d}_{shared}$ is the smallest $J$ such that $\left({\displaystyle {\sum}_{i=1}^{J}}\text{\hspace{0.17em}}{\lambda}_{i}\right)/\left({\displaystyle {\sum}_{i=1}^{D}}\text{\hspace{0.17em}}{\lambda}_{i}\right)\ge 0.95$. Note that the absolute dimensionality depends on the method (% shared variance threshold) and criterion (threshold = 95%) used for assessing dimensionality. This is the same method used in Williamson et al. (2016), but differs slightly from the method used in Sadtler et al. (2014). We found that the dimensionality of the population activity increased with the number of units (Appendix 1—figure 1A).
As in Williamson et al. (2016), we computed the percentage of each neural unit’s activity variance that was shared with other recorded units (% shared variance). We calculated the average percent shared variance across neurons as follows:
where ${L}_{k}$ is the row of $L$ corresponding to unit $k$. We found that the % shared variance initially increased with the number of units, then reached an asymptote, such that the % shared variance was similar with 30 and 85 units (Appendix 1—figure 1B). The results in Appendix 1—figure 1A–B imply that the top $\sim $10 dimensions explain nearly all of the shared variance, and that additional dimensions identified by recording from more units explain only a small amount of additional shared variance. Thus, recording from more units beyond the $\sim $85 units that we recorded in these experiments is not likely to reveal additional dimensions with substantial shared variance. We next measured the principal angles between modes identified using 30 units with those identified using 85 units (Appendix 1—figure 1C) (Bjorck and Golub, 1973). Modes were defined as the eigenvectors of the shared covariance matrices corresponding to units from the 30unit set (i.e. the eigenvectors of $L{L}^{\top}$ where $L$ includes only the rows corresponding to the same 30 units). To restrict the analysis to the number of modes used to estimate the intrinsic manifold, only the ten modes explaining the most shared variance were included in the principal angle calculations. The small principal angles between modes identified using 30 and 85 units indicate that the dominant modes remained largely unchanged when using more units, in agreement with Williamson et al. (2016). These modes define the intrinsic manifold, the space within which we perform all of our analyses in the current work. Thus, recording from more units beyond the $\sim $85 units that we recorded in these experiments is not likely to substantially change the results reported in this work.
Data availability
Source data files have been provided for Figures 26. Code for analysis has been made available at https://github.com/mobeets/neuralredundancyelife2018, with an MIT open source license (copy archived at https://github.com/elifesciencespublications/neuralredundancyelife2018).
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Article and author information
Author details
Funding
National Science Foundation (NCS BCS1533672)
 Aaron P Batista
 Byron M Yu
 Steven M Chase
National Institutes of Health (R01 HD071686)
 Aaron P Batista
 Byron M Yu
 Steven M Chase
National Science Foundation (Career award IOS1553252)
 Steven M Chase
National Institutes of Health (CRCNS R01 NS105318)
 Aaron P Batista
 Byron M Yu
Craig H. Neilsen Foundation (280028)
 Aaron P Batista
 Byron M Yu
 Steven M Chase
Simons Foundation (364994)
 Byron M Yu
Pennsylvania Department of Health (Research Formula Grant SAP 4100077048)
 Byron M Yu
 Steven M Chase
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
The authors would like to thank Wilsaan Joiner and Doug Ruff for their feedback on the manuscript. This work was supported by NIH R01 HD071686 (APB, BMY, and SMC), NSF NCS BCS1533672 (SMC, BMY, and APB), NSF CAREER award IOS1553252 (SMC), NIH CRCNS R01 NS105318 (BMY and APB), Craig H Neilsen Foundation 280028 (BMY, SMC, and APB), Simons Foundation 364994 (BMY), and Pennsylvania Department of Health Research Formula Grant SAP 4100077048 under the Commonwealth Universal Research Enhancement program (SMC and BMY).
Ethics
Animal experimentation: All animal handling procedures were approved by the University of Pittsburgh Institutional Animal Care and Use Committee (protocol #15096685) in accordance with NIH guidelines. All surgery was performed under general anesthesia and strictly sterile conditions, and every effort was made to minimize suffering.
Version history
 Received: March 18, 2018
 Accepted: August 6, 2018
 Accepted Manuscript published: August 15, 2018 (version 1)
 Version of Record published: September 5, 2018 (version 2)
Copyright
© 2018, Hennig et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Further reading

 Neuroscience
The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel ‘shadow’ was quantified to evaluate the magnitude of the frequencylocked vasomotion. This method allows vasomotion to be easily quantified in nontransgenic wildtype mice using either the widefield macrozoom microscopy or the deepbrain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0.25 Hz) were presented to the awake mouse, and oscillatory vasomotion locked to the temporal frequency of the visual stimulation was induced not only in the primary visual cortex but across a wide surface area of the cortex and the cerebellum. The visually induced vasomotion adapted to a wide range of stimulation parameters. Repeated trials of the visual stimulus presentations resulted in the plastic entrainment of vasomotion. Horizontally oscillating visual stimulus is known to induce horizontal optokinetic response (HOKR). The amplitude of the eye movement is known to increase with repeated training sessions, and the flocculus region of the cerebellum is known to be essential for this learning to occur. Here, we show a strong correlation between the average HOKR performance gain and the vasomotion entrainment magnitude in the cerebellar flocculus. Therefore, the plasticity of vasomotion and neuronal circuits appeared to occur in parallel. Efficient energy delivery by the entrained vasomotion may contribute to meeting the energy demand for increased coordinated neuronal activity and the subsequent neuronal circuit reorganization.

 Medicine
 Neuroscience
Background:
Ketamine has emerged as one of the most promising therapies for treatmentresistant depression. However, interindividual variability in response to ketamine is still not well understood and it is unclear how ketamine’s molecular mechanisms connect to its neural and behavioral effects.
Methods:
We conducted a singleblind placebocontrolled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified restingstate functional connectivity via datadriven global brain connectivity and related it to individual ketamineinduced symptom variation and cortical gene expression targets.
Results:
We found that: (i) both the neural and behavioral effects of acute ketamine are multidimensional, reflecting robust interindividual variability; (ii) ketamine’s datadriven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral datadriven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the singlesubject level.
Conclusions:
These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry.
Funding:
This study was supported by NIH grants DP5OD01210901 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA01287011 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1–190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016–0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 – 2056) (FXV).
Clinical trial number: