Cortical activity in the null space: permitting preparation without movement

Journal name:
Nature Neuroscience
Volume:
17,
Pages:
440–448
Year published:
DOI:
doi:10.1038/nn.3643
Received
Accepted
Published online

Abstract

Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet it remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex, largely accounting for how preparatory activity is attenuated in primary motor cortex. Selective use of 'output-null' vs. 'output-potent' patterns of activity may thus help control communication to the muscles and between these brain areas.

At a glance

Figures

  1. Task and typical data.
    Figure 1: Task and typical data.

    (a) Layout of maze task. One typical trial shown. The same mazes were repeated many times; each maze is hereafter called a 'condition'. (b) Top, task timeline. The monkey initially touched a central spot with a cursor projected slightly above his fingertip; then a target and (typically) barriers appeared. On some trials, two inaccessible distractor 'targets' also appeared. After the Go cue (cessation of slight target jitter, extinguishing of central spot), the monkey made a curved reach around the barriers to touch the accessible target, leaving a white trail on the screen. If no barriers were present, reaches were straight. Middle, trial-averaged deltoid EMG; a.u., arbitrary units. Bottom, firing rate of one PMd neuron. Target, target onset; Go, go cue; Move, movement onset. Flanking traces show s.e.m. Maze identifier 100, neuron J-PM48, EMG recording J-PD10.

  2. Simplified output-null model.
    Figure 2: Simplified output-null model.

    For illustration, assume a muscle receives input from two neurons and produces a response that is the linear sum of the inputs. If the sum is constant (output-null dimension), the muscle cannot distinguish between input 1 being high and 2 low, and vice versa. When the sum changes (output-potent dimension), muscle output will change. If preparatory neural activity changes only within the output-null dimension (two different reaches illustrated in darker and lighter shades), then the muscle's activity remains constant; when neural activity changes in the output-potent dimension also, movement ensues. Insets: PSTHs for the neurons and PSTH-like views of output-potent and output-null dimensions. T, target onset; G, go cue; FR, firing rate.

  3. Examples suggesting potential output-null structure.
    Figure 3: Examples suggesting potential output-null structure.

    (a) When the weighted activity of the neuron graphed at left is added to the activity of the neuron graphed at center, the result (right) has less preparatory activity than either input. This pair thus illustrate the output-null idea, though with more neurons a more complete cancellation occurs. Constant c was set to 0.37. Conditions colored according to preparatory activity of left neuron. Targ, target onset; Move, movement onset. (b) Example readouts of real data. Each panel shows a linear, two-dimensional readout of real data, exhibiting the predicted structure (compare with Fig. 2). Each trace corresponds to a single, trial-averaged condition. Preparatory activity, blue; movement activity, green; state at Go cue, gray circles. Red ellipse shows 2 s.d. of the preparatory activity. As in the model, preparatory activity for different conditions is mostly spread out in one dimension, while movement-epoch activity travels through both dimensions. Dimensions found using the jPCA algorithm from ref. 38.

  4. Output-null results for cortex to muscles.
    Figure 4: Output-null results for cortex to muscles.

    (a) Neural activity in one output-null dimension for one data set (JA-2D1). All activity is trial-averaged, and each trace represents the response for a different condition; a.u., arbitrary units. (b) Neural data in one output-potent dimension. Dimensions were identified relative to EMG activity. This pair of example dimensions has a tuning ratio of 9.2. Bars indicate test epoch (−100 to 400 ms from target onset), where the tuning ratio was computed, and regression epoch (−50 to 600 ms from movement onset), where dimensions were identified. (c) Fraction of preparatory tuning (across conditions and times) in output-null (gray) and output-potent (black) dimensions for each data set. Tuning ratios indicated above bars; all values were significantly greater than unity. (d) Tuning at each time point, in the output-null and output-potent dimensions. Flanking traces indicate s.e.m. computed via resampling of conditions. Targ, target onset; Move, movement onset.

  5. Testing analysis method on simulated data.
    Figure 5: Testing analysis method on simulated data.

    Simulations produced artificial neural and EMG 'recordings' with the desired strength of output-null structure (Online Methods). We ran our analysis on this artificial data to quantify accuracy. (a) Example real neural recording. Each trace shows trial-averaged response for one condition. Conditions color-coded according to preparatory activity level. (b) Example simulated neuron. Qualitatively, it exhibits similar response complexity to that of the real neuron. (c) Example real EMG recording; a.u., arbitrary units. (d) Example simulated EMG recording. Qualitatively, it exhibits similar response complexity to that of real muscle. (e) Analysis of data produced without distorting nonlinearities. Dot indicates median measured effect size for set of 50 simulations. Error bars encompass 68% of simulations (equivalent to 1 s.d.). Gray line shows unity. (f) Analysis of data distorted with floor effects and saturating nonlinearities. (g) Analysis of same data as in f, but with underlying dimensionality underestimated during analysis: two output-null and two output-potent dimensions instead of three and three. (h) Same as g, but dimensionality overestimated as four output-null and four output-potent dimensions. In essentially all cases, adding nonlinearities to the data or misestimating dimensionality (including unequal numbers of output-null and output-potent dimensions; Supplementary Fig. 3) resulted in underestimates, not overestimates, of true effect size. Our results are thus likely conservative. Targ, target onset; Move, movement onset.

  6. Output-null results for PMd to M1.
    Figure 6: Output-null results for PMd to M1.

    Format as in Figure 4. (a) Neural activity in one PMd output-null dimension for one data set (NA-D4); a.u., arbitrary units. (b) Neural activity in one PMd output-potent dimension. Dimensions were identified relative to M1 activity. This example pair of dimensions has a tuning ratio of 3.8. (c) Fraction of preparatory tuning (across conditions and times) in output-null (gray) and output-potent (black) dimensions for each data set. Both ratios were significantly greater than unity. (d) Tuning at each time point, in the output-null and output-potent dimensions. Flanking traces indicate s.e.m. computed via resampling of conditions. Targ, target onset; Move, movement onset.

  7. Controls for output-null analysis.
    Figure 7: Controls for output-null analysis.

    (a) Results of output-null analysis, with M1 as source and PMd as target. As expected, no substantial effect was found. (b) Muscle activity over time relative to key epochs. Each muscle's activity was first normalized by its range. Heavy trace indicates mean across muscles. Distance from thin traces to thick trace shows mean tuning depth (assessed as s.d. across conditions). Red bar shows epoch used to identify output-potent dimensions. Effect size computed using only preparatory data (black bar). Monkey J; a.u., arbitrary units. (c) Black bars, measured effect size for each data set. Blue bars, effect size due to neurons with strong preparatory tuning preferentially contributing to output-null dimensions. Chance is unity. P-M, PMd-to-M1 analysis. (d) Neurons' contributions to the output-null and output-potent dimensions. For each neuron, we computed a space preference index that was 1 if the neuron contributed solely to output-potent dimensions and −1 if the neuron contributed solely to output-null dimensions. The histogram of values from the data is plotted in black (data set J). Chance distribution is plotted in purple. Horizontal bars (above) show ±1 s.d. Dots indicate means. Values for examples below indicated by green arrowheads. (e) PSTH for example neuron that mainly contributed to output-null dimensions. Unit J36. (f) Same as e, for neuron that contributed almost equally to output-null and output-potent dimensions. Unit J2. (g) Same as e, for neuron that mainly contributed to output-potent dimensions. Unit J149. Targ, target onset; Move, movement onset.

  8. Tuning is mismatched between the preparatory and movement epochs.
    Supplementary Fig. 1: Tuning is mismatched between the preparatory and movement epochs.

    Each panel represents firing rate data from one dataset. For each neuron, firing rates were first averaged over trials, then conditions were sorted based on the neuron's firing rate at movement onset. The reddest trace shows the average across neurons for each neuron's “most preferred" condition. The greenest trace shows the average across neurons for each neuron's “least preferred” condition; the other two traces represent the 33rd and 67th percentile conditions. Very little preparatory tuning remains after this procedure, indicating that tuning is mismatched between the preparatory and movement epochs. Each neuron's response was normalized by its range before averaging. Target, target onset; Move, movement onset. Scale bar indicates 200 ms.

  9. Behavioral consistency within and across days.
    Supplementary Fig. 2: Behavioral consistency within and across days.

    Single-electrode neural data were collected over many days, and it is important that behavior was similar over time. In particular, since EMG data were recorded late during single-electrode data collection, and array recordings were performed after that, this figure compares behavior during neural data collection and EMG data collection. (a) All reaches for a particular maze for dataset J (single electrode recordings for monkey J). Blue shows reaches made during one neuron worth of neural recording, red during one muscle worth of EMG recording. Note that some blue traces are hidden by the red traces. The same EMG session is shown as in Figure 1. The neural recording day was chosen to be the most distant possible from the EMG day, 4.5 months apart. Behavior for this example maze was slightly more different between days than typical. The scale bar is 40 mm. (b) The speed profile for each reach, aligned to movement onset (black tick). Colors as in (a). Scale bars are 100 ms and 0.5 m/s. (c-d) Same for dataset N (single electrode recordings for monkey N). The days were again chosen to be maximally far apart, ~3 months. (e-f) Same for dataset JA (array recordings for monkey J). Days were 9.5 months apart. (g-h) Same for dataset NA (array recordings for monkey N). Days were 1.2 months apart.

  10. Output-null method on additional simulated data.
    Supplementary Fig. 3: Output-null method on additional simulated data.

    Plots are as in Figure 5. These plots illustrate algorithm performance when the simulated data contains unequal numbers of output-null and output-potent dimensions, and how the analysis performs with (right) and without (left) the 1/γ term. (a-b) Four output-potent dimensions and two output-null dimensions were present in the simulated data. The analysis assumed three and three. Though false positives were present without the 1/γ term (a), using the term prevented false positives (b). (c-d) Four output-null dimensions and three output-potent dimensions were present in the simulated data, but the analysis assumed three and three. Again, false positives are absent, and the 1/γ term helps to reduce underestimation of the effect size. (e-f) Three output-potent and six output-null dimensions were present in the simulated data. The analysis assumed three and three. We note that this situation, of having only a few output-potent dimensions and many output-null dimensions, is likely to be the true one. Here, again, the analysis produces reasonable results with a mild underestimation of the effect size.

  11. Output-null results from cortex to muscles as a function of time.
    Supplementary Fig. 4: Output-null results from cortex to muscles as a function of time.

    This figure is similar to Figure 4d, but shows all datasets and all time points. We measured the tuning depth at each time point in the putative output-null dimensions (gray) and output-potent dimensions (black). As predicted, in all four datasets, the tuning depth during preparation was greater in the output-null dimensions than in the output-potent dimensions. This confirms that during movement preparation, the pattern of activity preferentially avoids the dimensions that cause muscle activity. Importantly, however, the effects shown here are smaller than the effects shown in Figure 4c because the present analysis cannot factor in the shift in the across-condition mean that occurs at target onset. Note that here the variance across conditions is shown, which effectively squares the ratio of how strong movement-epoch tuning appears relative to preparatory tuning; preparatory activity thus looks weaker than it would in a PSTH. Flanking traces indicate s.e.m.s computed via resampling of conditions.

  12. Output-null results from PMd to M1 as a function of time.
    Supplementary Fig. 5: Output-null results from PMd to M1 as a function of time.

    These plots are similar to Supplementary Figure 4, but show the output-null results for PMd to M1 instead of both areas to the muscles. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset, which is a major source of the effect size especially for monkey N.

  13. Analysis of neurons' projections into the output-null and output-potent spaces.
    Supplementary Fig. 6: Analysis of neurons' projections into the output-null and output-potent spaces.

    For each neuron, a space preference index was computed, which is +1 if the neuron contributes solely to output-potent dimensions and –1 if the neuron contributes solely to output-null dimensions. If there were two populations of neurons, where one population contributed mostly to output-null dimensions and the other contributed mostly to output-potent dimensions, we would expect a distribution with a peak near each extremum. The histogram of the values from the data are plotted in black, one panel per dataset. The null distribution (computed from random vectors in the same-dimensional space) is plotted in purple. One standard deviation above and below zero is plotted as horizontal bars above, with dots indicating the means. In each dataset, the empirical distribution is nearly identical to the random distribution. This indicates that there are not two separate populations of neurons driving the differences between the output-null and output-potent dimensions.

  14. Recording locations.
    Supplementary Fig. 7: Recording locations.

    Left, monkey J; right, monkey N. Top panels show outlines of the entry points where single-electrode recordings were made. Note that many recordings were made deep in the central sulcus, underneath the outlined areas. Bottom panels show the locations of the electrode arrays. For scale in the bottom panels, the arrays were square, 4.2 mm on a side.

  15. Muscle activity over time relative to key epochs.
    Supplementary Fig. 8: Muscle activity over time relative to key epochs.

    Plots as in Figure 7b, but for each monkey (J, left; N, right). Each muscle's activity was first normalized by its range (over all times and conditions). The heavy trace indicates the mean of these values across all muscles at each time point. This therefore gives a general idea of how average activity changed over time (muscle activity rose more strongly than it fell). To obtain an estimate of tuning depth with time, the standard deviation was taken across conditions (different reach shapes) at each time point for each normalized muscle. These values were then averaged across muscles at each time point. The width of the thin traces around the mean show this value, at one standard deviation. Muscle activity was only used in our main analysis to identify output-potent dimensions and output-null dimensions. To do so, we only used activity from the peri-movement epoch (red bar). Specifically, we used 0 to +650 ms from movement onset. Note that the muscle is already responding strongly by this time point. Effect size was computed using only preparatory data (black bar).

  16. Output-null results from cortex to muscles as a function of time, without the 1/[gamma] term.
    Supplementary Fig. 9: Output-null results from cortex to muscles as a function of time, without the 1/γ term.

    These plots are similar to the plots in Supplementary Figure 4, but the tuning in each space has not been normalized by the movement-epoch tuning. The 1/γ term was important in simulations to produce accurate results, and we therefore believe that Supplementary Figure 4 represents the most quantitatively interpretable effect sizes. However, we wished to show that the central effect was not somehow created by normalization. The current analysis demonstrates that the central effect survives without the 1/γ term. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset. This limitation appears to be particularly important in the monkey N dataset.

  17. Output-null results from PMd to M1 as a function of time, without the 1/[gamma] term.
    Supplementary Fig. 10: Output-null results from PMd to M1 as a function of time, without the 1/γ term.

    These plots are similar to Supplementary Figure 5, but as in Supplementary Figure 9, the values are not normalized by the movement-epoch tuning. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset, which is a major source of the effect size in monkey N.

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Author information

Affiliations

  1. Neurosciences Program, Stanford University, Stanford, California, USA.

    • Matthew T Kaufman &
    • Krishna V Shenoy
  2. Department of Electrical Engineering, Stanford University, Stanford, California, USA.

    • Matthew T Kaufman,
    • Stephen I Ryu &
    • Krishna V Shenoy
  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.

    • Matthew T Kaufman
  4. Department of Neuroscience, Columbia University Medical Center, New York, New York, USA.

    • Mark M Churchland
  5. Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, New York, USA.

    • Mark M Churchland
  6. David Mahoney Center for Brain and Behavior Research, Columbia University Medical Center, New York, New York, USA.

    • Mark M Churchland
  7. Kavli Institute for Brain Science, Columbia University Medical Center, New York, New York, USA.

    • Mark M Churchland
  8. Department of Neurosurgery, Palo Alto Medical Foundation, Palo Alto, California, USA.

    • Stephen I Ryu
  9. Department of Bioengineering, Stanford University, Stanford, California, USA.

    • Krishna V Shenoy
  10. Department of Neurobiology, Stanford University, Stanford, California, USA.

    • Krishna V Shenoy

Contributions

M.T.K. and M.M.C. designed and performed experiments. M.T.K. performed analyses and wrote the manuscript. S.I.R. performed array implantation surgery. K.V.S. oversaw all parts of experiments and writing.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Tuning is mismatched between the preparatory and movement epochs. (59 KB)

    Each panel represents firing rate data from one dataset. For each neuron, firing rates were first averaged over trials, then conditions were sorted based on the neuron's firing rate at movement onset. The reddest trace shows the average across neurons for each neuron's “most preferred" condition. The greenest trace shows the average across neurons for each neuron's “least preferred” condition; the other two traces represent the 33rd and 67th percentile conditions. Very little preparatory tuning remains after this procedure, indicating that tuning is mismatched between the preparatory and movement epochs. Each neuron's response was normalized by its range before averaging. Target, target onset; Move, movement onset. Scale bar indicates 200 ms.

  2. Supplementary Figure 2: Behavioral consistency within and across days. (100 KB)

    Single-electrode neural data were collected over many days, and it is important that behavior was similar over time. In particular, since EMG data were recorded late during single-electrode data collection, and array recordings were performed after that, this figure compares behavior during neural data collection and EMG data collection. (a) All reaches for a particular maze for dataset J (single electrode recordings for monkey J). Blue shows reaches made during one neuron worth of neural recording, red during one muscle worth of EMG recording. Note that some blue traces are hidden by the red traces. The same EMG session is shown as in Figure 1. The neural recording day was chosen to be the most distant possible from the EMG day, 4.5 months apart. Behavior for this example maze was slightly more different between days than typical. The scale bar is 40 mm. (b) The speed profile for each reach, aligned to movement onset (black tick). Colors as in (a). Scale bars are 100 ms and 0.5 m/s. (c-d) Same for dataset N (single electrode recordings for monkey N). The days were again chosen to be maximally far apart, ~3 months. (e-f) Same for dataset JA (array recordings for monkey J). Days were 9.5 months apart. (g-h) Same for dataset NA (array recordings for monkey N). Days were 1.2 months apart.

  3. Supplementary Figure 3: Output-null method on additional simulated data. (101 KB)

    Plots are as in Figure 5. These plots illustrate algorithm performance when the simulated data contains unequal numbers of output-null and output-potent dimensions, and how the analysis performs with (right) and without (left) the 1/γ term. (a-b) Four output-potent dimensions and two output-null dimensions were present in the simulated data. The analysis assumed three and three. Though false positives were present without the 1/γ term (a), using the term prevented false positives (b). (c-d) Four output-null dimensions and three output-potent dimensions were present in the simulated data, but the analysis assumed three and three. Again, false positives are absent, and the 1/γ term helps to reduce underestimation of the effect size. (e-f) Three output-potent and six output-null dimensions were present in the simulated data. The analysis assumed three and three. We note that this situation, of having only a few output-potent dimensions and many output-null dimensions, is likely to be the true one. Here, again, the analysis produces reasonable results with a mild underestimation of the effect size.

  4. Supplementary Figure 4: Output-null results from cortex to muscles as a function of time. (72 KB)

    This figure is similar to Figure 4d, but shows all datasets and all time points. We measured the tuning depth at each time point in the putative output-null dimensions (gray) and output-potent dimensions (black). As predicted, in all four datasets, the tuning depth during preparation was greater in the output-null dimensions than in the output-potent dimensions. This confirms that during movement preparation, the pattern of activity preferentially avoids the dimensions that cause muscle activity. Importantly, however, the effects shown here are smaller than the effects shown in Figure 4c because the present analysis cannot factor in the shift in the across-condition mean that occurs at target onset. Note that here the variance across conditions is shown, which effectively squares the ratio of how strong movement-epoch tuning appears relative to preparatory tuning; preparatory activity thus looks weaker than it would in a PSTH. Flanking traces indicate s.e.m.s computed via resampling of conditions.

  5. Supplementary Figure 5: Output-null results from PMd to M1 as a function of time. (34 KB)

    These plots are similar to Supplementary Figure 4, but show the output-null results for PMd to M1 instead of both areas to the muscles. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset, which is a major source of the effect size especially for monkey N.

  6. Supplementary Figure 6: Analysis of neurons' projections into the output-null and output-potent spaces. (108 KB)

    For each neuron, a space preference index was computed, which is +1 if the neuron contributes solely to output-potent dimensions and –1 if the neuron contributes solely to output-null dimensions. If there were two populations of neurons, where one population contributed mostly to output-null dimensions and the other contributed mostly to output-potent dimensions, we would expect a distribution with a peak near each extremum. The histogram of the values from the data are plotted in black, one panel per dataset. The null distribution (computed from random vectors in the same-dimensional space) is plotted in purple. One standard deviation above and below zero is plotted as horizontal bars above, with dots indicating the means. In each dataset, the empirical distribution is nearly identical to the random distribution. This indicates that there are not two separate populations of neurons driving the differences between the output-null and output-potent dimensions.

  7. Supplementary Figure 7: Recording locations. (136 KB)

    Left, monkey J; right, monkey N. Top panels show outlines of the entry points where single-electrode recordings were made. Note that many recordings were made deep in the central sulcus, underneath the outlined areas. Bottom panels show the locations of the electrode arrays. For scale in the bottom panels, the arrays were square, 4.2 mm on a side.

  8. Supplementary Figure 8: Muscle activity over time relative to key epochs. (38 KB)

    Plots as in Figure 7b, but for each monkey (J, left; N, right). Each muscle's activity was first normalized by its range (over all times and conditions). The heavy trace indicates the mean of these values across all muscles at each time point. This therefore gives a general idea of how average activity changed over time (muscle activity rose more strongly than it fell). To obtain an estimate of tuning depth with time, the standard deviation was taken across conditions (different reach shapes) at each time point for each normalized muscle. These values were then averaged across muscles at each time point. The width of the thin traces around the mean show this value, at one standard deviation. Muscle activity was only used in our main analysis to identify output-potent dimensions and output-null dimensions. To do so, we only used activity from the peri-movement epoch (red bar). Specifically, we used 0 to +650 ms from movement onset. Note that the muscle is already responding strongly by this time point. Effect size was computed using only preparatory data (black bar).

  9. Supplementary Figure 9: Output-null results from cortex to muscles as a function of time, without the 1/γ term. (73 KB)

    These plots are similar to the plots in Supplementary Figure 4, but the tuning in each space has not been normalized by the movement-epoch tuning. The 1/γ term was important in simulations to produce accurate results, and we therefore believe that Supplementary Figure 4 represents the most quantitatively interpretable effect sizes. However, we wished to show that the central effect was not somehow created by normalization. The current analysis demonstrates that the central effect survives without the 1/γ term. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset. This limitation appears to be particularly important in the monkey N dataset.

  10. Supplementary Figure 10: Output-null results from PMd to M1 as a function of time, without the 1/γ term. (35 KB)

    These plots are similar to Supplementary Figure 5, but as in Supplementary Figure 9, the values are not normalized by the movement-epoch tuning. Again, note that this analysis underestimates the true effect size because it cannot factor in the shift in the across-condition mean that occurs at target onset, which is a major source of the effect size in monkey N.

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  1. Supplementary Text and Figures (2,839 KB)

    Supplementary Figures 1–10 and Supplementary Table 1

Additional data