The motor cortex is capable of reliably driving complex movements1,2 yet exhibits considerable plasticity during motor learning3,4,5,6,7,8,9,10. These observations suggest that the fundamental relationship between motor cortex activity and movement may not be fixed but is instead shaped by learning; however, to what extent and how motor learning shapes this relationship are not fully understood. Here we addressed this issue by using in vivo two-photon calcium imaging11 to monitor the activity of the same population of hundreds of layer 2/3 neurons while mice learned a forelimb lever-press task over two weeks. Excitatory and inhibitory neurons were identified by transgenic labelling12,13. Inhibitory neuron activity was relatively stable and balanced local excitatory neuron activity on a movement-by-movement basis, whereas excitatory neuron activity showed higher dynamism during the initial phase of learning. The dynamics of excitatory neurons during the initial phase involved the expansion of the movement-related population which explored various activity patterns even during similar movements. This was followed by a refinement into a smaller population exhibiting reproducible spatiotemporal sequences of activity. This pattern of activity associated with the learned movement was unique to expert animals and not observed during similar movements made during the naive phase, and the relationship between neuronal activity and individual movements became more consistent with learning. These changes in population activity coincided with a transient increase in dendritic spine turnover in these neurons. Our results indicate that a novel and reproducible activity–movement relationship develops as a result of motor learning, and we speculate that synaptic plasticity within the motor cortex underlies the emergence of reproducible spatiotemporal activity patterns for learned movements. These results underscore the profound influence of learning on the way that the cortex produces movements.
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We thank A. Kim and S. Kalina for technical assistance; L. L. Looger, J. Akerboom, D. S. Kim and the GENIE Project at Janelia Farm for making GCaMP available; E. Kyubwa and J. Keller for help with task development; A. D. Lien and M. Caudill for help with two-photon guided recordings; and M. Long, R. Malinow, G. Murphy, M. Scanziani and members of the Komiyama laboratory for comments and discussions. This work was supported by grants from Japan Science and Technology Agency (PRESTO), Pew Charitable Trusts, Alfred P. Sloan Foundation, David & Lucile Packard Foundation, Human Frontier Science Program and New York Stem Cell Foundation to T.K. A.J.P. is supported by the Neuroplasticity of Aging Training Grant (AG000216). S.X.C. is a Human Frontier Science Program postdoctoral fellow. T.K. is a NYSCF-Robertson Investigator.
The authors declare no competing financial interests.
Extended data figures and tables
The fraction of rewarded trials is consistently high but the timing of behaviour improves during learning. a, Fraction of trials that are rewarded. b, Time from cue onset to movement onset decreases (P < 0.001, one-way ANOVA); inset, zoom. c, Time from movement onset to reward decreases (P < 0.001, one-way ANOVA); inset, zoom. d, The duration of each rewarded movement is stable throughout learning (P = 0.94, one-way ANOVA). Grey, individual mice; red, mean of all animals (a) or median of all trials (b–d).
a, Aspiration lesion of motor cortex impairs learning. Mice were allowed to recover for 14 days after lesion before training. Left: histological image showing lesion in the right motor cortex and quantification of lesion extents in four mice shown as a density map of the fraction of animals in which the area was lesioned. Anterior is to the top; lateral to the right. + denotes bregma. The white circle indicates the imaged area. Middle: average time from movement onset to reward throughout learning. This time is longer in mice with motor cortex lesion (P < 0.01, two-way ANOVA), indicating that the mice with a lesion are less efficient in their movements. Right: correlation of lever movements in all pairs of trials within each session throughout learning. This correlation is lower in the mice with a lesion (P < 0.001, two-way ANOVA), indicating that the mice with a lesion do not develop reproducible movements. b, Injections of muscimol, a GABA receptor agonist, into the imaged area acutely impairs performance (control versus muscimol in motor cortex, **P < 0.01, Wilcoxon rank sum test). Muscimol injections in the barrel cortex had no significant effect (control versus muscimol in barrel cortex, P = 0.35, Wilcoxon rank sum test). Control, n = 18 sessions in 6 mice; barrel cortex, n = 6 sessions in 6 mice; motor cortex, n = 6 sessions in 6 mice. c, The imaged cortical area was acutely inactivated by stimulation of ChR2 in parvalbumin-expressing inhibitory neurons by blue light in interleaved 20% of trials (n = 10 sessions in 2 animals). This optogenetic inactivation of the imaged area impaired performance on a trial-by-trial basis (***P < 0.001, Wilcoxon rank sum test). Blue light had no effect on behaviour when the window was covered with opaque silicone (n = 4 sessions in 2 animals). All error bars are s.e.m.
Extended Data Figure 3 Optogenetic stimulation of the imaged area evokes forelimb movements in awake mice.
a, Optogenetic excitation of the imaged area triggers forelimb movements in mice expressing ChR2 but not in control mice not expressing ChR2 (P < 0.001, chi-squared test). ChR2 expression does not alter spontaneous movement frequency in the absence of stimulation (P = 0.64, chi-squared test). ‘During light’, 1-s light stimulation; ‘before light’, 2 to 1 s before light onset, n = 40 ‘during light’ trials and 38 ‘before light’ trials in two ChR2 mice, 38 ‘during light’ trials and 38 ‘before light’ trials in two control mice. b, Histological section showing the expression of ChR2 in the motor cortex. Green, ChR2–YFP; blue, DAPI.
Extended Data Figure 4 Simultaneous cell-attached recordings and two-photon calcium imaging in awake mice.
a, Left: in vivo two-photon image of motor cortex neurons expressing GCaMP5G. The neuron in the centre is targeted with a patch electrode. Right: after the recording session, voltage step was applied to the electrode to activate the recorded neuron. The increased GCaMP5G fluorescence in the middle neuron confirms that the neuron was indeed targeted. b, Example GCaMP5G fluorescence trace (top: black indicates fluorescence trace and red indicates detected calcium events) and simultaneously recorded action potentials (bottom: black vertical ticks; the numbers indicate the number of action potentials contained in each burst). Horizontal red lines at bottom indicate the duration of detected calcium events. Note the precise temporal relationship between action potentials and calcium events. c, Table summarizing data from six neurons in two mice. Positive offsets indicate the lag of the onset of detected calcium events relative to the first spike in the burst. The offset (7.1 ± 41.4 ms) is on the order of the temporal resolution of our imaging (∼35 ms per image frame).
Each plot represents one animal. Red dots show the mean pairwise distance between movement-related excitatory neurons. Solid and dotted black lines show the mean and 95% confidence intervals, respectively, obtained from shuffling the identities of movement-related neurons among all excitatory neurons 10,000 times. Dots below the lower dotted line would indicate significant clustering of cells, whereas dots above the upper dotted line would indicate the significant dispersion of cells (P < 0.05).
a, Cumulative distribution of fraction of sessions classified as movement-related for inhibitory (red) and excitatory (green) neurons, showing the relative invariance of inhibitory neurons and dynamism of excitatory neurons (P < 0.001, Kolmogorov–Smirnov test). b, Movement-related excitatory neuron populations in each session compared to the previous session. Grey, fraction of neurons classified in the previous session; white, not classified in the previous session. A large number of newly movement-related neurons were added in the first few sessions (P < 0.001, comparison between sessions 2–4 versus 10–14, Wilcoxon rank sum test). c, Fraction of excitatory neurons classified as movement-related in each session. Black, training (n = 7 mice, this is the data shown in Fig. 2b); red, no training (n = 6 mice). The expansion of movement-related neurons is specific to animals that underwent training (P = 0.74, sessions 1–2 combined; P < 0.001, sessions 3–7 combined; Wilcoxon rank sum test). d, Average population activity aligned to movement onset (black dotted line). Average activity (calcium event trace) of each movement-related excitatory neuron was averaged. The population activity diverged from baseline 105 ms before movement onset (red dotted line, Methods). e, Standard deviation of activity timing of individual movement-related excitatory neurons across sessions. Focusing on neurons that are classified as movement-related in three or more sessions, the standard deviation of activity onset timing relative to movement onset is plotted across sessions. Sessions were binned into one-third of the total number of sessions each neuron was classified. Activity timing became more stable on the neuron-by-neuron basis (r = −0.14, P < 0.001). f, Histogram of the time from movement onset that the activity of each movement-related neuron significantly diverged from baseline. 9.2% of movement-related excitatory neurons show significant pre-movement activity, a composition similar to a previous study14. 82.7% of activity of movement-related neurons occurred during the periods between 105 ms before movement onset and movement offset (Methods). g, The cumulative fraction plot of the timing of all activity onsets of movement-related excitatory neurons during rewarded movements. Each group of sessions is shown as a line, with different colours representing different sessions. The distribution of activity onset timing during later sessions shifts towards the movement onset (P < 0.001, Kolmogorov–Smirnov test for all three comparisons). All error bars are s.e.m.
For the activity analyses in the main figures that used the duration of 3 s after movement onset, we repeated the same analyses focusing on the first 500 ms of each movement (median time from movement onset to reward = 506 ms). This early activity shows progression throughout learning, similar to when activity over 3 s was considered. a, Standard deviation of the timing of activity onsets for movement-related excitatory neurons over sessions, indicating a gradual refinement of activity timing (r = −0.18, P < 0.001). Neurons that were active in less than five trials of a given session were excluded from this analysis. The first bin contains only one data point and thus does not have an error bar. This analysis is equivalent to Fig. 2g. b, Pairwise trial-to-trial correlation of temporal population activity vectors increases with learning (r = 0.38, P < 0.001). Temporal population activity vector was defined as a concatenation of the activity traces of all movement-related neurons and thus maintained temporal information within each movement. This analysis is equivalent to Fig. 2h. c, Correlation of spatiotemporal activity with the learned activity pattern is a function of the correlation of movement with the learned movement pattern in expert sessions. Movements similar to the learned movement pattern but made in naive sessions display activity very different from the learned activity pattern (P = 0.28 and <0.001 in the bins 1 and 2–10, respectively, Wilcoxon rank sum test). This analysis is equivalent to Fig. 3b. d, Pairwise trial-to-trial correlation of temporal population activity vectors plotted as a function of movement correlation on those trials. A strong relationship between population activity and movement emerges during learning (P = 0.08, = 0.08, = 0.004, <0.001, = 0.002, <0.001, = 0.001, = 0.002, <0.001 and = 0.046 for each bin, Wilcoxon rank sum test). This analysis is equivalent to Fig. 3c. All error bars are s.e.m.
Extended Data Figure 8 Dynamics of dendritic spines in the hindlimb area during learning of the lever-press task.
Summary of dendritic spine dynamics in the hindlimb area during control period (7 days before training) and subsequent 7 days of training. Mice were water restricted in both conditions. Top: spine additions (black) and eliminations (grey) in each session. For control sessions, data from all sessions are combined. Bottom: total spine number across sessions. Values are normalized to the total spine number in session 1 in each condition. Unlike the forelimb area, the density of dendritic spines in the hindlimb area is relatively stable during learning (P = 0.07, comparisons between control versus training sessions 4–7, Wilcoxon rank sum test). All error bars are s.e.m.
Extended Data Figure 9 Schematic of learning-related changes in the relationship of motor cortex activity and movement.
Top: abstract space of activity patterns. Bottom: abstract space of movements. Circles in the movement space represent observed movements, and ovals in the activity space represent possible activity patterns that can lead to corresponding movements. Crosses and arrows represent example individual trials of activity–movement pairs. In naive animals, each trial involves variable activity and movement patterns as illustrated by scattered crosses and multiple movements. In this stage, the relationship between activity and movement is inconsistent (that is, degenerate), such that same movement is derived from different activity patterns in different trials. During learning, this degeneracy is reduced and a reproducible spatiotemporal activity pattern emerges in the motor cortex that reliably generates the learned movement. This learned activity pattern (bold cross) is rarely, if at all, observed in naive stages.
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Peters, A., Chen, S. & Komiyama, T. Emergence of reproducible spatiotemporal activity during motor learning. Nature 510, 263–267 (2014). https://doi.org/10.1038/nature13235
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