Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Tanji, J. & Evarts, E. V. Anticipatory activity of motor cortex neurons in relation to direction of an intended movement. J. Neurophysiol. 39, 1062–1068 (1976)
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I. & Shenoy, K. V. Cortical preparatory activity: representation of movement or first cog in a dynamical machine? Neuron 68, 387–400 (2010)
Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014)
Erlich, J. C., Bialek, M. & Brody, C. D. A cortical substrate for memory-guided orienting in the rat. Neuron 72, 330–343 (2011)
Murakami, M., Vicente, M. I., Costa, G. M. & Mainen, Z. F. Neural antecedents of self-initiated actions in secondary motor cortex. Nature Neurosci. 17, 1574–1582 (2014)
Maimon, G. & Assad, J. A. A cognitive signal for the proactive timing of action in macaque LIP. Nature Neurosci. 9, 948–955 (2006)
Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971)
Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989)
Romo, R., Brody, C. D., Hernandez, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999)
Liu, D. et al. Medial prefrontal activity during delay period contributes to learning of a working memory task. Science 346, 458–463 (2014)
Wang, X. J. Decision making in recurrent neuronal circuits. Neuron 60, 215–234 (2008)
Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007)
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015)
Mainen, Z. F. & Sejnowski, T. J. Reliability of spike timing in neocortical neurons. Science 268, 1503–1506 (1995)
Cannon, S. C., Robinson, D. A. & Shamma, S. A proposed neural network for the integrator of the oculomotor system. Biol. Cybern. 49, 127–136 (1983)
Sheffield, M. E., Best, T. K., Mensh, B. D., Kath, W. L. & Spruston, N. Slow integration leads to persistent action potential firing in distal axons of coupled interneurons. Nature Neurosci. 14, 200–207 (2011)
Yoshida, M. & Hasselmo, M. E. Persistent firing supported by an intrinsic cellular mechanism in a component of the head direction system. J. Neurosci. 29, 4945–4952 (2009)
Barak, O., Sussillo, D., Romo, R., Tsodyks, M. & Abbott, L. F. From fixed points to chaos: three models of delayed discrimination. Prog. Neurobiol. 103, 214–222 (2013)
Murakami, M. & Mainen, Z. F. Preparing and selecting actions with neural populations: toward cortical circuit mechanisms. Curr. Opin. Neurobiol. 33, 40–46 (2015)
Fisher, D., Olasagasti, I., Tank, D. W., Aksay, E. R. & Goldman, M. S. A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit. Neuron 79, 987–1000 (2013)
Wang, X. J. Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455–463 (2001)
Sussillo, D. & Abbott, L. F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009)
Laje, R. & Buonomano, D. V. Robust timing and motor patterns by taming chaos in recurrent neural networks. Nature Neurosci. 16, 925–933 (2013)
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013)
London, M., Roth, A., Beeren, L., Hausser, M. & Latham, P. E. Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex. Nature 466, 123–127 (2010)
Kitano, H. Biological robustness. Nature Rev. Genet. 5, 826–837 (2004)
Csete, M. E. & Doyle, J. C. Reverse engineering of biological complexity. Science 295, 1664–1669 (2002)
Aksay, E. et al. Functional dissection of circuitry in a neural integrator. Nature Neurosci. 10, 494–504 (2007)
Kopec, C. D., Erlich, J. C., Brunton, B. W., Deisseroth, K. & Brody, C. D. Cortical and subcortical contributions to short-term memory for orienting movements. Neuron 88, 367–377 (2015)
Li, N., Chen, T. W., Guo, Z. V., Gerfen, C. R. & Svoboda, K. A motor cortex circuit for motor planning and movement. Nature 519, 51–56 (2015)
Komiyama, T. et al. Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464, 1182–1186 (2010)
Shenoy, K. V., Sahani, M. & Churchland, M. M. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337–359 (2013)
Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nature Neurosci. 17, 440–448 (2014)
Tanaka, M. Cognitive signals in the primate motor thalamus predict saccade timing. J. Neurosci. 27, 12109–12118 (2007)
Seung, H. S. How the brain keeps the eyes still. Proc. Natl Acad. Sci. USA 93, 13339–13344 (1996)
Amit, D. J. & Brunel, N. Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. Cereb. Cortex 7, 237–252 (1997)
Lim, S. & Goldman, M. S. Balanced cortical microcircuitry for maintaining information in working memory. Nature Neurosci. 16, 1306–1314 (2013)
Stopfer, M., Jayaraman, V. & Laurent, G. Intensity versus identity coding in an olfactory system. Neuron 39, 991–1004 (2003)
Cisek, P., Puskas, G. A. & El-Murr, S. Decisions in changing conditions: the urgency-gating model. J. Neurosci. 29, 11560–11571 (2009)
Druckmann, S. & Chklovskii, D.B. Neuronal circuits underlying persistent representations despite time varying activity. Curr. Biol 22, 2095–2103 (2012)
Goldman, M. S. Memory without feedback in a neural network. Neuron 61, 621–634 (2009)
Diester, I. et al. An optogenetic toolbox designed for primates. Nature Neurosci. 14, 387–397 (2011)
Sadtler, P. T. et al. Neural constraints on learning. Nature 512, 423–426 (2014)
Hippenmeyer, S. et al. A developmental switch in the response of DRG neurons to ETS transcription factor signaling. PLoS Biol. 3, e159 (2005)
Hooks, B. M., Lin, J. Y., Guo, C. & Svoboda, K. Dual-channel circuit mapping reveals sensorimotor convergence in the primary motor cortex. J. Neurosci. 35, 4418–4426 (2015)
Gerfen, C. R., Paletzki, R. & Heintz, N. GENSAT BAC cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits. Neuron 80, 1368–1383 (2013)
Madisen, L. et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nature Neurosci. 15, 793–802 (2012)
Guo, Z. V. et al. Procedures for behavioral experiments in head-fixed mice. PLoS ONE 9, e88678 (2014)
Sompolinsky, H., Crisanti, A. & Sommers, H. J. Chaos in random neural networks. Phys. Rev. Lett. 61, 259–262 (1988)
Haykin, S. Adaptive Filter Theory 4th edn (Prentice Hall, 2002)
We thank B. DePasquale, A. Finkelstein, D. Gutnisky, A. Hantman, H. Inagaki, V. Jayaraman, J. Magee, S. Peron, S. Romani and N. Spruston for comments on the manuscript and discussion, T. Pluntke for animal training, A. Hu for histology, T. Harris and B. Barbarits for silicon probe recording system. This work was funded by Howard Hughes Medical Institute. N.L. and K.D. are Helen Hay Whitney Foundation postdoctoral fellows.
The authors declare no competing financial interests.
Extended data figures and tables
a, Two example ALM neurons with selectivity during the object location discrimination task, out of 890 putative pyramidal neurons from 12 mice (Methods). Correct lick-right (blue) and lick-left (red) trials only. Dashed lines demarcate behavioural epochs. Averaging window, 200 ms. b, ALM population selectivity. Top, delay epoch was 1.3 s; bottom, delay epoch was 1.7 s. Selectivity is the difference in spike rate between the preferred and non-preferred trial type, normalized to the peak selectivity (Methods). Only putative pyramidal neurons with significant trial selectivity are shown (n = 634 out of 890). In addition, neurons tested for <15 trials for each trial type (19 out of 634) were excluded. c, Average population selectivity in spike rate (black line, ± s.e.m. across neurons, bootstrap). d, Population response correlation. Pearson’s correlation between the population response vectors at different times during the task and the population response vector at the onset of the go cue (time = 0). All selective putative pyramidal neurons were used, even if not recorded at the same time (ignoring potential correlations between neurons). To equalize the contributions of individual neurons, each neuron’s response was mean-subtracted and normalized to the variance of its response across the entire trial (computed in time bins of 200 ms). e, Distribution of selectivity across the population during different epochs. For each neuron, a ROC value between lick-right and lick-left trials was computed using the spike counts during the particular behavioural epoch. Solid bars, neurons with significant trial-type selectivity (P < 0.05, two-tailed t-test using spike counts). (f-i) Monolithic models (see Methods). Each solid line represents the activity of the network’s output in response to photoinhibition. Activity does not recover after transiently silencing subsets of neurons in: Simple integrator model35 (f), Integrator with corrective feedback37 (g), Trained RNN, FORCE learning22 (h), Trained RNN, Tamed Chaos23 (i).
a, Silicon probe recording and photoinhibition in different experimental configurations used in this study. Experiment 1, data presented in Fig. 1 and Extended Data Figs 3, 4; experiment 2, data presented in Figs 2, 3, 4 and Extended Data Figs 6, 8, 9; experiment 3, data presented in Extended Data Fig. 7. b, Effect of photoinhibition on putative pyramidal neurons. For each neuron, spike rate during photoinhibition was normalized to spike rate in control trials. Left, experiment 1: n = 117, 110 and 109 neurons from 6 mice; experiment 2: n = 300, 294 and 301 from 7 mice; experiment 3: n = 52, 52 and 102 from 3 mice. Ipsilateral and bilateral photoinhibition similarly silenced neuronal activity. Average spike rate across the population was little affected by contralateral photoinhibition. Right, comparison of photoinhibition in VGAT-ChR2-EYFP mice and PV-ires-cre mice crossed to a ReaChR reporter line (Methods)45. Photoinhibition was similar in the two mouse lines (>90% activity reduction). Data from ipsilateral photoihibition from experiment 2 (n = 94 neurons from 3 VGAT mice; n = 201 from 4 PV-cre × ReaChR mice). Error bars, s.e.m. over neurons. Neurons with mean spike rate of <1 spikes s−1 were excluded. c, Top, photostimuli were shaped to minimize rebound activity after photoinhibition. Peak photostimulus intensity was gradually reduced over 200 ms during stimulus offset. Bottom, average spike rate across the population (black, control; cyan, photoinhibition). Data from experiment 2, ipsilateral photoinhibition, n = 300 neurons from 7 mice. d, Effect of photoinhibition versus distance from the laser centre under the standard photostimulus (1 laser spot). Neurons were pooled across cortical depths. Recording data were obtained from ALM of 4 untrained mice under awake and non-behaving conditions. Recording procedures were described previously3. Thin lines, individual mice (n = 246 neurons, 2 VGAT-ChR2-EYFP mice, 2 PV-ires-cre × ReaChR mice). e, Average spike rates on control versus photoinhibition lick-right trials during different epochs of the task. Data from experiment 2. Photoinhibition was for 800 ms at the beginning of the delay epoch. The delay epoch was 1.7 s. Columns from left to right: the last 400 ms of the sample epoch, the first 400 ms of the photoinhibition, the last 400 ms of the photoinhibition, the first 400 ms after photoinhibition, 400–800 ms after photoinhibition, first 400 ms of the response epoch (see a for trial structure). Top, ipsilateral photoinhibition (1 laser spot, Methods); middle, contralateral photoinhibition (1 laser spot); bottom, bilateral photoinhibition (4 laser spots). Coloured dots, neurons with significant spike rate change (P < 0.01, two tailed t-test). Crosses, population means. No rebound excitation was detected after photoinhibition offset on average (d). A small proportion of neurons showed rebound excitation which was balanced by a low level of sustained inhibition in a larger proportion of neurons. Results are similar for lick-left trials (not shown).
Extended Data Figure 3 Unilateral photoinhibition of ALM immediately before movement causes ipsilateral bias.
a, Unilateral photoinhibition of ALM during different task epochs. Sample epoch, 1.3 s; delay epoch, 1.3 s. Photoinhibition, 0.5 s (0.4 s and 0.1 s ramp, Methods). b, Performance with 0.5 s photoinhibition of left or right ALM during different trial epochs. Performance was plotted as a function of time interval between photoinhibition offset (the end of ramp offset) and the onset of go cue (Trecovery). Performance was not significantly affected for Trecovery > 0.3 s. Thick lines, mean; thin lines, individual mice (n = 5). *P < 0.05, **P < 0.01, ***P < 0.001, two-tailed t-test. c, Unilateral photoinhibition of ALM during different task epochs. Sample epoch, 1.3 s; delay epoch, variable duration, 1.2–1.7 s in 0.1-s increments. Trials with different delay epoch durations were randomly interleaved. Photoinhibition was for 1.3 s (1.2 s and 0.1 s ramp, Methods), resulting in different Trecovery. d. Performance with 1.3 s photoinhibition. Plot is similar to b. Performance was not significantly affected for Trecovery > 0.3 s. e. Photoinhibition (0.5 s) immediately before the go cue is similar to the behavioural effect caused by photoinhibition during the entire delay epoch (1.3 s). Photoinhibition data at Trecovery = 0 from b and d was re-plotted.
Extended Data Figure 4 ALM neurons with decreasing spike rates during the delay epoch recovered their normal spike rates after unilateral photoinhibition.
a, Three example ALM neurons with decreasing spike rates during the delay epoch. Top, spike raster. Bottom, PSTH. All lick-right (blue) and lick-left (red) trials. Dashed lines, behavioural epochs. Blue shades, photoinhibition. b, Normalized spike rate for all neurons with significant spike rate decrease at the end of the delay epoch compared to the beginning of the delay epoch (P < 0.05, two-tailed t-test; 400 ms windows; pooled across trial types). 27 neurons from 6 mice. The spike rate for each neuron was normalized to the mean spike rate. Blue, preferred trial type; red, non-preferred. Mean ± s.e.m. across neurons, bootstrap. Dotted lines, spike rates in control trials. c, The data are consistent with a return to the normal trajectory and inconsistent with decay to the end point. Top, spike rate difference between perturbed trials and the time-matched spike rates in control trials. Bottom, spike rate difference between perturbed trials and the spike rates at the end of the delay epoch in control trials. Data from b. Mean ± s.e.m. across neurons, bootstrap. Spike rate difference relative to time-matched control show significantly smaller root mean squared error (r.m.s.e.) than spike rate difference relative to end point (P < 0.001, paired t-test). r.m.s. was computed during the epoch between photoinhibition offset and the go cue.
a, Left, silicon probe recording during unilateral photoactivation of a subset of excitatory neurons. Tlx_PL56-Cre mice were crossed to Ai32 (Rosa26-ChR2) reporter mice to express ChR2 in layer 5 intratelencephalic (IT) neurons46. Right, task structure and timing of photoactivation (cyan). b, Top, photostimulus. Bottom, average spike rate across the population (n = 69 neurons from 2 mice). Black, control; cyan, photoactivation. Rebound inhibition was observed after photoactivation. c, Effect of photoactivation on spike rates. Data are for photoactivation during early delay epoch. Black circles, neurons with significant spike rate change (P < 0.01, two tailed t-test). Photoactivation during sample epoch: 19% excited, 22% suppressed; late delay epoch: 15% excited, 17% suppressed. Lick-right and lick-left trials were pooled to compute spike rates. d, Three example ALM neurons. Top, spike raster. Bottom, PSTH. All lick-right (blue) and lick-left (red) trials. Dashed lines, behavioural epochs. Blue shades, photoinhibition. e, Top, significant spike rate changes relative to control are highlighted for individual neurons. Neurons (rows) are sorted based on their mean spike rate across the trial epochs. Neurons with mean spike rate below 1 spikes s−1 or tested for less than 3 trials are excluded. Middle, fraction of neurons with significant spike rate change (n = 43, 44 from 2 mice). Bottom, average spike rate across the population. f, Average population selectivity change from control (Δselectivity ± s.e.m. across neurons, bootstrap). Only selective neurons tested for >3 trials in all conditions are shown (n = 26). Green lines, time points when the selectivity recovered to 80% of control selectivity (mean ± s.e.m. across neurons, bootstrap). Sample epoch: 249 ± 68 ms to recover to 80% of control selectivity; early delay: 275 ± 168 ms; middle delay: 250 ± 218 ms.
Extended Data Figure 6 ALM dynamics predicts upcoming movements at the level of behavioural sessions.
a, Behavioural performance on control and bilateral photoinhibition trials. b, Time course of activity trajectories projected onto the coding direction (CD). Dotted lines, average trajectories from control lick-right (blue) and lick-left (red) trials. Solid lines, average trajectories from bilateral photoinhibition trials. Each plot shows data from one session for one mouse. Trajectories in photoinhibition trials were similar to control trials before photoinhibition and were persistently altered by transient bilateral photoinhibition. The resultant trajectories were inconsistent from session to session: in some cases the altered trajectories were closer to the lick-right control trajectories (blue dotted lines), and in other cases closer to the lick-left control trajectories (red dotted lines). Averaging window, 400 ms. In sessions with altered activity trajectories that were closer to the control lick-left trajectories, movements were biased to the left, resulting in high performance in lick-left trials and low performance in lick-right trials (session 1, 3, 4). The opposite behavioural bias was observed when altered activity trajectories were closer to the control lick-right trajectories (session 2, 5). The biases in movement were predicted based ALM activity trajectories. Session 1–5, n = 20, 16, 18, 10 and 12 neurons.
a, Silicon probe recording during unilateral (4 laser spots) and bilateral (1 laser spot; red box) photoinhibition. b, Behavioural performance. Bar, mean across all mice (n = 3). Symbols, individual mice (mean ± s.e.m., bootstrap). c, Top, significant spike rate changes for individual neurons (black). Neurons (rows) are sorted based on their mean spike rate across the trial epochs. Neurons with mean spike rate below 1 spike s−1 or tested for less than 3 trials are excluded (n = 60, 59 and 60). Photoinhibition is indicated on the top. Bottom, fraction of neurons with significant spike rate change. d, Average population selectivity change from control (Δselectivity ± s.e.m. across neurons, bootstrap). Only selective neurons tested for >3 trials in all conditions are shown (n = 40). Green lines, time points when the selectivity recovered to 80% of control selectivity (mean ± s.e.m. across neurons, bootstrap). Ipsilateral: 490 ± 280 ms to recover to 80% of control selectivity; contralateral: 235 ± 156 ms; bilateral: no recovery at end of delay period. e, Time course of activity trajectories on lick-right (blue) and lick-left (red) trials projected onto the coding direction (CD). Average trajectories from all sessions (±s.e.m. across sessions, bootstrap, Methods). From left to right panels: control trials, ipsilateral photoinhibition (4 laser spots), contralateral photoinhibition (4 laser spots), and bilateral photoinhibition (1 laser spot). Dotted line, trajectories in control trials. Only sessions with >5 simultaneously recorded neurons tested for >3 trials in each condition. We quantified the separation between trajectories at the end of delay epoch by computing ROC values for each session: control, 0.80 ± 0.08; ipsilateral, 0.64 ± 0.10; contralateral, 0.68 ± 0.15; bilateral, 0.54 ± 0.8. Mean ± s.e.m. across sessions, Methods.
a, Decomposition of activity into five modes based on control trials and ipsilateral perturbations (Methods). Fraction of activity variance (left) and selectivity (right) explained by modes 1–5. The overlap in variance and selectivity between mode 1 and modes 2–3 are highlighted in black. Error bars, s.e.m. across sessions. Data from 16 sessions, 7 mice. Activity variance here is computed using trial-averaged activity (Methods), thus they reflect variance across time and neurons. Activity variance across trials is not reflected. The fraction of variance explained for the single-trial activity would be much lower. b, Fraction of upcoming movements predicted based on modes 1–5. Trajectory distance from the decision boundary at the time of the go cue is used to predict behaviour. Lick-right and lick-left trials are pooled. Error bars, s.e.m. across sessions. c, Projections of activity along modes 1–5 for ipsilateral perturbation trials (solid). Dashed blue and red lines correspond to the means for control trials. Error bars, s.e.m. across sessions. For the CD mode, a different set of trials was used here to compute CD compared to Fig. 3c (Methods). This resulted in small differences in the projected trajectories. d, Projections of activity in the same dimensions as in c for contralateral perturbation trials. e, Projections of activity in the same dimensions as in c for bilateral perturbation trials. f, Weights of each neuron for mode 1 versus modes 2–5. Mode 1 and modes 2–5 involve overlapping populations of neurons. Data from all sessions were pooled. Note that the ramping modes (4 and 5) are resistant to all perturbations, including bilateral perturbations, suggesting that overall ramping may be driven by a source external to ALM. ROC values between trajectories along the CD mode at the end of delay epoch: control, 0.76 ± 0.03; ipsilateral, 0.73 ± 0.02; contralateral, 0.74 ± 0.03; bilateral 0.58 ± 0.03. ROC values during the time period of photoinhibition: control, 0.72 ± 0.02; ipsilateral, 0.54 ± 0.03; contralateral, 0.64 ± 0.03; bilateral 0.54 ± 0.01.
a, Schematic of trajectory analysis in activity space. The difference in the mean response vectors between lick-right and lick-left trials, w , was estimated across different time windows (400 ms) during sample and delay epochs. b, w values are similar during sample and delay epoch. Correlation of w values across time. Data from 16 sessions, 7 mice. The coding direction (CD) was taken as the average w value over time. c, The recovery of ALM dynamics along the coding direction (CD) is robust to the choice of time window for the calculation of CD. Left, CD was the average w value from the first 400 ms of the delay epoch. Right, CD was the average w value from the last 400 ms of the delay epoch. d, The recovery of ALM dynamics along CD is robust across mice. e, Behavioural performance in lick-right and lick-left trials as a function of trajectory distance from the decision boundary at the time of the go cue. Positive values on the x axis indicate closer distance to the control lick-right trajectory. From left to right panels: control trials, ipsilateral photoinhibition trials, contralateral photoinhibition trials, and bilateral photoinhibition trials. Performance was computed by binning along the CD distance (bin size, 4 on the CD distance scale). s.e.m. was obtained by bootstrapping the trials in each bin. f, Reaction times are faster on trials in which the trajectory is far from the decision boundary at the time of the go cue. ΔReaction time is relative to the mean reaction time from each session. Data from 16 sessions, 7 mice. Data from lick-right and lick-left trials were pooled.
a, Schematic. Corpus callosum (CC) was bisected while sparing the pyramidal tract (PT) and corticothalamic (CT) projections. b, Behavioural performance. Bar, mean across all mice (n = 7). Symbols, individual mice (mean ± s.e.m., bootstrap). Performance was not affected by the corpus callosum bisection. First session was ~17 h after the corpus callosum bisection. c, Location of the corpus callosum cut superimposed on axonal projections from ALM. AAV2/1-CAG-EGFP was injected into ALM. A vertical cut ~3.5 mm deep was made approximately 0.5 mm from the mid-line. The cut extended from bregma anterior 1.5 mm to posterior 1 mm. The cut was either made in the left hemisphere (3 mice) or the right hemisphere (4 mice). The cut spared the pyramidal tract and corticothalamic axons. d, Coronal section showing the corpus callosum bisection in 6 mice. Left, autofluoresence; right, GFAP immunofluorescence (Methods). e, ALM shows normal preparatory activity after the corpus callosum bisection. ALM population selectivity. Selectivity is the difference in spike rate between the preferred and non-preferred trial type, normalized to the peak selectivity (Methods). Only putative pyramidal neurons with significant trial selectivity are shown (n = 254 out of 496). In addition, 11 out of 254 neurons tested for <15 trials for each trial type were excluded. f, Average population selectivity in spike rate (black line, ± s.e.m. across neurons, bootstrap). g, Proportion of contra-preferring vs. ipsi-preferring neurons. Error bars, s.e.m. across mice, bootstrap.
About this article
Cite this article
Li, N., Daie, K., Svoboda, K. et al. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016). https://doi.org/10.1038/nature17643
National Science Review (2020)
Journal of Cheminformatics (2020)
Cerebral Cortex (2020)