Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior

Behavior relies on continuous influx of sensory information about the body and the environment. In primates, cortex integrates somatic feedback to accurately reach and manipulate objects. Yet, in many experimental regimes motor cortex seems paradoxically to operate as a feedforward, rather than feedback-driven, system. Here, we recorded simultaneously from motor and somatosensory cortex as monkeys performed a naturalistic reaching and object interaction behavior. We studied how unexpected feedback from behavioral errors influences cortical dynamics. Motor cortex generally exhibited robust feedforward dynamics, yet displayed feedback-driven dynamics surrounding correction of behavioral errors. We then decomposed motor cortical activity into orthogonal subspaces capturing communication with somatosensory cortex or behavior-generating dynamics. During error correction, the communication subspace became feedback-driven, while the behavioral subspace maintained feedforward dynamics. We therefore demonstrate that cortical activity is compartmentalized within distinct subspaces that shape the population dynamics, enabling flexible integration of salient inputs with ongoing activity for robust behavior.

[1]  P. Strick,et al.  Motor areas in the frontal lobe of the primate , 2002, Physiology & Behavior.

[2]  J. Andrew Pruszynski,et al.  Primary motor cortex underlies multi-joint integration for fast feedback control , 2011, Nature.

[3]  Naoshige Uchida,et al.  Demixed principal component analysis of neural population data , 2014, eLife.

[4]  Matthew G Perich,et al.  Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning , 2017, Experimental Brain Research.

[5]  Bamdev Mishra,et al.  Manopt, a matlab toolbox for optimization on manifolds , 2013, J. Mach. Learn. Res..

[6]  Mark M Churchland,et al.  Vacillation, indecision and hesitation in moment-by-moment decoding of monkey motor cortex , 2015, eLife.

[7]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[8]  Hansjörg Scherberger,et al.  Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning , 2016, PLoS Comput. Biol..

[9]  Eduardo Martin Moraud,et al.  Configuration of electrical spinal cord stimulation through real-time processing of gait kinematics , 2018, Nature Protocols.

[10]  Matthew T. Kaufman,et al.  Neural population dynamics during reaching , 2012, Nature.

[11]  Devika Narain,et al.  Flexible timing by temporal scaling of cortical responses , 2017, Nature Neuroscience.

[12]  Stephen I. Ryu,et al.  Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions , 2017, Neuron.

[13]  Christian Ethier,et al.  Cortical population activity within a preserved neural manifold underlies multiple motor behaviors , 2018, Nature Communications.

[14]  J. Cunningham,et al.  Different population dynamics in the supplementary motor area and motor cortex during reaching , 2018, Nature Communications.

[15]  Matthew T. Kaufman,et al.  The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type , 2016, eNeuro.

[16]  P. Strick,et al.  Subdivisions of primary motor cortex based on cortico-motoneuronal cells , 2009, Proceedings of the National Academy of Sciences.

[17]  Lee E. Miller,et al.  Neural Manifolds for the Control of Movement , 2017, Neuron.

[18]  Stephen H Scott,et al.  Independent representations of ipsilateral and contralateral limbs in primary motor cortex , 2019, eLife.

[19]  E E Fetz,et al.  Corticomotoneuronal cells contribute to long‐latency stretch reflexes in the rhesus monkey. , 1984, The Journal of physiology.

[20]  Mark M Churchland,et al.  Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response , 2019, eLife.

[21]  Lee E. Miller,et al.  Long-term stability of cortical population dynamics underlying consistent behavior , 2019, Nature Neuroscience.

[22]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[23]  S. Scott,et al.  Feedback control during voluntary motor actions , 2015, Current Opinion in Neurobiology.

[24]  Silvestro Micera,et al.  A versatile robotic platform for the design of natural, three-dimensional reaching and grasping tasks in monkeys. , 2019, Journal of neural engineering.

[25]  N. Hatsopoulos,et al.  Sensing with the Motor Cortex , 2011, Neuron.

[26]  V. Jayaraman,et al.  Intensity versus Identity Coding in an Olfactory System , 2003, Neuron.

[27]  Konrad Paul Kording,et al.  Single reach plans in dorsal premotor cortex during a two-target task , 2018, Nature Communications.

[28]  Byron M. Yu,et al.  Dimensionality reduction for large-scale neural recordings , 2014, Nature Neuroscience.

[29]  Matthew T. Kaufman,et al.  A neural network that finds a naturalistic solution for the production of muscle activity , 2015, Nature Neuroscience.

[30]  Bijan Pesaran,et al.  Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification (PSID) , 2019, bioRxiv.

[31]  Matthew T. Kaufman,et al.  Supplementary materials for : Cortical activity in the null space : permitting preparation without movement , 2014 .

[32]  Lee E. Miller,et al.  A Neural Population Mechanism for Rapid Learning , 2017, Neuron.

[33]  John P. Cunningham,et al.  Reorganization between preparatory and movement population responses in motor cortex , 2016, Nature Communications.

[34]  R L Sainburg,et al.  Control of limb dynamics in normal subjects and patients without proprioception. , 1995, Journal of neurophysiology.

[35]  Kristin Branson,et al.  Cortical pattern generation during dexterous movement is input-driven , 2019, Nature.

[36]  Byron M. Yu,et al.  Neural constraints on learning , 2014, Nature.

[37]  S. Scott.  Optimal feedback control and the neural basis of volitional motor control , 2004, Nature Reviews Neuroscience.

[38]  B. Pakkenberg,et al.  Aging and the human neocortex , 2003, Experimental Gerontology.

[39]  Byron M. Yu,et al.  Cortical Areas Interact through a Communication Subspace , 2019, Neuron.

[40]  Abigail A. Russo,et al.  Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response , 2018, Neuron.