Journal of Computational Neuroscience

, Volume 32, Issue 3, pp 555–561

Redundant information encoding in primary motor cortex during natural and prosthetic motor control

  • Kelvin So
  • Karunesh Ganguly
  • Jessica Jimenez
  • Michael C. Gastpar
  • Jose M. Carmena
Article

Abstract

Redundant encoding of information facilitates reliable distributed information processing. To explore this hypothesis in the motor system, we applied concepts from information theory to quantify the redundancy of movement-related information encoded in the macaque primary motor cortex (M1) during natural and neuroprosthetic control. Two macaque monkeys were trained to perform a delay center-out reaching task controlling a computer cursor under natural arm movement (manual control, ‘MC’), and using a brain-machine interface (BMI) via volitional control of neural ensemble activity (brain control, ‘BC’). During MC, we found neurons in contralateral M1 to contain higher and more redundant information about target direction than ipsilateral M1 neurons, consistent with the laterality of movement control. During BC, we found that the M1 neurons directly incorporated into the BMI (‘direct’ neurons) contained the highest and most redundant target information compared to neurons that were not incorporated into the BMI (‘indirect’ neurons). This effect was even more significant when comparing to M1 neurons of the opposite hemisphere. Interestingly, when we retrained the BMI to use ipsilateral M1 activity, we found that these neurons were more redundant and contained higher information than contralateral M1 neurons, even though ensembles from this hemisphere were previously less redundant during natural arm movement. These results indicate that ensembles most associated to movement contain highest redundancy and information encoding, which suggests a role for redundancy in proficient natural and prosthetic motor control.

Keywords

Mutual information Neural ensemble Motor control Brain-machine interface Electrophysiology Primary motor cortex 

Supplementary material

10827_2011_369_MOESM1_ESM.docx (25 kb)
ESM 1(DOCX 25 kb)

References

  1. Abbott, L. F., & Dayan, P. (1999). The effect of correlated variability on the accuracy of a population code. Neural Computation, 11, 91–101.PubMedCrossRefGoogle Scholar
  2. Ashe, J., & Georgopoulos, A. P. (1994). Movement parameters and neural activity in motor cortex and area 5. Cerebral Cortex, 4, 590–600.PubMedCrossRefGoogle Scholar
  3. Averbeck, B. B., & Lee, D. (2006). Effects of noise correlations on information encoding and decoding. Journal of Neurophysiology, 95, 3633.PubMedCrossRefGoogle Scholar
  4. Barlow, H. (2001). Redundancy reduction revisited. Network, 12, 241–253.PubMedGoogle Scholar
  5. Carmena, J. M., Lebedev, M. A., Crist, R. E., O’Doherty, J. E., Santucci, D. M., Dimitrov, D. F., et al. (2003). Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biology, 1, 193–208.CrossRefGoogle Scholar
  6. Fetz, E. E., & Cheney, P. D. (1980). Postspike facilitation of forelimb muscle activity by primate corticomotoneuronal cells. Journal of Neurophysiology, 44, 751–772.PubMedGoogle Scholar
  7. Fu, Q. G., Flament, D., Coltz, J. D., & Ebner, T. J. (1995). Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. Journal of Neurophysiology, 73, 836–854.PubMedGoogle Scholar
  8. Ganguly, K., & Carmena, J. M. (2009). Emergence of a stable cortical map for neuroprosthetic control. PLoS Biology, 7, e1000153.PubMedCrossRefGoogle Scholar
  9. Gawne, T. J., & Richmond, B. J. (1993). How independent are the messages carried by adjacent inferior temporal cortical neurons? The Journal of Neuroscience, 13, 2758–2771.PubMedGoogle Scholar
  10. Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., & Massey, J. T. (1982). On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. The Journal of Neuroscience, 2, 1527–1537.PubMedGoogle Scholar
  11. Hatsopoulos, N. G., Ojakangas, C. L., Paninski, L., & Donoghue, J. P. (1998). Information about movement direction obtained from synchronous activity of motor cortical neurons. Proceedings of the National Academy of Sciences of the United States of USA, 95, 15706–15711.CrossRefGoogle Scholar
  12. Jarosiewicz, B., Chase, S. M., Fraser, G. W., Velliste, M., Kass, R. E., & Schwartz, A. B. (2008). Functional network reorganization during learning in a brain-computer interface paradigm. Proceedings of the National Academy of Sciences of the United States of USA, 105, 19486.CrossRefGoogle Scholar
  13. Johnson, M. T., Mason, C. R., & Ebner, T. J. (2001). Central processes for the multiparametric control of arm movements in primates. Current Opinion in Neurobiology, 11, 684–688.PubMedCrossRefGoogle Scholar
  14. Kleim, J. A., Barbay, S., & Nudo, R. J. (1998). Functional reorganization of the rat motor cortex following motor skill learning. Journal of Neurophysiology, 80, 3321.PubMedGoogle Scholar
  15. Latham, P. E., & Nirenberg, S. (2005). Synergy, redundancy, and independence in population codes, revisited. The Journal of Neuroscience, 25, 5195.PubMedCrossRefGoogle Scholar
  16. Maynard, E. M., Hatsopoulos, N. G., Ojakangas, C. L., Acuna, B. D., Sanes, J. N., Normann, R. A., et al. (1999). Neuronal interactions improve cortical population coding of movement direction. The Journal of Neuroscience, 19, 8083–8093.PubMedGoogle Scholar
  17. Narayanan, N. S., Kimchi, E. Y., & Laubach, M. (2005). Redundancy and synergy of neuronal ensembles in motor cortex. The Journal of Neuroscience, 25, 4207–4216.PubMedCrossRefGoogle Scholar
  18. Nudo, R. J., Milliken, G. W., Jenkins, W. M., & Merzenich, M. M. (1996). Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. The Journal of Neuroscience, 16, 785–807.PubMedGoogle Scholar
  19. Oram, M. W., Hatsopoulos, N. G., Richmond, B. J., & Donoghue, J. P. (2001). Excess synchrony in motor cortical neurons provides redundant direction information with that from coarse temporal measures. Journal of Neurophysiology, 86, 1700–1716.PubMedGoogle Scholar
  20. Quian Quiroga, R., & Panzeri, S. (2009). Extracting information from neuronal populations: information theory and decoding approaches. Nature Reviews Neuroscience, 10, 173–185.PubMedCrossRefGoogle Scholar
  21. Schneidman, E., Bialek, W., & Berry, M. J. (2003). Synergy, redundancy, and independence in population codes. The Journal of Neuroscience, 23, 11539–11553.PubMedGoogle Scholar
  22. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., & Donoghue, J. P. (2002). Brain-machine interface: instant neural control of a movement signal. Nature, 416, 141–142.PubMedCrossRefGoogle Scholar
  23. Shadlen, M. N., & Newsome, W. T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. The Journal of Neuroscience, 18, 3870–3896.PubMedGoogle Scholar
  24. Shamir, M., & Sompolinsky, H. (2011). Nonlinear population codes. Neural Computation, 16, 1105–1136.CrossRefGoogle Scholar
  25. Thach, W. T. (1978). Correlation of neural discharge with pattern and force of muscular activity, joint position, and direction of intended next movement in motor cortex and cerebellum. Journal of Neurophysiology, 41, 654–676.PubMedGoogle Scholar
  26. Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 370, 140–143.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Kelvin So
    • 1
  • Karunesh Ganguly
    • 2
    • 3
  • Jessica Jimenez
    • 1
  • Michael C. Gastpar
    • 1
    • 4
  • Jose M. Carmena
    • 1
    • 5
    • 6
    • 7
  1. 1.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA
  2. 2.San Francisco VA Medical CenterSan FranciscoUSA
  3. 3.Department of NeurologyUniversity of CaliforniaSan FranciscoUSA
  4. 4.School of Computer and Communication Sciences, Ecole Polytechnique Fédérale (EPFL)LausanneSwitzerland
  5. 5.Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyUSA
  6. 6.UCB/UCSF Joint Graduate Group in BioengineeringUniversity of CaliforniaBerkeleyUSA
  7. 7.Program in Cognitive ScienceUniversity of CaliforniaBerkeleyUSA

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