Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Using multi-neuron population recordings for neural prosthetics

Nature Neuroscience volume 7, pages 452–455 (2004)Cite this article

Abstract

Classical single-neuron recording methods led to 'neuron-centric' concepts of neural coding, whereas more recent multi-neuron population recordings have inspired 'population-centric' concepts of distributed processing in neural systems. Because most neocortical neurons code information coarsely, sensory or motor processing tends to be widely distributed across neuronal populations. Dynamic fluctuations in neural population functions thus involve subtle changes in the overall pattern of neural activity. Mathematical analysis of neural population codes allows extraction of 'motor signals' from neuronal population recordings in the motor cortices, which can then be used in real-time to directly control movement of a robot arm. This technique holds promise for the development of neurally controlled prosthetic devices and provides insights into how information is distributed across several brain regions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Neuronal population response maps.
Figure 2: Interactions between sensory stimuli and brain dynamics in neural populations.
Figure 3: Brain-controlled neural prosthesis.

Similar content being viewed by others

References

  1. Chapin, J.K. & Lin, C-S. Mapping the body representation in the SI cortex of anesthetized and awake rats. J. Comp. Neurol. 229, 199–213 (1984).

    Article  CAS  Google Scholar 

  2. Chapin, J.K. Laminar differences in sizes, shapes and response profiles of cutaneous receptive fields in the rat SI cortex. Exp. Brain Res. 262, 549–559 (1986).

    Google Scholar 

  3. Nicolelis, A.L. et al. Simultaneous representation of tactile information by three primate cortical areas. Nat. Neurosci. 1, 621–630 (1998).

    Article  CAS  Google Scholar 

  4. Chapin, J.K. & Woodward, D.J. Modulation of sensory responsiveness of single somatosensory cortical cells during movement and arousal behaviors. Exp. Neurol. 72, 164–178 (1981).

    Article  CAS  Google Scholar 

  5. Chapin, J.K. & Woodward, D.J. Somatic sensory transmission to the cortex during movement: II. Phasic modulation over the locomotor step cycle. Exp. Neurol. 78, 670–684 (1982).

    Article  CAS  Google Scholar 

  6. Rumelhart, D.E., Hinton, G.E. & McClelland, J.L. A general framework for parallel distributed processing. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations (eds. Rumelhart, D.E., McClelland, J.L. and the PDP Research Group) (MIT Press, Cambridge, Massachusettes, 1986).

    Google Scholar 

  7. Lilly, J.C. Correlations between neurophysiological activity in the cortex and short-term behavior in the monkey. in Biological and Biochemical Bases of Behavior (ed. Woolsey, H.H.C.) (Univ. Wisconsin Press, Madison, Wisconsin, 1958).

    Google Scholar 

  8. Kruger, J. Simultaneous individual recordings from many cerebral neurons: techniques and results. Rev. Physiol. Biochem. Pharmacol. 98, 177–233 (1983).

    Article  CAS  Google Scholar 

  9. McNaughton, B.L., O'Keefe, J. & Barnes, C.A. The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J. Neurosci. Methods 8, 391–397 (1983).

    Article  CAS  Google Scholar 

  10. Nicolelis, M.A.L., Baccala, L.A., Lin, C-S. & Chapin, J.K. Synchronous neuronal ensemble activity at multiple levels of the rat somatosensory system anticipates onset and frequency of tactile exploratory movements. Science 268, 1353–1358 (1995).

    Article  CAS  Google Scholar 

  11. Deadwyler, S.A. & Hampson, R.E. Ensemble activity and behavior – what's the code. Science 270, 1316–1318 (1995).

    Article  CAS  Google Scholar 

  12. Laubach, M., Wessberg, J. & Nicolelis, M.A. Cortical ensemble activity increasingly predicts behaviour outcomes during learning of a motor task. Nature 405, 567–571 (2000).

    Article  CAS  Google Scholar 

  13. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361–365 (2000).

    Article  CAS  Google Scholar 

  14. Taylor, D.M., Tillery, S.I. & Schwartz, A.B. Direct cortical control of 3D neuroprosthetic devices. Science 296, 1829–1832 (2002).

    Article  CAS  Google Scholar 

  15. Moxon, K.A., Leiser, S.L., Gerhardt, G.A. & Chapin, J.K. Ceramic-based multisite electrode arrays for chronic single-neuron recording. IEEE Trans. Biomed. Eng. 51, 647–656 (2004).

    Article  Google Scholar 

  16. Chapin, J.K. Population-level analysis of multi-single neuron recording data: multivariate statistical methods. in Many-Neuron Recording (ed. Nicolelis, M.) (CRC Press, 1998).

    Google Scholar 

  17. Chapin, J.K. & Woodward, D.J. Ethanol withdrawal increases sensory responsiveness of single somatosensory cortical neurons in the awake, behaving rat. Alcoholism 13, 8–14 (1989).

    Article  CAS  Google Scholar 

  18. Patel, I.M. & Chapin, J.K. Ketamine effects on somatosensory cortical single neurons in behaving rats. Anesth. Analg. 70, 635–644 (1990).

    Article  CAS  Google Scholar 

  19. Shin H-C., Park, H.J., Oh, Y.S. & Chapin, J.K. Cocaine-induced changes in functional connectivities between simultaneously recorded single neurons in the SI cortex and the VPL thalamus of conscious rats. Kor. J. Physiol. 27, 79–91 (1993).

    CAS  Google Scholar 

  20. Smith, S.S. & Chapin, J.K. The estrous cycle and the olivo-cerebellar circuit: I. Contrast enhancement of sensorimotor-correlated Purkinje cell discharge. Exp. Brain Res. 111, 371–384 (1996).

    Article  CAS  Google Scholar 

  21. Smith, S.S. & Chapin, J.K. The estrous cycle and the olivo-cerebellar circuit: II. Enhanced selective sensory gating of responses from the rostral dorsal accessory olive. Exp. Brain Res. 111, 385–392 (1996).

    Article  CAS  Google Scholar 

  22. Nicolelis, M.A.L., Lin, C-S., Woodward, D.J. & Chapin, J.K. Peripheral block of ascending cutaneous information induces immediate spatio-temporal changes in thalamic networks. Nature 361, 533–536 (1993).

    Article  CAS  Google Scholar 

  23. Brown, E.N., Kass, R.E. & Mitra, P.P. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456–461 (2004).

    Article  CAS  Google Scholar 

  24. Chapin, J.K. & Nicolelis, M.A.L. Beyond single unit recording: characterizing neural information in networks of simultaneously recorded neurons. in Scale in Conscious Experience: Is the Brain Too Important to Be Left to Specialists to Study? (eds. King, J. & Pribram, K.) (Laurence Erlbaum Assoc., Mahwah, NJ, 1995).

    Google Scholar 

  25. Chapin, J.K & Nicolelis, M.A.L. Population coding in simultaneously recorded neuronal ensembles in ventral posteromedial (VPM) thalamus: multidimensional sensory representations and population vectors. J. Neurosci. Methods 94, 121–140 (1999).

    Article  CAS  Google Scholar 

  26. Nicolelis, M.A.L. & Chapin, J.K. Building brain-machine interfaces to restore and augment human motor performance. Sci. Am. 4, 45–52 (2002).

    Google Scholar 

  27. Chapin, J.K., Markowitz, R.A., Moxon, K.A. & Nicolelis, M.A.L. Direct real-time control of a robot arm using signals derived from neuronal population recordings in motor cortex. Nat. Neurosci. 2, 664–670 (1999).

    Article  CAS  Google Scholar 

  28. Isaacs, R.E., Weber, D.J. & Schwartz, A.B. Work toward real-time control of a cortical neural prosthesis. IEEE Trans. Rehabil. Eng. 8, 196–198 (2000).

    Article  CAS  Google Scholar 

  29. Donoghue, J.P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 1085–1088 (2002).

    Article  CAS  Google Scholar 

  30. Nicolelis, M.A.L. et al. Chronic, multisite, multielectrode recordings in macaque monkeys. Proc. Natl. Acad. Sci. USA 100, 11041–11046 (2003).

    Article  CAS  Google Scholar 

  31. Carmena, J.M. et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS. Biol. 1, E42 (2003).

    Article  Google Scholar 

  32. Patil, P.G., Carmena, J.M., Nicolelis, M.A.L. & Turner, D.A. Ensembles of subcortical neurons as a source of motor control signals for a brain-machine interface in humans. Neurosurgery (in press).

  33. Talwar, S.K. et al. “Virtual” operant conditioning using brain microstimulation facilitates training of complex behaviors in freely roaming animals. Nature 417, 37–38 (2002).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH grants NS2672213, NS4054303 & NS24707, DARPA grants 02SCNSF1015 & N6600103C8035 and James McDonnell Foundation grant 21002018.

Author information

Authors and Affiliations

  1. Department of Physiology and Pharmacology, Center for Neurorobotics and Neuroengineering, State University of New York Downstate Medical Center, 450 Clarkson Ave, Brooklyn, 11203, New York, USA

    John K Chapin

Authors
  1. John K Chapin
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Competing interests

The author declares no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chapin, J. Using multi-neuron population recordings for neural prosthetics. Nat Neurosci 7, 452–455 (2004). https://doi.org/10.1038/nn1234

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1234

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing