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Real-time estimation and biofeedback of single-neuron firing rates using local field potentials

Author

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  • Thomas M. Hall

    (Institute of Neuroscience, Medical School, Newcastle University)

  • Kianoush Nazarpour

    (Institute of Neuroscience, Medical School, Newcastle University
    School of Electrical and Electronic Engineering, Newcastle University)

  • Andrew Jackson

    (Institute of Neuroscience, Medical School, Newcastle University)

Abstract

The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain–machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.

Suggested Citation

  • Thomas M. Hall & Kianoush Nazarpour & Andrew Jackson, 2014. "Real-time estimation and biofeedback of single-neuron firing rates using local field potentials," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6462
    DOI: 10.1038/ncomms6462
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