4.7 Article

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

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SCIENTIFIC REPORTS
卷 7, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/srep45898

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资金

  1. Italian Ministry of Foreign Affairs and International Cooperation
  2. Directorate General for Country Promotion (Economy, Culture and Science) - Unit for Scientific and Technological Cooperation
  3. Swedish Research Council, via the Italy-Sweden bilateral research SensBrain [J52I15000030005]
  4. EU [FET 611687, FP7-NMP 228844]
  5. national project PRIN/HandBot [B81J12002680008]
  6. Swedish Research Council [K2014-63X-14780-12-3]

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Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra-and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

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