Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-020-80132-4
Title: A functional spiking neuronal network for tactile sensing pathway to process edge orientation
Authors: Parvizi-Fard, Adel
Amiri, Mahmood
Kumar, Deepesh 
Iskarous, Mark M.
Thakor, Nitish, V 
Issue Date: 14-Jan-2021
Publisher: Nature Research
Citation: Parvizi-Fard, Adel, Amiri, Mahmood, Kumar, Deepesh, Iskarous, Mark M., Thakor, Nitish, V (2021-01-14). A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Scientific Reports 11 (1) : 1320. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-020-80132-4
Rights: Attribution 4.0 International
Abstract: To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications. © 2021, The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/232357
ISSN: 2045-2322
DOI: 10.1038/s41598-020-80132-4
Rights: Attribution 4.0 International
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