Literature DB >> 21911056

Encoding/decoding of first and second order tactile afferents in a neurorobotic application.

Luca Leonardo Bologna1, Jérémie Pinoteau, Romain Brasselet, Marco Maggiali, Angelo Arleo.   

Abstract

We present a neurorobotic framework to investigate tactile information processing at the early stages of the somatosensory pathway. We focus on spatiotemporal coding of first and second order responses to Braille stimulation, which offers a suitable protocol to investigate the neural bases of fine touch discrimination. First, we model Slow Adaptive type I fingertip mechanoreceptor responses to Braille characters sensed both statically and dynamically. We employ a network of spiking neurones to transduce analogue skin deformations into primary spike trains. Then, we model second order neurones in the cuneate nucleus (CN) of the brainstem to study how mechanoreceptor responses are possibly processed prior to their transmission to downstream central areas. In the model, the connectivity layout of mechanoreceptor-to-cuneate projections produces a sparse CN code. To characterise the reliability of neurotransmission we employ an information theoretical measure accounting for the metrical properties of spiking signals. Our results show that perfect discrimination of primary and secondary responses to a set of 26 Braille characters is achieved within 100 and 500 ms of stimulus onset, in static and dynamic conditions, respectively. Furthermore, clusters of responses to different stimuli are better separable after the CN processing. This finding holds for both statically and dynamically delivered stimuli. In the presented system, when sliding the artificial fingertip over a Braille line, a speed of 40-50mm/s is optimal in terms of rapid and reliable character discrimination. This result is coherent with psychophysical observations reporting average reading speeds of 30-40±5 mm/s adopted by expert Braille readers.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21911056     DOI: 10.1016/j.jphysparis.2011.08.002

Source DB:  PubMed          Journal:  J Physiol Paris        ISSN: 0928-4257


  4 in total

1.  Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Authors:  Francisco Naveros; Jesus A Garrido; Richard R Carrillo; Eduardo Ros; Niceto R Luque
Journal:  Front Neuroinform       Date:  2017-02-07       Impact factor: 4.081

2.  A Digital Hardware System for Spiking Network of Tactile Afferents.

Authors:  Nima Salimi-Nezhad; Erfan Ilbeigi; Mahmood Amiri; Egidio Falotico; Cecilia Laschi
Journal:  Front Neurosci       Date:  2020-01-14       Impact factor: 4.677

3.  Learning touch preferences with a tactile robot using dopamine modulated STDP in a model of insular cortex.

Authors:  Ting-Shuo Chou; Liam D Bucci; Jeffrey L Krichmar
Journal:  Front Neurorobot       Date:  2015-07-22       Impact factor: 2.650

4.  A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor.

Authors:  Nima Salimi-Nezhad; Mahmood Amiri; Egidio Falotico; Cecilia Laschi
Journal:  Front Neurosci       Date:  2018-06-08       Impact factor: 4.677

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.