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A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System

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@article{IJASEIT9438,
   author = {Gianluca Susi and Fabio Bartolacci and Maurizio Massarelli},
   title = {A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1474--1480},
   keywords = {spiking neurons; stochastic resonance; computational model; auditory system.},
   abstract = {Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9438},
   doi = {10.18517/ijaseit.9.4.9438}
}

EndNote

%A Susi, Gianluca
%A Bartolacci, Fabio
%A Massarelli, Maurizio
%D 2019
%T A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System
%B 2019
%9 spiking neurons; stochastic resonance; computational model; auditory system.
%! A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System
%K spiking neurons; stochastic resonance; computational model; auditory system.
%X Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9438
%R doi:10.18517/ijaseit.9.4.9438
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 4
%@ 2088-5334

IEEE

Gianluca Susi,Fabio Bartolacci and Maurizio Massarelli,"A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1474-1480, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.9438.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Susi, Gianluca
AU  - Bartolacci, Fabio
AU  - Massarelli, Maurizio
PY  - 2019
TI  - A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1474
EP  - 1480
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - spiking neurons; stochastic resonance; computational model; auditory system.
N2  - Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9438
DO  - 10.18517/ijaseit.9.4.9438

RefWorks

RT Journal Article
ID 9438
A1 Susi, Gianluca
A1 Bartolacci, Fabio
A1 Massarelli, Maurizio
T1 A Computational Approach for the Understanding of Stochastic Resonance Phenomena in the Human Auditory System
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1474
OP 1480
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 spiking neurons; stochastic resonance; computational model; auditory system.
AB Stochastic resonance (SR) is a nonlinear phenomenon by which the introduction of noise in a system causes a counterintuitive increase in levels of detection performance of a signal. SR has been extensively studied in different physical and biological systems, including the human auditory system (HAS), where a positive role for noise has been recognized both at the level of peripheral auditory system (PAS) and central nervous system (CNS). This dualism regarding the mechanistic underpinnings of the RS phenomenon in the HAS is confirmed by discrepancies among different experimental studies and reflects on a disagreement about how this phenomenon can be exploited for the improvement of prosthesis and aids devoted to hypoacusic people. HAS is one of the human body’s most complex sensory system. On the other hand, SR involves system nonlinearities. Then, the characterization of SR in the HAS is very challenging and many efforts are being made to characterize this mechanism as a whole. Current computational modelling tools make possible to investigate the phenomena separately in the CNS and in the PAS, then simplifying the analysis of the involved mechanisms. In this work we present a computational model of PAS supporting SR, that shows improved detection of sounds when input noise is added. As preparatory step, we provided a test signal to the system, at the edge of the hearing threshold. As next step, we repeated the experiment adding background noise at different intensities. We found an increase of relative spike count in the frequency bands of the test signal when input noise is added, confirming that the maximum value is obtained under a specific range of added noise, whereas further increase in noise intensity only degrades signal detection or information content.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9438
DO  - 10.18517/ijaseit.9.4.9438