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

Gianluca Susi, Fabio Bartolacci, Maurizio Massarelli


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.


spiking neurons; stochastic resonance; computational model; auditory system.

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L. Alfonsi, L. Gammaitoni, “Senti che bel rumore,†Le Scienze, 2009

T. Nguyen, “Robust data-optimized stochastic analog-to-digital converters,†IEEE Transactions on Signal Processing, Vol.55, No.6, 2007.

EU project: “Transforming a little noise into a lot of energy,†coordinated by Bayerische Julius-Maximilians Universitaet Wuerzburg. Official website: http://cordis.europa.eu/result/rcn/85878_it.html

J.F. Lindner, M. Bennett, K.Wiesenfeld, Stochastic resonance in the mechanoelectrical transduction of hair cells, Physical Review, E 72, 051911, (2005)

K. Ehrenberger, D. Felix, K. Svozil. “Stochastic resonance in cochlear signal transduction.†Acta Otolaryngol. 1999 Mar;119(2):166-70. PubMed PMID: 10320069.

T.F. Weiss “A model of outer peripheral auditory system,†Kybernetik, vol.3, No.4, Springer, 1966.

K.Tanaka, I. Nemoto, M.Kawakatsu, Y. Uchikawa, “Stochastic resonance in brain activity elicited by auditory stimuli,†Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009.

N. G. Stocks, D. Allingham, R. P. Morse, “The application of suprathreshold stochastic resonance to cochlear implant coding,†Fluct. Noise Lett. 02, L169, 2002.

M.D. McDonnell & D.Abbott, “McDonnell MD, Abbott D. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology.†PLoS Comput Biol. 5(5):e1000348, 2009.

M.Chatterjee, M.E. Robert, “Noise enhances modulation sensitivity in cochlear implant listeners: stochastic resonance in a prosthetic sensory system?,†J Assoc Res Otolaryngol. 2(2), 2001.

A.Longtin, “Stochastic resonance in neuron models†Journal of Statistical Physics 70(1):309-327, 1993.

D. F. M. Goodman and R. Brette, "The Brian simulatorâ€, Frontiers in Neuroscience, vol.3, 2009.

D.W. Repperger and K.A. Farris, "Stochastic resonance–a nonlinear control theory interpretation," International Journal of Systems Science, vol. 41, No. 7, pp. 897-907,2010.

K.Wiesenfeld, F.Moss, "Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs." Nature. 5;373(6509):33-6, 1995.

F.Chapeau-Blondeau, “Stochastic Resonance and Optimal Detection of Pulse Trains by Threshold Devices.†Digital signal processing , Vol.9, No.3, 1999.

M.Artico, P.Castano, A.Cataldi et al. “Anatomia Umana Principi†Edi. Ermes s.r.l. Milano, 2005.

G. von Békésy, Experiments in Hearing, Mc Graw Hill, New York, 1960.

H. Fastl E. Zwicker, Psychoacoustics – facts and models, Springer-Verlag, Berlin Heidelberg, 2007.

H.Fletcher and W.A. Munson, “Loudness, Its Definition, Measurement and Calculation,†The Journal of the Acoustical Society of America 5, 82, 1933.

H. Fletcher, & W.A.Munson, “Relation between loudness and masking,†Journal of the Acoustical Society of America, 9, 1-10, 1937.

Zwicker E., Fastl H. Critical Bands and Excitation. In: Psychoacoustics. Springer Series in Information Sciences, vol 22. Springer, Berlin, Heidelberg, 1999.

E. Zwicker, E. Terhardt: Analytical expressions for criticalband rate and critical bandwidth as a function of frequency. J. Acoust. Soc. Am. 68 (1980) 1523–1525.

B.Fontaine, D.F.Goodman, V.Benichoux, R.Brette, “Brian hears: online auditory processing using vectorization over channelsâ€. Frontiers in Neuroinformatics, Vol.5, 2011.

E.Muller, J.Bednar, M.Diesmann, M.O.Gewaltigr, M.Hines, A.P.Davison, “Python in neuroscience.†Frontiers in Neuroinformatics, Vol.9, 2015.

Q. Tan, L.H.Carney (2003) A phenomenological model for the responses of auditory-nerve fibers. II. Nonlinear tuning with a frequency glide. J Acoust Soc Am 114:2007-20

Official website of the BRIAN simulator: http://www.briansimulator.org/docs/reference-hears.html#brian.hears.MiddleEar

M.Slaney, “An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank,†(Technical Report) Apple computer, 1993.

M. Salerno, G.Susi, A.Cristini, “Accurate latency characterization for very large asynchronous spiking neural networks,†in BIOINFORMATICS 2011 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms pp. 116-124.

Susi G., Cristini A., Salerno M., "Path multimodality in a feedforward SNN module, using LIF with latency model," Neural Network World, vol. 26, n.4, (2016).

Malhotra, R.â€A systematic review of machine learning techniques for software fault prediction,†Applied Soft Computing. Vol.27, pp.504-518 (2015).

Cardarilli G. C., Di Nunzio L., Fazzolari R., Re M. and Spanó S., "AW-SOM, an Algorithm for High-speed Learning in Hardware Self-Organizing Maps," in IEEE Transactions on Circuits and Systems II: Express Briefs.

S. Angra and S. Ahuja, "Machine learning and its applications: A review," 2017 IEEE International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, 2017, pp. 57-60.

M.Altawaier, & S. Tiun, “Comparison of machine learning approaches on Arabic twitter sentiment analysis,†International Journal on Advanced Science, Engineering and Information Technology, 6(6), 1067-1073 (2016).

M. Matta, G.C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Re, F. Silvestri, S. Spanò, “Q-RTS: a real-time swarm intelligence based on multi-agent Q-learning†Electronics Letters(2019), 55 (10):589, 2019.

M.Z. Rehman, “Noise-Induced Hearing Loss (NIHL) Prediction in Humans Using a Modified Back Propagation Neural Network, “ International Journal on Advanced Science, Vol.1, No.2, 2011.

O.I. Abiodun, A. Jantan, A.E.Omolara, K.V.Dada, N.A.Mohamed, H.Arshad,., “State-of-the-art in artificial neural network applications: A survey,†Heliyon, 2018, Vol. 4 (11), 2018.

Capizzi G., Lo Sciuto G., Monforte P. and Napoli C. “Cascade feed forward neural network-based model for air pollutants evaluation of single monitoring stations in urban areas,†INTL Journal of Electronics and Communications, 2015, Vol. 61, No. 4, pp. 327–332.

Rahman A., Muniyandi R.C. “Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network,†International Journal on Advanced Science, Engineering and Information Technology. Vol 8, No 4-2, 2018.

Cardarilli G.C. et al. "Efficient Ensemble Machine Learning Implementation on FPGA Using Partial Reconfiguration," Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018. Lecture Notes in Electrical Engineering, vol 550. Springer, Cham.

Napoli C., Bonanno F., Capizzi G., "Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach," Proceedings of the International astronomical union, No.6, S274, 2010.

F. Grassia, T. Levi, E. Doukkali, T. Kohno. “Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platformâ€. Artificial Life and Robotics, Volume 23, Issue 2, 2018,.

Lo Sciuto G., Susi G., Cammarata G. and Capizzi G. “A spiking neural network-based model for anaerobic digestion process,“ in 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), pp. 996-1003.

Simone Acciarito, Gian Carlo Cardarilli, Alessandro Cristini, Luca Di Nunzio, Rocco Fazzolari, Gaurav Mani Khanal, Marco Re, Gianluca Susi, "Hardware design of LIF with Latency neuron model with memristive STDP synapses," Integration, Vol. 59, 2017.

G. C. Cardarilli, A. Cristini, L. Di Nunzio, M. Re, M. Salerno and G. Susi, "Spiking neural networks based on LIF with latency: Simulation and synchronization effects", 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2013, pp. 1838-1842

G.Susi, L.Antón Toro, L.Canuet, M.E.López, F.Maestú, C.R.Mirasso, E.Pereda, “A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDPâ€, Frontiers in Neuroscience, Vol.12, 2018.

Yuhandri, S.Madenda, E.P.Wibowo, Karmilasari, "Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval", International Journal on Advanced Science, Engineering and Information Technology. Vol.7, No.6 (2017).

A.A. Jaber, A.A. M. Saleh, H.F. Mohammed Ali, "Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network," International Journal on Advanced Science, Engineering and Information Technology. Vol.9, No.1 (2019).

S.Brusca, G.Capizzi, G.Lo Sciuto, G.Susi, “A new design methodology to predict wind farm energy production by means of a spiking neural network–based system,†International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 32(4),e2267 (2019)

Yu S. "Neuro-inspired computing with emerging nonvolatile memorys" in Proceedings of the IEEE, Vol.106, Issue 2, 2018.

G.Susi, S.Acciarito, T.Pascual, A.Cristini, F.Maestú, “Towards Neuro-Inspired Electronic Oscillators Based on The Dynamical Relaying Mechanism,†International Journal on Advanced Science, Engineering and Information Technology. Vol 9, No 2, 2019.

A. Detti, L. Bracciale, P. Loreti, G. Rossi, N. Blefari Melazzi, “A cluster-based scalable router for information centric networks,†in Computer networks, pp.24-32, vol.142 , 2018.

A. Detti, M.Orru, R.Paolillo, G.Rossi, P. Loreti, L.Bracciale, N.Blefari Melazzi, “Application to information centric networking to nosql database,†in 2017 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), 2017.

DOI: http://dx.doi.org/10.18517/ijaseit.9.4.9438


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