Parallel distribution of an inner hair cell and auditory nerve model for real-time application

This paper summarises recent efforts into implementing a model of the inner hair cell and auditory nerve on a neuromorphic hardware platform, the SpiNNaker machine. Such an implementation exploits the massive parallelism of the target architecture to obtain real-time modelling to a biologically realistic number of human auditory nerve fibres. The potential for incorporating this implementation into a full-scale digital realtime model of the human auditory pathway is then discussed.

[1]  Tim Jürgens,et al.  A computer model of the auditory periphery and its application to the study of hearing. , 2013, Advances in experimental medicine and biology.

[2]  R Plomp,et al.  Effect of multiple speechlike maskers on binaural speech recognition in normal and impaired hearing. , 1992, The Journal of the Acoustical Society of America.

[3]  R. Ciuman The Efferent System or Olivocochlear Function Bundle – Fine Regulator and Protector of Hearing Perception , 2010, International journal of biomedical science : IJBS.

[4]  Odette Scharenborg,et al.  Speech perception by humans and machines , 2017 .

[5]  Paul Albert Fuchs,et al.  Oxford Handbook of Auditory Science The Ear , 2010 .

[6]  Richard Lippmann,et al.  Speech recognition by machines and humans , 1997, Speech Commun..

[7]  Paul H. Delano,et al.  Corticofugal modulation of peripheral auditory responses , 2015, Front. Syst. Neurosci..

[8]  Ray Meddis,et al.  A revised model of the inner-hair cell and auditory-nerve complex. , 2002, The Journal of the Acoustical Society of America.

[9]  Steve B. Furber,et al.  Parallel Distribution of an Inner Hair Cell and Auditory Nerve Model for Real-Time Application , 2017, IEEE Transactions on Biomedical Circuits and Systems.

[10]  R L Smith,et al.  Conservation of adapting components in auditory-nerve responses. , 1987, The Journal of the Acoustical Society of America.

[11]  M. Malmierca,et al.  The cortical modulation of stimulus-specific adaptation in the auditory midbrain and thalamus: a potential neuronal correlate for predictive coding , 2015, Front. Syst. Neurosci..

[12]  E. Lopez-Poveda,et al.  A human nonlinear cochlear filterbank. , 2001, The Journal of the Acoustical Society of America.

[13]  Ray Meddis,et al.  Auditory-nerve first-spike latency and auditory absolute threshold: a computer model. , 2006, The Journal of the Acoustical Society of America.

[14]  G. Terreros,et al.  Selective Attention to Visual Stimuli Using Auditory Distractors Is Altered in Alpha-9 Nicotinic Receptor Subunit Knock-Out Mice , 2016, The Journal of Neuroscience.

[15]  M. C. Brown Anatomy of Olivocochlear Neurons , 2011 .

[16]  E. Lopez-Poveda,et al.  A computational algorithm for computing nonlinear auditory frequency selectivity. , 2001, The Journal of the Acoustical Society of America.

[17]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[18]  Richard M. Stern,et al.  Hearing Is Believing: Biologically Inspired Methods for Robust Automatic Speech Recognition , 2012, IEEE Signal Processing Magazine.

[19]  C. D. Geisler,et al.  From Sound to Synapse: Physiology of the Mammalian Ear , 1998 .

[20]  R. Plomp,et al.  Effects of fluctuating noise and interfering speech on the speech-reception threshold for impaired and normal hearing. , 1990, The Journal of the Acoustical Society of America.