Parallel Distribution of an Inner Hair Cell and Auditory Nerve Model for Real-Time Application

Abstract

This paper summarizes recent efforts in implementing a model of the ear's inner hair cell and auditory nerve on a neuromorphic hardware platform, the SpiNNaker machine. This exploits the massive parallelism of the target architecture to obtain real-time modeling of a biologically realistic number of human auditory nerve fibres. We show how this model can be integrated with additional modules that simulate previous stages of the early auditory pathway running on the same hardware architecture, thus producing a full-scale spiking auditory nerve output from a single sound stimulus. The results of the SpiNNaker implementation are shown to be comparable with a MATLAB version of the same model algorithms, while removing the inherent performance limitations associated with an increase in auditory model scale that are seen in the conventional computer simulations. Finally, we outline the potential for using this system as part of a full-scale, real-time digital model of the complete human auditory pathway on the SpiNNaker platform.

SpiNNaker: A Spiking Neural Network Architecture

“…The SpiNNak-Ear system is a fully scaled biological model of the early mammalian auditory pathway: converting a sound stimulus into a spiking representation spread across a number of parallel auditory nerve fibres [119]. This system takes advantage of the generic digital processing elements on a SpiNNaker machine, enabling a Digital Signal Processing (DSP) application to be distributed across its massively parallel architecture.…”

“…PyNN,84,[103][104][105][106]110,113,114,118,119,122,126,130,138,171,183,185,204,207,219,222,250, 256 Python, 84, 85, 87, 89, 91, 97, 99, 103, 106, 119, 122, 171, 227 QPE, xxi, 266-268, 270, 279 Qualcomm, 263 quantum computer, 148 RAM, xxi, 8, 10, 14, 19, 25-27, 31, 34, 44, 46, 48, 49, 51, 147 Ramón y Cajal, 3 random initialisation, 259 random mutation, 256 random number generator, 272 rank-order encoding, 250, 254 Raspberry-Pi, 146, 147 RBM, xxi, 161, 162, 170, 189 read-modify-write operation, 39 unsupervised learning, 161, 170, 227, 229, 247, 249, 250 user, 44, 47, 48, 55, 74, 77, 78, 84, 89, 92, 103-105, 107, 108, 110, 112, 116-119, 139, 142, 143, 146, 178, 219, 259, 273, 274, 279 user event, 107, 108, 112, 117…”

Intrinsic Noise Improves Speech Recognition in a Computational Model of the Auditory Pathway

“…The model is not intended to be a fine-grained model of the complete auditory pathway with exhaustive biological detail, but is rather used to demonstrate, analyze and interpret the basic principles of information processing in the auditory system. Thus, we abstracted from most biological details and constructed a coarse-grained model of the cochlea, which does not cover the full potential of cochlear information processing compared to more fine-grained implementations as introduced e.g., by Carney (1993 , 2021) , Sumner et al (2002) , James et al (2018) , and Verhulst et al (2018) . Thus, Carney and co-workers simulate the cochlea as narrow-band filters but applied a feed-back loop changing the parameters of this filters with intensity ( Carney, 1993 ).…”