RT Journal Article
SR Electronic
T1 Spiking network optimized for noise robust word recognition approaches human-level performance and predicts auditory system hierarchy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 243915
DO 10.1101/243915
A1 Fatemeh Khatami
A1 Monty A. Escabí
YR 2018
UL http://biorxiv.org/content/early/2018/01/05/243915.abstract
AB The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amongst competing sound sources, yet, the transformations enabling noise robust abilities are largely unknown. We report that a hierarchical spiking neural network (HSNN) trained to maximize word recognition accuracy in noise and multiple talkers approaches human-level performance. Intriguingly, comparisons with data from auditory nerve, midbrain, thalamus and cortex reveals that the organization and nonlinear transformations of the optimal network predict several properties of the ascending auditory pathway including a sequential loss of temporal resolution, increasing sparseness and selectivity. The optimal organizational scheme is critical for noise robustness since an identical network arranged to enable high information transfer does not predict auditory pathway organization and has substantially poorer performance. Furthermore, conventional linear and nonlinear receptive field-based models fail to achieve similar noise robust performance. The findings suggest that the auditory pathway hierarchy and its sequential nonlinear feature extraction computations may form a near optimal code capable of efficiently detecting sounds in noise impoverished conditions.Significance Statement The brain’s ability to recognize sounds in the presence of competing sounds or background noise is essential for everyday hearing tasks. How the brain accomplishes noise resiliency, however, is poorly understood. Using neural recording from the ascending auditory pathway and an auditory spiking network model trained for optimal sound recognition in noise we explore the computational strategies that enable noise robustness. Our results suggest that the hierarchical organization of the auditory pathway and the resulting nonlinear transformations may form a near optimal strategy that is essential for sound recognition in the presence of noise.