Robust sound classification through the representation of similarity using response fields derived from stimuli during early experience

Matching Pursuit Analysis of Auditory Receptive Fields' Spectro-Temporal Properties

Gabor filters have long been proposed as models for spectro-temporal receptive fields (STRFs), with their specific spectral and temporal rate of modulation qualitatively replicating characteristics of STRF filters estimated from responses to auditory stimuli in physiological data. The present study builds on the Gabor-STRF model by proposing a methodology to quantitatively decompose STRFs into a set of optimally matched Gabor filters through matching pursuit, and by quantitatively evaluating spectral and temporal characteristics of STRFs in terms of the derived optimal Gabor-parameters. To summarize a neuron's spectro-temporal characteristics, we introduce a measure for the “diagonality,” i.e., the extent to which an STRF exhibits spectro-temporal transients which cannot be factorized into a product of a spectral and a temporal modulation. With this methodology, it is shown that approximately half of 52 analyzed zebra finch STRFs can each be well approximated by a single Gabor or a linear combination of two Gabor filters. Moreover, the dominant Gabor functions tend to be oriented either in the spectral or in the temporal direction, with truly “diagonal” Gabor functions rarely being necessary for reconstruction of an STRF's main characteristics. As a toy example for the applicability of STRF and Gabor-STRF filters to auditory detection tasks, we use STRF filters as features in an automatic event detection task and compare them to idealized Gabor filters and mel-frequency cepstral coefficients (MFCCs). STRFs classify a set of six everyday sounds with an accuracy similar to reference Gabor features (94% recognition rate). Spectro-temporal STRF and Gabor features outperform reference spectral MFCCs in quiet and in low noise conditions (down to 0 dB signal to noise ratio).

Using naturalistic utterances to investigate vocal communication processing and development in human and non-human primates

Humans and several non-human primates possess cortical regions that are most sensitive to vocalizations produced by their own kind (conspecifics). However, the use of speech and other broadly defined categories of behaviorally relevant natural sounds has led to many discrepancies regarding where voice-sensitivity occurs, and more generally the identification of cortical networks, "proto-networks" or protolanguage networks, and pathways that may be sensitive or selective for certain aspects of vocalization processing. In this prospective review we examine different approaches for exploring vocal communication processing, including pathways that may be, or become, specialized for conspecific utterances. In particular, we address the use of naturally produced non-stereotypical vocalizations (mimicry of other animal calls) as another category of vocalization for use with human and non-human primate auditory systems. We focus this review on two main themes, including progress and future ideas for studying vocalization processing in great apes (chimpanzees) and in very early stages of human development, including infants and fetuses. Advancing our understanding of the fundamental principles that govern the evolution and early development of cortical pathways for processing non-verbal communication utterances is expected to lead to better diagnoses and early intervention strategies in children with communication disorders, improve rehabilitation of communication disorders resulting from brain injury, and develop new strategies for intelligent hearing aid and implant design that can better enhance speech signals in noisy environments. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives".

Emergent feature sensitivity in a model of the auditory thalamocortical system

If, as is widely believed, perception is based upon the responses of neurons that are tuned to stimulus features, then precisely what features are encoded and how do neurons in the system come to be sensitive to those features? Here we show differential responses to ripple stimuli can arise through exposure to formative stimuli in a recurrently connected model of the thalamocortical system which exhibits delays, lateral and recurrent connections, and learning in the form of spike timing dependent plasticity.

The linearity of emergent spectro-temporal receptive fields in a model of auditory cortex

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.